system

Abstract

We provide the system. [Solution] A means of digitizing users' clothing information, A means of suggesting the optimal clothing combination based on digitized clothing information and weather information, A means for displaying the proposed clothing combination to the user, A means for obtaining user feedback and optimizing the proposed means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, consumers own a variety of fashion items, and there is a problem that daily style selection is complicated and time-consuming. Also, it is difficult to access a lot of information to select an appropriate coordination and maintain a consistent styling. For this reason, there is a demand for a system that efficiently manages all the clothing information possessed by a user and improves the user's fashion experience by making a proposal for an individualized style based on the data.

Means for Solving the Problems

[0005] This invention comprises means for digitizing a user's clothing information and means for suggesting the optimal clothing combination using the digitized information and weather information. Furthermore, it displays the suggested coordination on the user's terminal and continuously optimizes the suggestions by obtaining feedback from the user, thereby realizing styling tailored to the user's preferences. As a result, users can make daily clothing choices more quickly and accurately.

[0006] "Means of digitizing user clothing information" refers to a function that electronically records attribute information and images related to clothing owned by the user and stores them in a database.

[0007] "Weather information" refers to meteorological information obtained through the internet or other means, including weather forecasts, temperature, and humidity for a specific region.

[0008] "A means of suggesting the optimal clothing combination" refers to a function that calculates and presents the most suitable clothing set for the user based on the user's digitized clothing information and acquired weather information.

[0009] "Means of displaying information to the user" refers to a function that displays information sent from the server in a format that can be viewed on the user's device, and enables user input.

[0010] "Means for obtaining feedback and optimizing the suggested methods" refers to a function in which the system receives user evaluations of the suggested coordination, analyzes them, and reflects them in future suggestions. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system according to the present invention is a system for digitizing a user's clothing and suggesting personalized outfits based on that digitized information. The user installs a dedicated application on their terminal, takes photos of their clothing, and inputs tag information. The tag information includes the type of clothing, color, brand name, material, size, etc.

[0033] The server receives this clothing information from the user's device and stores it in a database. This digitized clothing information forms the foundational data for centrally managing all items in the user's closet.

[0034] The server periodically retrieves weather information via the internet and analyzes it in combination with user-specific clothing information stored in a database. This analysis calculates the most suitable daily outfit for each user. For example, on rainy days, it suggests items including waterproof jackets and shoes, while on sunny days, it recommends breathable, casual combinations.

[0035] The generated outfit suggestions are sent from the server to the user's device and displayed visually through the app. Users can select from these suggestions or, if necessary, request other suggestions. Furthermore, by submitting feedback on the suggestions, the system learns the user's preferences and optimizes itself to provide more appropriate suggestions.

[0036] For example, if a user specifies that they need "office casual attire for tomorrow," the server will use that request and the weather forecast to suggest an outfit that is casual yet incorporates formal elements. For instance, it might suggest a style combining a navy blazer, a white Oxford shirt, and gray slacks. In this way, the system can streamline the user's clothing selection process and, consequently, improve the quality of their fashion life.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users take photos of their clothes through a smartphone app and enter tag information (e.g., type of clothing, color, brand, material). This information is stored in the app and transmitted to a server via the internet.

[0040] Step 2:

[0041] The server stores clothing data received from the user's terminal in a database. During storage, tag information associated with the image data is also stored, forming a clothing list for each user.

[0042] Step 3:

[0043] The server obtains the latest weather information through an external data provision service. This information can be used to consider weather factors in coordination proposals.

[0044] Step 4:

[0045] The server uses an AI algorithm to calculate the optimal clothing combination for the user based on stored clothing data and weather information. It also refers to past suggestion history and user feedback to improve the quality of the styles.

[0046] Step 5:

[0047] The server sends the generated outfit suggestions to the user's device. The suggestions include images of each selected garment and notes on the combinations.

[0048] Step 6:

[0049] The device visually displays outfit suggestions received from the server within the application. The user can review the displayed suggestions and select their preferred outfit.

[0050] Step 7:

[0051] Users submit feedback on outfit suggestions through the app. This feedback includes an evaluation of whether the suggestion is suitable.

[0052] Step 8:

[0053] Feedback data from the device is sent back to the server and analyzed by an AI algorithm. Based on the feedback received, the suggestion method is optimized so that the next outfit suggestion is more personalized and better suited to the user's preferences.

[0054] (Example 1)

[0055] 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."

[0056] Modern consumers own a wide variety of clothing, making it difficult to choose the right outfits for each day. Furthermore, there is a demand for providing optimal clothing combinations tailored to individual preferences and the weather conditions. However, with so many options available, combining and deciding on the right outfits requires a tremendous amount of time and effort.

[0057] 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.

[0058] In this invention, the server includes means for digitizing the user's clothing information and storing it on an information technology infrastructure; means for performing analysis based on the digitized clothing information and acquired environmental information, and calculating the optimal clothing combination using a generated artificial intelligence model; and means for presenting the calculated clothing combination to the user via display technology. This makes it possible for the user to efficiently receive suggestions optimized according to their individual preferences and the weather, making it easier to choose their daily outfits.

[0059] A "user" is an individual or group that uses the system to manage their own clothing information and receive suggestions for the most suitable clothing combinations.

[0060] "Clothing information" refers to digital data about clothing owned by the user, including attributes such as type, color, brand name, material, and size.

[0061] "Digitalization" is the process of converting analog information into digital data and making it usable within a system.

[0062] "Information technology infrastructure" is a general term for hardware and software used for storing, managing, and analyzing data.

[0063] "Environmental information" refers to data about weather conditions and other external factors that the system acquires from external sources.

[0064] A "generative artificial intelligence model" is an algorithm or program that has the ability to generate knowledge and make new suggestions based on input data.

[0065] "Display technology" refers to technologies used to visually present digital data to users, and includes displays and smartphone screens.

[0066] This invention is a system for managing a user's clothing as digital data and suggesting the optimal clothing combinations based on this data. The user installs a dedicated application on their device. This application provides an interface for the user to take photos of their clothing and input detailed information such as type, color, brand name, material, and size. The device then transmits this input information to a server.

[0067] The server stores the received information in a database and digitizes the user's clothing information. This digital information functions as the user's virtual closet and serves as the basic data input into the generative AI model. The server periodically acquires weather information via the internet and analyzes this environmental information in combination with the clothing data. The generative AI model is used for analysis to generate optimal outfits in real time.

[0068] The generated outfit suggestions are sent from the server to the user's terminal. The user can view the suggestions visually on the terminal's application. These suggestions take into account the user's preferences and weather conditions, allowing the user to streamline their daily clothing choices. For example, if the user enters a prompt such as, "I need office casual attire tomorrow," the server will suggest appropriate combinations based on that request.

[0069] Furthermore, by providing user feedback on the suggestions, the server can continuously optimize the generated AI model, enabling it to provide more refined suggestions. This gradually improves the user experience and results in more personalized suggestions tailored to each user's preferences and style.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user installs a dedicated application on their device, takes a picture of their clothing, and inputs clothing information such as type, color, brand name, material, and size. The device collects this input data and generates a data package. Next, it prepares to send that package to the server. The output at this stage is the package of clothing information entered by the user.

[0073] Step 2:

[0074] The server receives a package of clothing information sent from the terminal. The received data is stored in a database. Next, the server updates the user's digital closet based on this data, centrally managing the clothing information of each individual user. The output of this step is the updated digital closet data.

[0075] Step 3:

[0076] The server periodically accesses an external weather information service to obtain the latest weather data. This weather data is added to the database and used in combination with clothing information. The input for this step is the weather data obtained from an external source, and the output is the complete information integrated into the database.

[0077] Step 4:

[0078] The server uses a generative AI model to analyze clothing information from the digital closet and weather information. This analysis uses prompts entered by the user, and the model generates the optimal clothing combination. For example, it would respond to a prompt such as, "I need office casual attire tomorrow." The output of this step is the generated outfit suggestion.

[0079] Step 5:

[0080] The server sends the generated coordination proposal to the user's terminal. The terminal displays the received proposal data to the user visually through the application interface. The user can review this proposal and accept it as needed, or request other proposals. The output of this step is the visually displayed coordination proposal.

[0081] Step 6:

[0082] The user provides feedback on the suggestions. This feedback is sent to the server via the terminal. The server uses this feedback information to adjust the generative AI model and improve the system so that it can make more effective suggestions based on the user's preferences. The final output is the optimized generative AI model.

[0083] (Application Example 1)

[0084] 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."

[0085] In modern times, users often spend time and effort selecting daily outfits from their vast wardrobe. Furthermore, consistently choosing attire appropriate for different weather conditions and events can be challenging. Therefore, there is a need to support users in their clothing choices and reduce daily stress through efficient and personalized outfit suggestions.

[0086] 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.

[0087] In this invention, the server includes means for quantifying the user's clothing data, means for recommending appropriate clothing combinations based on the quantified clothing data and weather data, and means for visually presenting the recommended clothing combinations to the user. This enables the user to efficiently select the most suitable outfit.

[0088] A "user" is an individual who uses the system to receive clothing coordination suggestions.

[0089] "Clothing data" refers to a digital representation of information about the clothing owned by a user.

[0090] "Digitization" refers to converting clothing information into a format that can be processed by a computer.

[0091] "Weather data" refers to information about current and future weather conditions.

[0092] "Outfit combinations" refer to the selection of clothing that is considered optimal for a particular occasion or weather condition.

[0093] "Recommendation" is the act of selecting and presenting the optimal option.

[0094] "Visual presentation" means displaying information to the user on a screen using photos, illustrations, and other visual means.

[0095] A "virtual model" is a digital doll displayed in a virtual space.

[0096] "Trying on clothes" means temporarily putting on clothing to check whether it fits or not.

[0097] "Collecting ratings" means gathering opinions and feedback from users.

[0098] "Adaptive adjustment" means improving the accuracy of the system's suggestions based on user feedback.

[0099] The system for realizing this invention consists of a user's communication terminal and a server. The user uses the communication terminal to take a picture of their clothing and inputs clothing information. Clothing information includes type, color, material, etc. The communication terminal transmits this information to the server.

[0100] The server uses the Google® Cloud Vision API to extract tag information from images and digitize clothing data. This digitized data is stored in a database, forming the user's virtual closet. Next, the server periodically retrieves weather data from the internet. This data is combined with the user's clothing data, and a generative AI model using TENSORFLOW® recommends the optimal outfit combination. The server sends this recommendation to the communication terminal and presents it visually to the user.

[0101] By wearing a head-mounted display, users can virtually try on suggested outfits in 3D virtual models. Furthermore, user ratings and feedback are collected on a server, and the algorithm is adjusted based on this data to improve the accuracy of recommendations.

[0102] As a concrete example, if a user requests a "stylish casual outfit for a cloudy day tomorrow" from their device, the system will suggest an appropriate outfit through its generative AI model.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1: The user uses a communication device to take a picture of their clothing and enter the clothing information. This information includes the type, color, and material of the clothing. The entered data is temporarily stored in the communication device as tag information.

[0105] Step 2: The device sends the saved clothing photos and tag information to the server. The server uses the Google Cloud Vision API to analyze the photos and extract tag information from the images. Based on the tag information obtained through the analysis, the clothing data is quantified and stored in a database.

[0106] Step 3: The server retrieves weather data from the internet. The retrieved weather data is combined with the user's clothing data in the database, and a generative AI model is used to perform data calculations and recommend the optimal outfit combination. The output data is the recommended outfit information.

[0107] Step 4: The server sends the recommended results to the user's communication terminal. On the user's terminal, the suggested outfit is displayed on a virtual model using 3D modeling technology via a head-mounted display. The user can visually confirm the outfit.

[0108] Step 5: The user sends their evaluation and feedback about their appearance from their device to the server. This feedback is collected by the server and used to refine the algorithm. This improves the accuracy of future recommendations.

[0109] 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.

[0110] The system according to the present invention not only digitizes the user's clothing information and proposes personalized outfits, but also incorporates an emotion engine that recognizes the user's emotions. This makes it possible to provide more appropriate fashion suggestions according to the user's emotional state.

[0111] Users take photos of their clothes using a smartphone app and input tag information. This process digitizes and centrally manages their clothing collection in a database. The app's emotion engine operates based on the user's facial expressions and voice. The emotion engine analyzes the user's facial data and voice tone to identify their current emotional state. This emotional data influences the fashion coordination suggestions.

[0112] The server integrates and analyzes digitized clothing information, current weather information, and acquired user sentiment data. An AI algorithm calculates the outfit that best suits the user's emotions and external factors. For example, if the user is feeling stressed, it suggests loose and comfortable clothing, and if they are in a cheerful mood, it recommends bright-colored clothing.

[0113] The generated outfit suggestions are sent from the server to the user's device and displayed through the app. Users can review the suggested outfits and submit feedback to enable further optimization. The emotion engine also monitors the user's emotional changes in real time and updates the suggestions as needed.

[0114] For example, if the emotion engine identifies a user as "feeling down" on a workday, the system will suggest outfits that focus on relaxing materials and incorporate color theory to brighten their mood. In this way, the system provides a fashion experience that takes the user's emotional state into consideration, further enhancing their personalized lifestyle.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] The user launches the smartphone app and takes a picture of their clothing. At the same time, they input tag information about the clothing (type, color, brand, etc.) and save the data to the app.

[0118] Step 2:

[0119] The device sends the saved clothing data to the server via the cloud. The transmitted data is stored in the server's database, and a clothing catalog for each user is created.

[0120] Step 3:

[0121] When a user begins their daily activities, the app inputs their facial expressions and voice into the emotion engine in real time. The emotion engine analyzes this data to identify the user's emotional state and prepares it for further analysis.

[0122] Step 4:

[0123] The server integrates clothing information stored in the database with acquired weather information, and further combines it with data from the emotion engine. Using an AI algorithm, it generates the optimal clothing combination for the day.

[0124] Step 5:

[0125] The server sends the generated optimal outfit suggestion to the terminal. The suggestion includes detailed information about each selected garment and the reason for its selection (e.g., relaxation effect based on emotion).

[0126] Step 6:

[0127] The terminal visually displays the coordination suggestions received from the server to the user, allowing them to review the suggestions. The user can then accept the suggestions or request further suggestions.

[0128] Step 7:

[0129] Users submit feedback on suggested outfits via the app. The emotion engine continuously updates the data, and the server uses this data to continuously improve the suggestions.

[0130] Step 8:

[0131] The server optimizes its algorithms to improve the accuracy of future suggestions based on feedback and real-time sentiment data. As new data influences potential suggestions, the system dynamically learns and provides a more personalized experience that meets user needs.

[0132] (Example 2)

[0133] 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".

[0134] Conventional clothing suggestion systems often fail to provide optimal suggestions because they rely solely on weather and basic clothing information, without considering the user's emotional state. Furthermore, they struggle to incorporate user feedback, resulting in a low degree of personalized suggestions. Additionally, they lack the ability to dynamically update outfits in real-time in response to changing circumstances.

[0135] 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.

[0136] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and the user's emotional data, means for obtaining feedback from the user and optimizing the suggestion means, and means for analyzing the user's emotions in real time and dynamically updating the coordination suggestions. This enables more personalized coordination suggestions that take into account the user's emotional state and external factors.

[0137] A "user" refers to an individual who uses a clothing suggestion system to receive outfit suggestions.

[0138] "Clothing information" refers to information about clothing owned by the user, and is digitized data including tag information such as color, brand, material, and intended use.

[0139] "Digitalization" is the process of converting information in a physical or analog state into an electronic format that can be processed by a computer.

[0140] "Emotional data" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and voice.

[0141] "Weather information" refers to weather data obtained from external sources and is a factor considered when proposing outfits.

[0142] "Suggestions" refer to the clothing combinations and outfit ideas that the system analyzes and provides to the user.

[0143] "Feedback" refers to user responses such as evaluations and comments on suggested outfits.

[0144] "Optimization" is the process of adjusting a system to improve its operation and provide suggestions that better meet user needs.

[0145] "Real-time" refers to a state of operation where data generation and processing occur instantaneously, and results are reflected immediately.

[0146] "Dynamic updating" is a process that keeps data and suggestions up-to-date whenever new information becomes available.

[0147] This invention is a system that digitizes information about a user's clothing and provides optimal outfit suggestions that take into account the user's emotional state and weather information. The system utilizes a terminal such as a smartphone, a network-connected server, and a dedicated application. The user uses the application installed on the terminal to take photos of the clothes they will wear and inputs tag information such as color, brand, material, and purpose. This information is temporarily stored on the terminal and then transmitted to the server.

[0148] The server stores the received clothing information in a database and uses a generative AI model to select suggested outfits. During this process, it obtains weather information using an external weather API and performs sentiment analysis based on the user's facial expressions and voice data collected from the device. This data is then analyzed by an AI algorithm to suggest the most suitable outfit for the user.

[0149] For example, if the emotion engine detects that a user is feeling "down," the server will prioritize suggesting clothing made of relaxing materials and outfits incorporating bright colors. This suggestion is sent from the server to the user's device and visually presented to the user through the application.

[0150] Furthermore, the system includes a feature to receive user feedback, and the suggestions are optimized based on that feedback. A novel feature of this system is its ability to dynamically update suggestions in response to real-time changes in emotional state.

[0151] An example of a prompt would be, "If a user is feeling down as they head to a casual meeting on a summer day, what outfit should be suggested?" Based on this prompt, the system can quickly generate a personalized outfit tailored to the user's state.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] Users take photos of their clothing using their smartphones and input tag information such as color, brand, material, and intended use. This input is temporarily stored on the device as the user's clothing collection. The input at this stage consists of image data of the clothing and its metadata. This data is then sent to the server as user-specific digitized clothing data.

[0155] Step 2:

[0156] The server receives the submitted clothing data and stores it in a database. During this process, image data is preprocessed and tag information is cleaned. Specifically, images are resized and tags are standardized, resulting in a high-quality and consistent dataset. The output data is digital clothing information organized for each user.

[0157] Step 3:

[0158] The user provides facial expressions and voices during everyday activities via a device. The device collects this data and sends it to a server as input to operate the emotion engine. The emotion engine analyzes this data using image recognition and voice analysis technologies to identify the user's current emotional state. The output is quantified emotion data.

[0159] Step 4:

[0160] The server uses an external weather information API to obtain weather information related to the user's location. This operation retrieves data such as current temperature, humidity, and probability of precipitation. The retrieved weather data is used as an element when suggesting outfits. The output is weather information formatted in a parseable format.

[0161] Step 5:

[0162] The server integrates digitized clothing information, emotional data, and weather information from a stored database, and applies AI algorithms to generate the optimal clothing coordination for the user's situation. This process involves analyzing individual elements and evaluating their correlations and suitability. The output is a list of suggested outfits for the user.

[0163] Step 6:

[0164] The proposed outfits are sent from the server to the user's terminal. The terminal's application visually displays the proposed outfits to the user through its user interface. The UI includes interactive elements, allowing the user to review the proposals and provide feedback if necessary. The output is a visually verifiable outfit proposal.

[0165] Step 7:

[0166] User feedback is sent to the server and re-inputted as feedback data into the AI ​​algorithm to improve the quality of suggestions. The emotion engine also continuously monitors daily emotional changes, allowing the server to dynamically update outfits based on the latest emotional state. The final output is a personalized outfit suggestion that has evolved based on the user's emotional state and feedback.

[0167] (Application Example 2)

[0168] 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 device 14 will be referred to as the "terminal."

[0169] Traditional fashion suggestion systems only offered two-dimensional suggestions based on weather information and digital clothing data, without considering the user's emotional state. As a result, the suggested outfits often did not match the user's current emotional state, leading to a poor user experience. There is a need for technology that can solve this problem and provide more personalized outfit suggestions.

[0170] 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.

[0171] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and emotional state using an emotion engine that analyzes the user's emotional state, and means for displaying the suggested clothing combination to the user through a visual receiving device. This makes it possible to suggest personalized fashion coordinates that are appropriate to the user's emotions.

[0172] "User clothing information" refers to information about the clothing owned by the user, and is data that is digitized and managed.

[0173] "Digitalization" refers to the process of converting physical information into electronic data and managing it in a database.

[0174] An "emotion engine" is an algorithm that analyzes a user's emotional state from their facial expressions and voice to identify that state.

[0175] "Weather information" refers to data on current weather conditions and is used as an element that influences fashion suggestions.

[0176] A "visual receiving device" is a device that allows users to visually confirm digital data, and includes smartphones and head-mounted displays.

[0177] "Feedback" refers to the evaluations and comments that users provide regarding suggested outfits, and this information is useful for optimizing the system.

[0178] A "virtual space" is a digital space created by a computer, an environment that users can experience interactively.

[0179] "Outfit suggestions" refer to clothing combinations selected based on the user's individual conditions, and are generated taking into account their emotional state and environmental factors.

[0180] This invention is a system for providing users with fashion coordination based on their emotional state. In this system, the user's terminal manages the digital information of the clothing and analyzes the user's emotional state using an emotion engine.

[0181] Users take photos of their clothing using visual receiving devices such as smartphones or smart glasses, and digitize the information. Identification information is added to the clothing and saved as digital data. The user's device has a camera and microphone, which are used by an emotion engine to analyze the user's facial expressions and voice tone, employing TensorFlow and OpenCV to determine their current emotional state.

[0182] The server uses a generating AI model to calculate and provide users with appropriate clothing combinations based on digitized clothing information, weather information, and emotional state data. These outfit suggestions are visualized in a virtual space using Unity or Unreal Engine, and users can view them using visual receiving devices. User feedback on the suggested outfits is also collected to continuously optimize the system's suggestion algorithm.

[0183] As a concrete example, if a user wears smart glasses and asks aloud, "What should I wear today?", the system of this invention analyzes the input voice data and facial expression data to determine that the user is in a relaxed mood. Based on this result, a generative AI model is used to suggest an outfit with bright colors and soft materials. Through this experience, the user can receive more personalized fashion advice.

[0184] Example of a prompt:

[0185] "Please suggest an outfit that perfectly suits my mood today. I want to feel relaxed."

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The device takes a picture of the user's clothing and digitizes the image. The device uses a camera to acquire a picture of the clothing and assigns identification information to it. The input is an image of the actual clothing, and the output is digitized clothing information stored in a digital database.

[0189] Step 2:

[0190] The server receives digitized clothing information sent from the terminal. The server stores the information in a database and manages clothing navigation and history as needed. The input is digitized clothing information, and the output is an update to a detailed clothing catalog.

[0191] Step 3:

[0192] The user accesses the system and communicates their current mood through voice and facial expressions. The terminal collects the user's voice and facial expression data and sends it to the emotion engine. The input is the user's current voice tone and facial expressions, and the output is the user's emotional state obtained by the emotion engine.

[0193] Step 4:

[0194] The emotion engine processes the user's voice and facial expression data to identify their current emotional state. Text analysis and image recognition algorithms are used to determine the emotion. The input is voice and facial expression data, and the output is the user's identified emotional state.

[0195] Step 5:

[0196] The server matches clothing information, weather information, and emotional state to generate the optimal clothing combination. A generative AI model is used to calculate outfits adapted to emotions and external factors. The input is digitized clothing information, weather information, and emotional information, and the output is the proposed clothing combination.

[0197] Step 6:

[0198] The terminal displays outfit suggestions received from the server to the user. It provides a visual virtual try-on experience, giving the user the opportunity to review and select from the suggestions. The input is the data of the suggested outfits, and the output is the outfit information visualized for the user.

[0199] Step 7:

[0200] The user provides feedback on the suggested outfit. The device collects this feedback and sends it to the server. The input is the user's feedback information, and the output is algorithmic data that is updated for optimization.

[0201] Step 8:

[0202] The server analyzes the received feedback and optimizes the proposed algorithm. As a result, the accuracy of subsequent proposals improves. The input is user feedback, and the output is the updated proposed algorithm.

[0203] 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] The system according to the present invention is a system for digitizing a user's clothing and suggesting personalized outfits based on that digitized information. The user installs a dedicated application on their terminal, takes photos of their clothing, and inputs tag information. The tag information includes the type of clothing, color, brand name, material, size, etc.

[0220] The server receives this clothing information from the user's device and stores it in a database. This digitized clothing information forms the foundational data for centrally managing all items in the user's closet.

[0221] The server periodically retrieves weather information via the internet and analyzes it in combination with user-specific clothing information stored in a database. This analysis calculates the most suitable daily outfit for each user. For example, on rainy days, it suggests items including waterproof jackets and shoes, while on sunny days, it recommends breathable, casual combinations.

[0222] The generated outfit suggestions are sent from the server to the user's device and displayed visually through the app. Users can select from these suggestions or, if necessary, request other suggestions. Furthermore, by submitting feedback on the suggestions, the system learns the user's preferences and optimizes itself to provide more appropriate suggestions.

[0223] For example, if a user specifies that they need "office casual attire for tomorrow," the server will use that request and the weather forecast to suggest an outfit that is casual yet incorporates formal elements. For instance, it might suggest a style combining a navy blazer, a white Oxford shirt, and gray slacks. In this way, the system can streamline the user's clothing selection process and, consequently, improve the quality of their fashion life.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] Users take photos of their clothes through a smartphone app and enter tag information (e.g., type of clothing, color, brand, material). This information is stored in the app and transmitted to a server via the internet.

[0227] Step 2:

[0228] The server stores clothing data received from the user's terminal in a database. During storage, tag information associated with the image data is also stored, forming a clothing list for each user.

[0229] Step 3:

[0230] The server obtains the latest weather information through an external data provision service. This information can be used to consider weather factors in coordination proposals.

[0231] Step 4:

[0232] The server uses an AI algorithm to calculate the optimal clothing combination for the user based on stored clothing data and weather information. It also refers to past suggestion history and user feedback to improve the quality of the styles.

[0233] Step 5:

[0234] The server sends the generated outfit suggestions to the user's device. The suggestions include images of each selected garment and notes on the combinations.

[0235] Step 6:

[0236] The device visually displays outfit suggestions received from the server within the application. The user can review the displayed suggestions and select their preferred outfit.

[0237] Step 7:

[0238] Users submit feedback on outfit suggestions through the app. This feedback includes an evaluation of whether the suggestion is suitable.

[0239] Step 8:

[0240] Feedback data from the device is sent back to the server and analyzed by an AI algorithm. Based on the feedback received, the suggestion method is optimized so that the next outfit suggestion is more personalized and better suited to the user's preferences.

[0241] (Example 1)

[0242] 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."

[0243] Modern consumers own a wide variety of clothing, making it difficult to choose the right outfits for each day. Furthermore, there is a demand for providing optimal clothing combinations tailored to individual preferences and the weather conditions. However, with so many options available, combining and deciding on the right outfits requires a tremendous amount of time and effort.

[0244] 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.

[0245] In this invention, the server includes means for digitizing the user's clothing information and storing it on an information technology infrastructure; means for performing analysis based on the digitized clothing information and acquired environmental information, and calculating the optimal clothing combination using a generated artificial intelligence model; and means for presenting the calculated clothing combination to the user via display technology. This makes it possible for the user to efficiently receive suggestions optimized according to their individual preferences and the weather, making it easier to choose their daily outfits.

[0246] A "user" is an individual or group that uses the system to manage their own clothing information and receive suggestions for the most suitable clothing combinations.

[0247] "Clothing information" refers to digital data about clothing owned by the user, including attributes such as type, color, brand name, material, and size.

[0248] "Digitalization" is the process of converting analog information into digital data and making it usable within a system.

[0249] "Information technology infrastructure" is a general term for hardware and software used for storing, managing, and analyzing data.

[0250] "Environmental information" refers to data about weather conditions and other external factors that the system acquires from external sources.

[0251] A "generative artificial intelligence model" is an algorithm or program that has the ability to generate knowledge and make new suggestions based on input data.

[0252] "Display technology" refers to technologies used to visually present digital data to users, and includes displays and smartphone screens.

[0253] This invention is a system for managing a user's clothing as digital data and suggesting the optimal clothing combinations based on this data. The user installs a dedicated application on their device. This application provides an interface for the user to take photos of their clothing and input detailed information such as type, color, brand name, material, and size. The device then transmits this input information to a server.

[0254] The server stores the received information in a database and digitizes the user's clothing information. This digital information functions as the user's virtual closet and serves as the basic data input into the generative AI model. The server periodically acquires weather information via the internet and analyzes this environmental information in combination with the clothing data. The generative AI model is used for analysis to generate optimal outfits in real time.

[0255] The generated outfit suggestions are sent from the server to the user's terminal. The user can view the suggestions visually on the terminal's application. These suggestions take into account the user's preferences and weather conditions, allowing the user to streamline their daily clothing choices. For example, if the user enters a prompt such as, "I need office casual attire tomorrow," the server will suggest appropriate combinations based on that request.

[0256] Furthermore, by providing user feedback on the suggestions, the server can continuously optimize the generated AI model, enabling it to provide more refined suggestions. This gradually improves the user experience and results in more personalized suggestions tailored to each user's preferences and style.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The user installs a dedicated application on their device, takes a picture of their clothing, and inputs clothing information such as type, color, brand name, material, and size. The device collects this input data and generates a data package. Next, it prepares to send that package to the server. The output at this stage is the package of clothing information entered by the user.

[0260] Step 2:

[0261] The server receives a package of clothing information sent from the terminal. The received data is stored in a database. Next, the server updates the user's digital closet based on this data, centrally managing the clothing information of each individual user. The output of this step is the updated digital closet data.

[0262] Step 3:

[0263] The server periodically accesses an external weather information service to obtain the latest weather data. This weather data is added to the database and used in combination with clothing information. The input for this step is the weather data obtained from an external source, and the output is the complete information integrated into the database.

[0264] Step 4:

[0265] The server uses a generative AI model to analyze clothing information from the digital closet and weather information. This analysis uses prompts entered by the user, and the model generates the optimal clothing combination. For example, it would respond to a prompt such as, "I need office casual attire tomorrow." The output of this step is the generated outfit suggestion.

[0266] Step 5:

[0267] The server sends the generated coordination proposal to the user's terminal. The terminal displays the received proposal data to the user visually through the application interface. The user can review this proposal and accept it as needed, or request other proposals. The output of this step is the visually displayed coordination proposal.

[0268] Step 6:

[0269] The user provides feedback on the suggestions. This feedback is sent to the server via the terminal. The server uses this feedback information to adjust the generative AI model and improve the system so that it can make more effective suggestions based on the user's preferences. The final output is the optimized generative AI model.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In modern times, users often spend time and effort selecting daily outfits from their vast wardrobe. Furthermore, consistently choosing attire appropriate for different weather conditions and events can be challenging. Therefore, there is a need to support users in their clothing choices and reduce daily stress through efficient and personalized outfit suggestions.

[0273] 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.

[0274] In this invention, the server includes means for quantifying the user's clothing data, means for recommending appropriate clothing combinations based on the quantified clothing data and weather data, and means for visually presenting the recommended clothing combinations to the user. This enables the user to efficiently select the most suitable outfit.

[0275] A "user" is an individual who uses the system to receive clothing coordination suggestions.

[0276] "Clothing data" refers to a digital representation of information about the clothing owned by a user.

[0277] "Digitization" refers to converting clothing information into a format that can be processed by a computer.

[0278] "Weather data" refers to information about current and future weather conditions.

[0279] "Outfit combinations" refer to the selection of clothing that is considered optimal for a particular occasion or weather condition.

[0280] "Recommendation" is the act of selecting and presenting the optimal option.

[0281] "Visually presenting" means displaying information to the user on the screen using photos, illustrations, etc.

[0282] A "virtual model" is a digital human figure displayed in a virtual space.

[0283] "Trying on" means hypothetically putting on clothes to check if they fit.

[0284] "Collecting evaluations" means gathering opinions and feedback from users.

[0285] "Adaptive adjustment" means improving the system's proposal accuracy based on user feedback.

[0286] The system for realizing this invention consists of the user's communication terminal and a server. The user uses the communication terminal to take a photo of their clothes and input clothing information. The clothing information includes type, color, material, etc. The communication terminal transmits this information to the server.

[0287] The server utilizes the Google Cloud Vision API to extract tag information from the image and digitize the clothing data. The digitized data is stored in a database to form the user's virtual closet. Next, the server periodically obtains weather data from the Internet. This data is combined with the user's clothing data, and an AI generation model using TensorFlow recommends the optimal outfit combination. The server transmits this recommendation to the communication terminal and visually presents it to the user.

[0288] By wearing a head-mounted display, the user can experientially try on the outfits proposed in the 3D virtual model. Furthermore, evaluations and feedback from the user are collected by the server, and the algorithm is adjusted based on this data to improve the accuracy of the recommendations.

[0289] As a concrete example, if a user requests a "stylish casual outfit for a cloudy day tomorrow" from their device, the system will suggest an appropriate outfit through its generative AI model.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1: The user uses a communication device to take a picture of their clothing and enter the clothing information. This information includes the type, color, and material of the clothing. The entered data is temporarily stored in the communication device as tag information.

[0292] Step 2: The device sends the saved clothing photos and tag information to the server. The server uses the Google Cloud Vision API to analyze the photos and extract tag information from the images. Based on the tag information obtained through the analysis, the clothing data is quantified and stored in a database.

[0293] Step 3: The server retrieves weather data from the internet. The retrieved weather data is combined with the user's clothing data in the database, and a generative AI model is used to perform data calculations and recommend the optimal outfit combination. The output data is the recommended outfit information.

[0294] Step 4: The server sends the recommended results to the user's communication terminal. On the user's terminal, the suggested outfit is displayed on a virtual model using 3D modeling technology via a head-mounted display. The user can visually confirm the outfit.

[0295] Step 5: The user sends their evaluation and feedback about their appearance from their device to the server. This feedback is collected by the server and used to refine the algorithm. This improves the accuracy of future recommendations.

[0296] 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.

[0297] The system according to the present invention not only digitizes the user's clothing information and proposes personalized outfits, but also incorporates an emotion engine that recognizes the user's emotions. This makes it possible to provide more appropriate fashion suggestions according to the user's emotional state.

[0298] Users take photos of their clothes using a smartphone app and input tag information. This process digitizes and centrally manages their clothing collection in a database. The app's emotion engine operates based on the user's facial expressions and voice. The emotion engine analyzes the user's facial data and voice tone to identify their current emotional state. This emotional data influences the fashion coordination suggestions.

[0299] The server integrates and analyzes digitized clothing information, current weather information, and acquired user sentiment data. An AI algorithm calculates the outfit that best suits the user's emotions and external factors. For example, if the user is feeling stressed, it suggests loose and comfortable clothing, and if they are in a cheerful mood, it recommends bright-colored clothing.

[0300] The generated outfit suggestions are sent from the server to the user's device and displayed through the app. Users can review the suggested outfits and submit feedback to enable further optimization. The emotion engine also monitors the user's emotional changes in real time and updates the suggestions as needed.

[0301] As a specific example, when a user is identified by the emotion engine as "lacking energy" on a workday, the system proposes a coordination that takes into account color science to brighten the mood, centered around clothing made of relaxing materials. In this way, this system can provide a fashion experience that takes into account the user's emotional state and further improve the personalized lifestyle.

[0302] The processing flow will be described below.

[0303] Step 1:

[0304] The user launches the smartphone app and takes a photo of the clothes they have. At this time, tag information (type, color, brand, etc.) related to the clothes is input and the data is saved in the app.

[0305] Step 2: [[ID=I8]]

[0306] The terminal sends the saved clothes data to the server via the cloud. The transmitted data is stored in the server's database, forming a clothes catalog for each user.

[0307] Step 3:

[0308] When the user starts their daily activities, the app inputs the user's facial expressions and voice into the emotion engine in real time. The emotion engine analyzes this and identifies the user's emotional state, preparing the data for analysis.

[0309] Step 4:

[0310] The server integrates the clothes information stored in the database and the acquired weather information, and further combines the data from the emotion engine. Using an AI algorithm, it generates the optimal combination of clothes for that day.

[0311] Step 5:

[0312] The server sends the generated optimal outfit suggestion to the terminal. The suggestion includes detailed information about each selected garment and the reason for its selection (e.g., relaxation effect based on emotion).

[0313] Step 6:

[0314] The terminal visually displays the coordination suggestions received from the server to the user, allowing them to review the suggestions. The user can then accept the suggestions or request further suggestions.

[0315] Step 7:

[0316] Users submit feedback on suggested outfits via the app. The emotion engine continuously updates the data, and the server uses this data to continuously improve the suggestions.

[0317] Step 8:

[0318] The server optimizes its algorithms to improve the accuracy of future suggestions based on feedback and real-time sentiment data. As new data influences potential suggestions, the system dynamically learns and provides a more personalized experience that meets user needs.

[0319] (Example 2)

[0320] 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".

[0321] Conventional clothing suggestion systems often fail to provide optimal suggestions because they rely solely on weather and basic clothing information, without considering the user's emotional state. Furthermore, they struggle to incorporate user feedback, resulting in a low degree of personalized suggestions. Additionally, they lack the ability to dynamically update outfits in real-time in response to changing circumstances.

[0322] 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.

[0323] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and the user's emotional data, means for obtaining feedback from the user and optimizing the suggestion means, and means for analyzing the user's emotions in real time and dynamically updating the coordination suggestions. This enables more personalized coordination suggestions that take into account the user's emotional state and external factors.

[0324] A "user" refers to an individual who uses a clothing suggestion system to receive outfit suggestions.

[0325] "Clothing information" refers to information about clothing owned by the user, and is digitized data including tag information such as color, brand, material, and intended use.

[0326] "Digitalization" is the process of converting information in a physical or analog state into an electronic format that can be processed by a computer.

[0327] "Emotional data" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and voice.

[0328] "Weather information" refers to weather data obtained from external sources and is a factor considered when proposing outfits.

[0329] "Suggestions" refer to the clothing combinations and outfit ideas that the system analyzes and provides to the user.

[0330] "Feedback" refers to user responses such as evaluations and comments on suggested outfits.

[0331] "Optimization" is the process of adjusting a system to improve its operation and provide suggestions that better meet user needs.

[0332] "Real-time" refers to a state of operation where data generation and processing occur instantaneously, and results are reflected immediately.

[0333] "Dynamic updating" is a process that keeps data and suggestions up-to-date whenever new information becomes available.

[0334] This invention is a system that digitizes information about a user's clothing and provides optimal outfit suggestions that take into account the user's emotional state and weather information. The system utilizes a terminal such as a smartphone, a network-connected server, and a dedicated application. The user uses the application installed on the terminal to take photos of the clothes they will wear and inputs tag information such as color, brand, material, and purpose. This information is temporarily stored on the terminal and then transmitted to the server.

[0335] The server stores the received clothing information in a database and uses a generative AI model to select suggested outfits. During this process, it obtains weather information using an external weather API and performs sentiment analysis based on the user's facial expressions and voice data collected from the device. This data is then analyzed by an AI algorithm to suggest the most suitable outfit for the user.

[0336] For example, if the emotion engine detects that a user is feeling "down," the server will prioritize suggesting clothing made of relaxing materials and outfits incorporating bright colors. This suggestion is sent from the server to the user's device and visually presented to the user through the application.

[0337] Furthermore, the system includes a feature to receive user feedback, and the suggestions are optimized based on that feedback. A novel feature of this system is its ability to dynamically update suggestions in response to real-time changes in emotional state.

[0338] An example of a prompt would be, "If a user is feeling down as they head to a casual meeting on a summer day, what outfit should be suggested?" Based on this prompt, the system can quickly generate a personalized outfit tailored to the user's state.

[0339] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0340] Step 1:

[0341] Users take photos of their clothing using their smartphones and input tag information such as color, brand, material, and intended use. This input is temporarily stored on the device as the user's clothing collection. The input at this stage consists of image data of the clothing and its metadata. This data is then sent to the server as user-specific digitized clothing data.

[0342] Step 2:

[0343] The server receives the submitted clothing data and stores it in a database. During this process, image data is preprocessed and tag information is cleaned. Specifically, images are resized and tags are standardized, resulting in a high-quality and consistent dataset. The output data is digital clothing information organized for each user.

[0344] Step 3:

[0345] The user provides facial expressions and voices during everyday activities via a device. The device collects this data and sends it to a server as input to operate the emotion engine. The emotion engine analyzes this data using image recognition and voice analysis technologies to identify the user's current emotional state. The output is quantified emotion data.

[0346] Step 4:

[0347] The server uses an external weather information API to obtain weather information related to the user's location. This operation retrieves data such as current temperature, humidity, and probability of precipitation. The retrieved weather data is used as an element when suggesting outfits. The output is weather information formatted in a parseable format.

[0348] Step 5:

[0349] The server integrates digitized clothing information, emotional data, and weather information from a stored database, and applies AI algorithms to generate the optimal clothing coordination for the user's situation. This process involves analyzing individual elements and evaluating their correlations and suitability. The output is a list of suggested outfits for the user.

[0350] Step 6:

[0351] The proposed outfits are sent from the server to the user's terminal. The terminal's application visually displays the proposed outfits to the user through its user interface. The UI includes interactive elements, allowing the user to review the proposals and provide feedback if necessary. The output is a visually verifiable outfit proposal.

[0352] Step 7:

[0353] User feedback is sent to the server and re-inputted as feedback data into the AI ​​algorithm to improve the quality of suggestions. The emotion engine also continuously monitors daily emotional changes, allowing the server to dynamically update outfits based on the latest emotional state. The final output is a personalized outfit suggestion that has evolved based on the user's emotional state and feedback.

[0354] (Application Example 2)

[0355] 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."

[0356] Traditional fashion suggestion systems only offered two-dimensional suggestions based on weather information and digital clothing data, without considering the user's emotional state. As a result, the suggested outfits often did not match the user's current emotional state, leading to a poor user experience. There is a need for technology that can solve this problem and provide more personalized outfit suggestions.

[0357] 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.

[0358] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and emotional state using an emotion engine that analyzes the user's emotional state, and means for displaying the suggested clothing combination to the user through a visual receiving device. This makes it possible to suggest personalized fashion coordinates that are appropriate to the user's emotions.

[0359] "User clothing information" refers to information about the clothing owned by the user, and is data that is digitized and managed.

[0360] "Digitalization" refers to the process of converting physical information into electronic data and managing it in a database.

[0361] An "emotion engine" is an algorithm that analyzes a user's emotional state from their facial expressions and voice to identify that state.

[0362] "Weather information" refers to data on current weather conditions and is used as an element that influences fashion suggestions.

[0363] A "visual receiving device" is a device that allows users to visually confirm digital data, and includes smartphones and head-mounted displays.

[0364] "Feedback" refers to the evaluations and comments that users provide regarding suggested outfits, and this information is useful for optimizing the system.

[0365] A "virtual space" is a digital space created by a computer, an environment that users can experience interactively.

[0366] "Outfit suggestions" refer to clothing combinations selected based on the user's individual conditions, and are generated taking into account their emotional state and environmental factors.

[0367] This invention is a system for providing users with fashion coordination based on their emotional state. In this system, the user's terminal manages the digital information of the clothing and analyzes the user's emotional state using an emotion engine.

[0368] Users take photos of their clothing using visual receiving devices such as smartphones or smart glasses, and digitize the information. Identification information is added to the clothing and saved as digital data. The user's device has a camera and microphone, which are used by an emotion engine to analyze the user's facial expressions and voice tone, employing TensorFlow and OpenCV to determine their current emotional state.

[0369] The server uses a generating AI model to calculate and provide users with appropriate clothing combinations based on digitized clothing information, weather information, and emotional state data. These outfit suggestions are visualized in a virtual space using Unity or Unreal Engine, and users can view them using visual receiving devices. User feedback on the suggested outfits is also collected to continuously optimize the system's suggestion algorithm.

[0370] As a concrete example, if a user wears smart glasses and asks aloud, "What should I wear today?", the system of this invention analyzes the input voice data and facial expression data to determine that the user is in a relaxed mood. Based on this result, a generative AI model is used to suggest an outfit with bright colors and soft materials. Through this experience, the user can receive more personalized fashion advice.

[0371] Example of a prompt:

[0372] "Please suggest an outfit that perfectly suits my mood today. I want to feel relaxed."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The device takes a picture of the user's clothing and digitizes the image. The device uses a camera to acquire a picture of the clothing and assigns identification information to it. The input is an image of the actual clothing, and the output is digitized clothing information stored in a digital database.

[0376] Step 2:

[0377] The server receives digitized clothing information sent from the terminal. The server stores the information in a database and manages clothing navigation and history as needed. The input is digitized clothing information, and the output is an update to a detailed clothing catalog.

[0378] Step 3:

[0379] The user accesses the system and communicates their current mood through voice and facial expressions. The terminal collects the user's voice and facial expression data and sends it to the emotion engine. The input is the user's current voice tone and facial expressions, and the output is the user's emotional state obtained by the emotion engine.

[0380] Step 4:

[0381] The emotion engine processes the user's voice and facial expression data to identify their current emotional state. Text analysis and image recognition algorithms are used to determine the emotion. The input is voice and facial expression data, and the output is the user's identified emotional state.

[0382] Step 5:

[0383] The server matches clothing information, weather information, and emotional state to generate the optimal clothing combination. A generative AI model is used to calculate outfits adapted to emotions and external factors. The input is digitized clothing information, weather information, and emotional information, and the output is the proposed clothing combination.

[0384] Step 6:

[0385] The terminal displays outfit suggestions received from the server to the user. It provides a visual virtual try-on experience, giving the user the opportunity to review and select from the suggestions. The input is the data of the suggested outfits, and the output is the outfit information visualized for the user.

[0386] Step 7:

[0387] The user provides feedback on the suggested outfit. The device collects this feedback and sends it to the server. The input is the user's feedback information, and the output is algorithmic data that is updated for optimization.

[0388] Step 8:

[0389] The server analyzes the received feedback and optimizes the proposed algorithm. As a result, the accuracy of subsequent proposals improves. The input is user feedback, and the output is the updated proposed algorithm.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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".

[0406] The system according to the present invention is a system for digitizing a user's clothing and suggesting personalized outfits based on that digitized information. The user installs a dedicated application on their terminal, takes photos of their clothing, and inputs tag information. The tag information includes the type of clothing, color, brand name, material, size, etc.

[0407] The server receives this clothing information from the user's device and stores it in a database. This digitized clothing information forms the foundational data for centrally managing all items in the user's closet.

[0408] The server periodically retrieves weather information via the internet and analyzes it in combination with user-specific clothing information stored in a database. This analysis calculates the most suitable daily outfit for each user. For example, on rainy days, it suggests items including waterproof jackets and shoes, while on sunny days, it recommends breathable, casual combinations.

[0409] The generated outfit suggestions are sent from the server to the user's device and displayed visually through the app. Users can select from these suggestions or, if necessary, request other suggestions. Furthermore, by submitting feedback on the suggestions, the system learns the user's preferences and optimizes itself to provide more appropriate suggestions.

[0410] For example, if a user specifies that they need "office casual attire for tomorrow," the server will use that request and the weather forecast to suggest an outfit that is casual yet incorporates formal elements. For instance, it might suggest a style combining a navy blazer, a white Oxford shirt, and gray slacks. In this way, the system can streamline the user's clothing selection process and, consequently, improve the quality of their fashion life.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] Users take photos of their clothes through a smartphone app and enter tag information (e.g., type of clothing, color, brand, material). This information is stored in the app and transmitted to a server via the internet.

[0414] Step 2:

[0415] The server stores clothing data received from the user's terminal in a database. During storage, tag information associated with the image data is also stored, forming a clothing list for each user.

[0416] Step 3:

[0417] The server obtains the latest weather information through an external data provision service. This information can be used to consider weather factors in coordination proposals.

[0418] Step 4:

[0419] The server uses an AI algorithm to calculate the optimal clothing combination for the user based on stored clothing data and weather information. It also refers to past suggestion history and user feedback to improve the quality of the styles.

[0420] Step 5:

[0421] The server sends the generated outfit suggestions to the user's device. The suggestions include images of each selected garment and notes on the combinations.

[0422] Step 6:

[0423] The device visually displays outfit suggestions received from the server within the application. The user can review the displayed suggestions and select their preferred outfit.

[0424] Step 7:

[0425] Users submit feedback on outfit suggestions through the app. This feedback includes an evaluation of whether the suggestion is suitable.

[0426] Step 8:

[0427] Feedback data from the device is sent back to the server and analyzed by an AI algorithm. Based on the feedback received, the suggestion method is optimized so that the next outfit suggestion is more personalized and better suited to the user's preferences.

[0428] (Example 1)

[0429] 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."

[0430] Modern consumers own a wide variety of clothing, making it difficult to choose the right outfits for each day. Furthermore, there is a demand for providing optimal clothing combinations tailored to individual preferences and the weather conditions. However, with so many options available, combining and deciding on the right outfits requires a tremendous amount of time and effort.

[0431] 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.

[0432] In this invention, the server includes means for digitizing the user's clothing information and storing it on an information technology infrastructure; means for performing analysis based on the digitized clothing information and acquired environmental information, and calculating the optimal clothing combination using a generated artificial intelligence model; and means for presenting the calculated clothing combination to the user via display technology. This makes it possible for the user to efficiently receive suggestions optimized according to their individual preferences and the weather, making it easier to choose their daily outfits.

[0433] A "user" is an individual or group that uses the system to manage their own clothing information and receive suggestions for the most suitable clothing combinations.

[0434] "Clothing information" refers to digital data about clothing owned by the user, including attributes such as type, color, brand name, material, and size.

[0435] "Digitalization" is the process of converting analog information into digital data and making it usable within a system.

[0436] "Information technology infrastructure" is a general term for hardware and software used for storing, managing, and analyzing data.

[0437] "Environmental information" refers to data about weather conditions and other external factors that the system acquires from external sources.

[0438] A "generative artificial intelligence model" is an algorithm or program that has the ability to generate knowledge and make new suggestions based on input data.

[0439] "Display technology" refers to technologies used to visually present digital data to users, and includes displays and smartphone screens.

[0440] This invention is a system for managing a user's clothing as digital data and suggesting the optimal clothing combinations based on this data. The user installs a dedicated application on their device. This application provides an interface for the user to take photos of their clothing and input detailed information such as type, color, brand name, material, and size. The device then transmits this input information to a server.

[0441] The server stores the received information in a database and digitizes the user's clothing information. This digital information functions as the user's virtual closet and serves as the basic data input into the generative AI model. The server periodically acquires weather information via the internet and analyzes this environmental information in combination with the clothing data. The generative AI model is used for analysis to generate optimal outfits in real time.

[0442] The generated outfit suggestions are sent from the server to the user's terminal. The user can view the suggestions visually on the terminal's application. These suggestions take into account the user's preferences and weather conditions, allowing the user to streamline their daily clothing choices. For example, if the user enters a prompt such as, "I need office casual attire tomorrow," the server will suggest appropriate combinations based on that request.

[0443] Furthermore, by providing user feedback on the suggestions, the server can continuously optimize the generated AI model, enabling it to provide more refined suggestions. This gradually improves the user experience and results in more personalized suggestions tailored to each user's preferences and style.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The user installs a dedicated application on their device, takes a picture of their clothing, and inputs clothing information such as type, color, brand name, material, and size. The device collects this input data and generates a data package. Next, it prepares to send that package to the server. The output at this stage is the package of clothing information entered by the user.

[0447] Step 2:

[0448] The server receives a package of clothing information sent from the terminal. The received data is stored in a database. Next, the server updates the user's digital closet based on this data, centrally managing the clothing information of each individual user. The output of this step is the updated digital closet data.

[0449] Step 3:

[0450] The server periodically accesses an external weather information service to obtain the latest weather data. This weather data is added to the database and used in combination with clothing information. The input for this step is the weather data obtained from an external source, and the output is the complete information integrated into the database.

[0451] Step 4:

[0452] The server uses a generative AI model to analyze clothing information from the digital closet and weather information. This analysis uses prompts entered by the user, and the model generates the optimal clothing combination. For example, it would respond to a prompt such as, "I need office casual attire tomorrow." The output of this step is the generated outfit suggestion.

[0453] Step 5:

[0454] The server sends the generated coordination proposal to the user's terminal. The terminal displays the received proposal data to the user visually through the application interface. The user can review this proposal and accept it as needed, or request other proposals. The output of this step is the visually displayed coordination proposal.

[0455] Step 6:

[0456] The user provides feedback on the suggestions. This feedback is sent to the server via the terminal. The server uses this feedback information to adjust the generative AI model and improve the system so that it can make more effective suggestions based on the user's preferences. The final output is the optimized generative AI model.

[0457] (Application Example 1)

[0458] 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."

[0459] In modern times, users often spend time and effort selecting daily outfits from their vast wardrobe. Furthermore, consistently choosing attire appropriate for different weather conditions and events can be challenging. Therefore, there is a need to support users in their clothing choices and reduce daily stress through efficient and personalized outfit suggestions.

[0460] 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.

[0461] In this invention, the server includes means for quantifying the user's clothing data, means for recommending appropriate clothing combinations based on the quantified clothing data and weather data, and means for visually presenting the recommended clothing combinations to the user. This enables the user to efficiently select the most suitable outfit.

[0462] A "user" is an individual who uses the system to receive clothing coordination suggestions.

[0463] "Clothing data" refers to a digital representation of information about the clothing owned by a user.

[0464] "Digitization" refers to converting clothing information into a format that can be processed by a computer.

[0465] "Weather data" refers to information about current and future weather conditions.

[0466] "Outfit combinations" refer to the selection of clothing that is considered optimal for a particular occasion or weather condition.

[0467] "Recommendation" is the act of selecting and presenting the optimal option.

[0468] "Visual presentation" means displaying information to the user on a screen using photos, illustrations, and other visual means.

[0469] A "virtual model" is a digital doll displayed in a virtual space.

[0470] "Trying on clothes" means temporarily putting on clothing to check whether it fits or not.

[0471] "Collecting ratings" means gathering opinions and feedback from users.

[0472] "Adaptive adjustment" means improving the accuracy of the system's suggestions based on user feedback.

[0473] The system for realizing this invention consists of a user's communication terminal and a server. The user uses the communication terminal to take a picture of their clothing and inputs clothing information. Clothing information includes type, color, material, etc. The communication terminal transmits this information to the server.

[0474] The server uses the Google Cloud Vision API to extract tag information from images and digitize clothing data. This digitized data is stored in a database, forming the user's virtual closet. Next, the server periodically retrieves weather data from the internet. This data is combined with the user's clothing data, and a generative AI model using TensorFlow recommends the best outfit combinations. The server sends these recommendations to a communication terminal and presents them visually to the user.

[0475] By wearing a head-mounted display, users can virtually try on suggested outfits in 3D virtual models. Furthermore, user ratings and feedback are collected on a server, and the algorithm is adjusted based on this data to improve the accuracy of recommendations.

[0476] As a concrete example, if a user requests a "stylish casual outfit for a cloudy day tomorrow" from their device, the system will suggest an appropriate outfit through its generative AI model.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1: The user uses a communication device to take a picture of their clothing and enter the clothing information. This information includes the type, color, and material of the clothing. The entered data is temporarily stored in the communication device as tag information.

[0479] Step 2: The device sends the saved clothing photos and tag information to the server. The server uses the Google Cloud Vision API to analyze the photos and extract tag information from the images. Based on the tag information obtained through the analysis, the clothing data is quantified and stored in a database.

[0480] Step 3: The server retrieves weather data from the internet. The retrieved weather data is combined with the user's clothing data in the database, and a generative AI model is used to perform data calculations and recommend the optimal outfit combination. The output data is the recommended outfit information.

[0481] Step 4: The server sends the recommended results to the user's communication terminal. On the user's terminal, the suggested outfit is displayed on a virtual model using 3D modeling technology via a head-mounted display. The user can visually confirm the outfit.

[0482] Step 5: The user sends their evaluation and feedback about their appearance from their device to the server. This feedback is collected by the server and used to refine the algorithm. This improves the accuracy of future recommendations.

[0483] 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.

[0484] The system according to the present invention not only digitizes the user's clothing information and proposes personalized outfits, but also incorporates an emotion engine that recognizes the user's emotions. This makes it possible to provide more appropriate fashion suggestions according to the user's emotional state.

[0485] Users take photos of their clothes using a smartphone app and input tag information. This process digitizes and centrally manages their clothing collection in a database. The app's emotion engine operates based on the user's facial expressions and voice. The emotion engine analyzes the user's facial data and voice tone to identify their current emotional state. This emotional data influences the fashion coordination suggestions.

[0486] The server integrates and analyzes digitized clothing information, current weather information, and acquired user sentiment data. An AI algorithm calculates the outfit that best suits the user's emotions and external factors. For example, if the user is feeling stressed, it suggests loose and comfortable clothing, and if they are in a cheerful mood, it recommends bright-colored clothing.

[0487] The generated outfit suggestions are sent from the server to the user's device and displayed through the app. Users can review the suggested outfits and submit feedback to enable further optimization. The emotion engine also monitors the user's emotional changes in real time and updates the suggestions as needed.

[0488] For example, if the emotion engine identifies a user as "feeling down" on a workday, the system will suggest outfits that focus on relaxing materials and incorporate color theory to brighten their mood. In this way, the system provides a fashion experience that takes the user's emotional state into consideration, further enhancing their personalized lifestyle.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The user launches the smartphone app and takes a picture of their clothing. At the same time, they input tag information about the clothing (type, color, brand, etc.) and save the data to the app.

[0492] Step 2:

[0493] The device sends the saved clothing data to the server via the cloud. The transmitted data is stored in the server's database, and a clothing catalog for each user is created.

[0494] Step 3:

[0495] When a user begins their daily activities, the app inputs their facial expressions and voice into the emotion engine in real time. The emotion engine analyzes this data to identify the user's emotional state and prepares it for further analysis.

[0496] Step 4:

[0497] The server integrates clothing information stored in the database with acquired weather information, and further combines it with data from the emotion engine. Using an AI algorithm, it generates the optimal clothing combination for the day.

[0498] Step 5:

[0499] The server sends the generated optimal outfit suggestion to the terminal. The suggestion includes detailed information about each selected garment and the reason for its selection (e.g., relaxation effect based on emotion).

[0500] Step 6:

[0501] The terminal visually displays the coordination suggestions received from the server to the user, allowing them to review the suggestions. The user can then accept the suggestions or request further suggestions.

[0502] Step 7:

[0503] Users submit feedback on suggested outfits via the app. The emotion engine continuously updates the data, and the server uses this data to continuously improve the suggestions.

[0504] Step 8:

[0505] The server optimizes its algorithms to improve the accuracy of future suggestions based on feedback and real-time sentiment data. As new data influences potential suggestions, the system dynamically learns and provides a more personalized experience that meets user needs.

[0506] (Example 2)

[0507] 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."

[0508] Conventional clothing suggestion systems often fail to provide optimal suggestions because they rely solely on weather and basic clothing information, without considering the user's emotional state. Furthermore, they struggle to incorporate user feedback, resulting in a low degree of personalized suggestions. Additionally, they lack the ability to dynamically update outfits in real-time in response to changing circumstances.

[0509] 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.

[0510] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and the user's emotional data, means for obtaining feedback from the user and optimizing the suggestion means, and means for analyzing the user's emotions in real time and dynamically updating the coordination suggestions. This enables more personalized coordination suggestions that take into account the user's emotional state and external factors.

[0511] A "user" refers to an individual who uses a clothing suggestion system to receive outfit suggestions.

[0512] "Clothing information" refers to information about clothing owned by the user, and is digitized data including tag information such as color, brand, material, and intended use.

[0513] "Digitalization" is the process of converting information in a physical or analog state into an electronic format that can be processed by a computer.

[0514] "Emotional data" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and voice.

[0515] "Weather information" refers to weather data obtained from external sources and is a factor considered when proposing outfits.

[0516] "Suggestions" refer to the clothing combinations and outfit ideas that the system analyzes and provides to the user.

[0517] "Feedback" refers to user responses such as evaluations and comments on suggested outfits.

[0518] "Optimization" is the process of adjusting a system to improve its operation and provide suggestions that better meet user needs.

[0519] "Real-time" refers to a state of operation where data generation and processing occur instantaneously, and results are reflected immediately.

[0520] "Dynamic updating" is a process that keeps data and suggestions up-to-date whenever new information becomes available.

[0521] This invention is a system that digitizes information about a user's clothing and provides optimal outfit suggestions that take into account the user's emotional state and weather information. The system utilizes a terminal such as a smartphone, a network-connected server, and a dedicated application. The user uses the application installed on the terminal to take photos of the clothes they will wear and inputs tag information such as color, brand, material, and purpose. This information is temporarily stored on the terminal and then transmitted to the server.

[0522] The server stores the received clothing information in a database and uses a generative AI model to select suggested outfits. During this process, it obtains weather information using an external weather API and performs sentiment analysis based on the user's facial expressions and voice data collected from the device. This data is then analyzed by an AI algorithm to suggest the most suitable outfit for the user.

[0523] For example, if the emotion engine detects that a user is feeling "down," the server will prioritize suggesting clothing made of relaxing materials and outfits incorporating bright colors. This suggestion is sent from the server to the user's device and visually presented to the user through the application.

[0524] Furthermore, the system includes a feature to receive user feedback, and the suggestions are optimized based on that feedback. A novel feature of this system is its ability to dynamically update suggestions in response to real-time changes in emotional state.

[0525] An example of a prompt would be, "If a user is feeling down as they head to a casual meeting on a summer day, what outfit should be suggested?" Based on this prompt, the system can quickly generate a personalized outfit tailored to the user's state.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] Users take photos of their clothing using their smartphones and input tag information such as color, brand, material, and intended use. This input is temporarily stored on the device as the user's clothing collection. The input at this stage consists of image data of the clothing and its metadata. This data is then sent to the server as user-specific digitized clothing data.

[0529] Step 2:

[0530] The server receives the submitted clothing data and stores it in a database. During this process, image data is preprocessed and tag information is cleaned. Specifically, images are resized and tags are standardized, resulting in a high-quality and consistent dataset. The output data is digital clothing information organized for each user.

[0531] Step 3:

[0532] The user provides facial expressions and voices during everyday activities via a device. The device collects this data and sends it to a server as input to operate the emotion engine. The emotion engine analyzes this data using image recognition and voice analysis technologies to identify the user's current emotional state. The output is quantified emotion data.

[0533] Step 4:

[0534] The server uses an external weather information API to obtain weather information related to the user's location. This operation retrieves data such as current temperature, humidity, and probability of precipitation. The retrieved weather data is used as an element when suggesting outfits. The output is weather information formatted in a parseable format.

[0535] Step 5:

[0536] The server integrates digitized clothing information, emotional data, and weather information from a stored database, and applies AI algorithms to generate the optimal clothing coordination for the user's situation. This process involves analyzing individual elements and evaluating their correlations and suitability. The output is a list of suggested outfits for the user.

[0537] Step 6:

[0538] The proposed outfits are sent from the server to the user's terminal. The terminal's application visually displays the proposed outfits to the user through its user interface. The UI includes interactive elements, allowing the user to review the proposals and provide feedback if necessary. The output is a visually verifiable outfit proposal.

[0539] Step 7:

[0540] User feedback is sent to the server and re-inputted as feedback data into the AI ​​algorithm to improve the quality of suggestions. The emotion engine also continuously monitors daily emotional changes, allowing the server to dynamically update outfits based on the latest emotional state. The final output is a personalized outfit suggestion that has evolved based on the user's emotional state and feedback.

[0541] (Application Example 2)

[0542] 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."

[0543] Traditional fashion suggestion systems only offered two-dimensional suggestions based on weather information and digital clothing data, without considering the user's emotional state. As a result, the suggested outfits often did not match the user's current emotional state, leading to a poor user experience. There is a need for technology that can solve this problem and provide more personalized outfit suggestions.

[0544] 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.

[0545] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and emotional state using an emotion engine that analyzes the user's emotional state, and means for displaying the suggested clothing combination to the user through a visual receiving device. This makes it possible to suggest personalized fashion coordinates that are appropriate to the user's emotions.

[0546] "User clothing information" refers to information about the clothing owned by the user, and is data that is digitized and managed.

[0547] "Digitalization" refers to the process of converting physical information into electronic data and managing it in a database.

[0548] An "emotion engine" is an algorithm that analyzes a user's emotional state from their facial expressions and voice to identify that state.

[0549] "Weather information" refers to data on current weather conditions and is used as an element that influences fashion suggestions.

[0550] A "visual receiving device" is a device that allows users to visually confirm digital data, and includes smartphones and head-mounted displays.

[0551] "Feedback" refers to the evaluations and comments that users provide regarding suggested outfits, and this information is useful for optimizing the system.

[0552] A "virtual space" is a digital space created by a computer, an environment that users can experience interactively.

[0553] "Outfit suggestions" refer to clothing combinations selected based on the user's individual conditions, and are generated taking into account their emotional state and environmental factors.

[0554] This invention is a system for providing users with fashion coordination based on their emotional state. In this system, the user's terminal manages the digital information of the clothing and analyzes the user's emotional state using an emotion engine.

[0555] Users take photos of their clothing using visual receiving devices such as smartphones or smart glasses, and digitize the information. Identification information is added to the clothing and saved as digital data. The user's device has a camera and microphone, which are used by an emotion engine to analyze the user's facial expressions and voice tone, employing TensorFlow and OpenCV to determine their current emotional state.

[0556] The server uses a generating AI model to calculate and provide users with appropriate clothing combinations based on digitized clothing information, weather information, and emotional state data. These outfit suggestions are visualized in a virtual space using Unity or Unreal Engine, and users can view them using visual receiving devices. User feedback on the suggested outfits is also collected to continuously optimize the system's suggestion algorithm.

[0557] As a concrete example, if a user wears smart glasses and asks aloud, "What should I wear today?", the system of this invention analyzes the input voice data and facial expression data to determine that the user is in a relaxed mood. Based on this result, a generative AI model is used to suggest an outfit with bright colors and soft materials. Through this experience, the user can receive more personalized fashion advice.

[0558] Example of a prompt:

[0559] "Please suggest an outfit that perfectly suits my mood today. I want to feel relaxed."

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The device takes a picture of the user's clothing and digitizes the image. The device uses a camera to acquire a picture of the clothing and assigns identification information to it. The input is an image of the actual clothing, and the output is digitized clothing information stored in a digital database.

[0563] Step 2:

[0564] The server receives digitized clothing information sent from the terminal. The server stores the information in a database and manages clothing navigation and history as needed. The input is digitized clothing information, and the output is an update to a detailed clothing catalog.

[0565] Step 3:

[0566] The user accesses the system and communicates their current mood through voice and facial expressions. The terminal collects the user's voice and facial expression data and sends it to the emotion engine. The input is the user's current voice tone and facial expressions, and the output is the user's emotional state obtained by the emotion engine.

[0567] Step 4:

[0568] The emotion engine processes the user's voice and facial expression data to identify their current emotional state. Text analysis and image recognition algorithms are used to determine the emotion. The input is voice and facial expression data, and the output is the user's identified emotional state.

[0569] Step 5:

[0570] The server matches clothing information, weather information, and emotional state to generate the optimal clothing combination. A generative AI model is used to calculate outfits adapted to emotions and external factors. The input is digitized clothing information, weather information, and emotional information, and the output is the proposed clothing combination.

[0571] Step 6:

[0572] The terminal displays outfit suggestions received from the server to the user. It provides a visual virtual try-on experience, giving the user the opportunity to review and select from the suggestions. The input is the data of the suggested outfits, and the output is the outfit information visualized for the user.

[0573] Step 7:

[0574] The user provides feedback on the suggested outfit. The device collects this feedback and sends it to the server. The input is the user's feedback information, and the output is algorithmic data that is updated for optimization.

[0575] Step 8:

[0576] The server analyzes the received feedback and optimizes the proposed algorithm. As a result, the accuracy of subsequent proposals improves. The input is user feedback, and the output is the updated proposed algorithm.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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".

[0594] The system according to the present invention is a system for digitizing a user's clothing and suggesting personalized outfits based on that digitized information. The user installs a dedicated application on their terminal, takes photos of their clothing, and inputs tag information. The tag information includes the type of clothing, color, brand name, material, size, etc.

[0595] The server receives this clothing information from the user's device and stores it in a database. This digitized clothing information forms the foundational data for centrally managing all items in the user's closet.

[0596] The server periodically retrieves weather information via the internet and analyzes it in combination with user-specific clothing information stored in a database. This analysis calculates the most suitable daily outfit for each user. For example, on rainy days, it suggests items including waterproof jackets and shoes, while on sunny days, it recommends breathable, casual combinations.

[0597] The generated outfit suggestions are sent from the server to the user's device and displayed visually through the app. Users can select from these suggestions or, if necessary, request other suggestions. Furthermore, by submitting feedback on the suggestions, the system learns the user's preferences and optimizes itself to provide more appropriate suggestions.

[0598] For example, if a user specifies that they need "office casual attire for tomorrow," the server will use that request and the weather forecast to suggest an outfit that is casual yet incorporates formal elements. For instance, it might suggest a style combining a navy blazer, a white Oxford shirt, and gray slacks. In this way, the system can streamline the user's clothing selection process and, consequently, improve the quality of their fashion life.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] Users take photos of their clothes through a smartphone app and enter tag information (e.g., type of clothing, color, brand, material). This information is stored in the app and transmitted to a server via the internet.

[0602] Step 2:

[0603] The server stores clothing data received from the user's terminal in a database. During storage, tag information associated with the image data is also stored, forming a clothing list for each user.

[0604] Step 3:

[0605] The server obtains the latest weather information through an external data provision service. This information can be used to consider weather factors in coordination proposals.

[0606] Step 4:

[0607] The server uses an AI algorithm to calculate the optimal clothing combination for the user based on stored clothing data and weather information. It also refers to past suggestion history and user feedback to improve the quality of the styles.

[0608] Step 5:

[0609] The server sends the generated outfit suggestions to the user's device. The suggestions include images of each selected garment and notes on the combinations.

[0610] Step 6:

[0611] The device visually displays outfit suggestions received from the server within the application. The user can review the displayed suggestions and select their preferred outfit.

[0612] Step 7:

[0613] Users submit feedback on outfit suggestions through the app. This feedback includes an evaluation of whether the suggestion is suitable.

[0614] Step 8:

[0615] Feedback data from the device is sent back to the server and analyzed by an AI algorithm. Based on the feedback received, the suggestion method is optimized so that the next outfit suggestion is more personalized and better suited to the user's preferences.

[0616] (Example 1)

[0617] 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".

[0618] Modern consumers own a wide variety of clothing, making it difficult to choose the right outfits for each day. Furthermore, there is a demand for providing optimal clothing combinations tailored to individual preferences and the weather conditions. However, with so many options available, combining and deciding on the right outfits requires a tremendous amount of time and effort.

[0619] 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.

[0620] In this invention, the server includes means for digitizing the user's clothing information and storing it on an information technology infrastructure; means for performing analysis based on the digitized clothing information and acquired environmental information, and calculating the optimal clothing combination using a generated artificial intelligence model; and means for presenting the calculated clothing combination to the user via display technology. This makes it possible for the user to efficiently receive suggestions optimized according to their individual preferences and the weather, making it easier to choose their daily outfits.

[0621] A "user" is an individual or group that uses the system to manage their own clothing information and receive suggestions for the most suitable clothing combinations.

[0622] "Clothing information" refers to digital data about clothing owned by the user, including attributes such as type, color, brand name, material, and size.

[0623] "Digitalization" is the process of converting analog information into digital data and making it usable within a system.

[0624] "Information technology infrastructure" is a general term for hardware and software used for storing, managing, and analyzing data.

[0625] "Environmental information" refers to data about weather conditions and other external factors that the system acquires from external sources.

[0626] A "generative artificial intelligence model" is an algorithm or program that has the ability to generate knowledge and make new suggestions based on input data.

[0627] "Display technology" refers to technologies used to visually present digital data to users, and includes displays and smartphone screens.

[0628] This invention is a system for managing a user's clothing as digital data and suggesting the optimal clothing combinations based on this data. The user installs a dedicated application on their device. This application provides an interface for the user to take photos of their clothing and input detailed information such as type, color, brand name, material, and size. The device then transmits this input information to a server.

[0629] The server stores the received information in a database and digitizes the user's clothing information. This digital information functions as the user's virtual closet and serves as the basic data input into the generative AI model. The server periodically acquires weather information via the internet and analyzes this environmental information in combination with the clothing data. The generative AI model is used for analysis to generate optimal outfits in real time.

[0630] The generated outfit suggestions are sent from the server to the user's terminal. The user can view the suggestions visually on the terminal's application. These suggestions take into account the user's preferences and weather conditions, allowing the user to streamline their daily clothing choices. For example, if the user enters a prompt such as, "I need office casual attire tomorrow," the server will suggest appropriate combinations based on that request.

[0631] Furthermore, by providing user feedback on the suggestions, the server can continuously optimize the generated AI model, enabling it to provide more refined suggestions. This gradually improves the user experience and results in more personalized suggestions tailored to each user's preferences and style.

[0632] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0633] Step 1:

[0634] The user installs a dedicated application on their device, takes a picture of their clothing, and inputs clothing information such as type, color, brand name, material, and size. The device collects this input data and generates a data package. Next, it prepares to send that package to the server. The output at this stage is the package of clothing information entered by the user.

[0635] Step 2:

[0636] The server receives a package of clothing information sent from the terminal. The received data is stored in a database. Next, the server updates the user's digital closet based on this data, centrally managing the clothing information of each individual user. The output of this step is the updated digital closet data.

[0637] Step 3:

[0638] The server periodically accesses an external weather information service to obtain the latest weather data. This weather data is added to the database and used in combination with clothing information. The input for this step is the weather data obtained from an external source, and the output is the complete information integrated into the database.

[0639] Step 4:

[0640] The server uses a generative AI model to analyze clothing information from the digital closet and weather information. This analysis uses prompts entered by the user, and the model generates the optimal clothing combination. For example, it would respond to a prompt such as, "I need office casual attire tomorrow." The output of this step is the generated outfit suggestion.

[0641] Step 5:

[0642] The server sends the generated coordination proposal to the user's terminal. The terminal displays the received proposal data to the user visually through the application interface. The user can review this proposal and accept it as needed, or request other proposals. The output of this step is the visually displayed coordination proposal.

[0643] Step 6:

[0644] The user provides feedback on the suggestions. This feedback is sent to the server via the terminal. The server uses this feedback information to adjust the generative AI model and improve the system so that it can make more effective suggestions based on the user's preferences. The final output is the optimized generative AI model.

[0645] (Application Example 1)

[0646] 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".

[0647] In modern times, users often spend time and effort selecting daily outfits from their vast wardrobe. Furthermore, consistently choosing attire appropriate for different weather conditions and events can be challenging. Therefore, there is a need to support users in their clothing choices and reduce daily stress through efficient and personalized outfit suggestions.

[0648] 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.

[0649] In this invention, the server includes means for quantifying the user's clothing data, means for recommending appropriate clothing combinations based on the quantified clothing data and weather data, and means for visually presenting the recommended clothing combinations to the user. This enables the user to efficiently select the most suitable outfit.

[0650] A "user" is an individual who uses the system to receive clothing coordination suggestions.

[0651] "Clothing data" refers to a digital representation of information about the clothing owned by a user.

[0652] "Digitization" refers to converting clothing information into a format that can be processed by a computer.

[0653] "Weather data" refers to information about current and future weather conditions.

[0654] "Outfit combinations" refer to the selection of clothing that is considered optimal for a particular occasion or weather condition.

[0655] "Recommendation" is the act of selecting and presenting the optimal option.

[0656] "Visual presentation" means displaying information to the user on a screen using photos, illustrations, and other visual means.

[0657] A "virtual model" is a digital doll displayed in a virtual space.

[0658] "Trying on clothes" means temporarily putting on clothing to check whether it fits or not.

[0659] "Collecting ratings" means gathering opinions and feedback from users.

[0660] "Adaptive adjustment" means improving the accuracy of the system's suggestions based on user feedback.

[0661] The system for realizing this invention consists of a user's communication terminal and a server. The user uses the communication terminal to take a picture of their clothing and inputs clothing information. Clothing information includes type, color, material, etc. The communication terminal transmits this information to the server.

[0662] The server uses the Google Cloud Vision API to extract tag information from images and digitize clothing data. This digitized data is stored in a database, forming the user's virtual closet. Next, the server periodically retrieves weather data from the internet. This data is combined with the user's clothing data, and a generative AI model using TensorFlow recommends the best outfit combinations. The server sends these recommendations to a communication terminal and presents them visually to the user.

[0663] By wearing a head-mounted display, users can virtually try on suggested outfits in 3D virtual models. Furthermore, user ratings and feedback are collected on a server, and the algorithm is adjusted based on this data to improve the accuracy of recommendations.

[0664] As a concrete example, if a user requests a "stylish casual outfit for a cloudy day tomorrow" from their device, the system will suggest an appropriate outfit through its generative AI model.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1: The user uses a communication device to take a picture of their clothing and enter the clothing information. This information includes the type, color, and material of the clothing. The entered data is temporarily stored in the communication device as tag information.

[0667] Step 2: The device sends the saved clothing photos and tag information to the server. The server uses the Google Cloud Vision API to analyze the photos and extract tag information from the images. Based on the tag information obtained through the analysis, the clothing data is quantified and stored in a database.

[0668] Step 3: The server retrieves weather data from the internet. The retrieved weather data is combined with the user's clothing data in the database, and a generative AI model is used to perform data calculations and recommend the optimal outfit combination. The output data is the recommended outfit information.

[0669] Step 4: The server sends the recommended results to the user's communication terminal. On the user's terminal, the suggested outfit is displayed on a virtual model using 3D modeling technology via a head-mounted display. The user can visually confirm the outfit.

[0670] Step 5: The user sends their evaluation and feedback about their appearance from their device to the server. This feedback is collected by the server and used to refine the algorithm. This improves the accuracy of future recommendations.

[0671] 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.

[0672] The system according to the present invention not only digitizes the user's clothing information and proposes personalized outfits, but also incorporates an emotion engine that recognizes the user's emotions. This makes it possible to provide more appropriate fashion suggestions according to the user's emotional state.

[0673] Users take photos of their clothes using a smartphone app and input tag information. This process digitizes and centrally manages their clothing collection in a database. The app's emotion engine operates based on the user's facial expressions and voice. The emotion engine analyzes the user's facial data and voice tone to identify their current emotional state. This emotional data influences the fashion coordination suggestions.

[0674] The server integrates and analyzes digitized clothing information, current weather information, and acquired user sentiment data. An AI algorithm calculates the outfit that best suits the user's emotions and external factors. For example, if the user is feeling stressed, it suggests loose and comfortable clothing, and if they are in a cheerful mood, it recommends bright-colored clothing.

[0675] The generated outfit suggestions are sent from the server to the user's device and displayed through the app. Users can review the suggested outfits and submit feedback to enable further optimization. The emotion engine also monitors the user's emotional changes in real time and updates the suggestions as needed.

[0676] For example, if the emotion engine identifies a user as "feeling down" on a workday, the system will suggest outfits that focus on relaxing materials and incorporate color theory to brighten their mood. In this way, the system provides a fashion experience that takes the user's emotional state into consideration, further enhancing their personalized lifestyle.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The user launches the smartphone app and takes a picture of their clothing. At the same time, they input tag information about the clothing (type, color, brand, etc.) and save the data to the app.

[0680] Step 2:

[0681] The device sends the saved clothing data to the server via the cloud. The transmitted data is stored in the server's database, and a clothing catalog for each user is created.

[0682] Step 3:

[0683] When a user begins their daily activities, the app inputs their facial expressions and voice into the emotion engine in real time. The emotion engine analyzes this data to identify the user's emotional state and prepares it for further analysis.

[0684] Step 4:

[0685] The server integrates clothing information stored in the database with acquired weather information, and further combines it with data from the emotion engine. Using an AI algorithm, it generates the optimal clothing combination for the day.

[0686] Step 5:

[0687] The server sends the generated optimal outfit suggestion to the terminal. The suggestion includes detailed information about each selected garment and the reason for its selection (e.g., relaxation effect based on emotion).

[0688] Step 6:

[0689] The terminal visually displays the coordination suggestions received from the server to the user, allowing them to review the suggestions. The user can then accept the suggestions or request further suggestions.

[0690] Step 7:

[0691] Users submit feedback on suggested outfits via the app. The emotion engine continuously updates the data, and the server uses this data to continuously improve the suggestions.

[0692] Step 8:

[0693] The server optimizes its algorithms to improve the accuracy of future suggestions based on feedback and real-time sentiment data. As new data influences potential suggestions, the system dynamically learns and provides a more personalized experience that meets user needs.

[0694] (Example 2)

[0695] 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".

[0696] Conventional clothing suggestion systems often fail to provide optimal suggestions because they rely solely on weather and basic clothing information, without considering the user's emotional state. Furthermore, they struggle to incorporate user feedback, resulting in a low degree of personalized suggestions. Additionally, they lack the ability to dynamically update outfits in real-time in response to changing circumstances.

[0697] 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.

[0698] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and the user's emotional data, means for obtaining feedback from the user and optimizing the suggestion means, and means for analyzing the user's emotions in real time and dynamically updating the coordination suggestions. This enables more personalized coordination suggestions that take into account the user's emotional state and external factors.

[0699] A "user" refers to an individual who uses a clothing suggestion system to receive outfit suggestions.

[0700] "Clothing information" refers to information about clothing owned by the user, and is digitized data including tag information such as color, brand, material, and intended use.

[0701] "Digitalization" is the process of converting information in a physical or analog state into an electronic format that can be processed by a computer.

[0702] "Emotional data" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and voice.

[0703] "Weather information" refers to weather data obtained from external sources and is a factor considered when proposing outfits.

[0704] "Suggestions" refer to the clothing combinations and outfit ideas that the system analyzes and provides to the user.

[0705] "Feedback" refers to user responses such as evaluations and comments on suggested outfits.

[0706] "Optimization" is the process of adjusting a system to improve its operation and provide suggestions that better meet user needs.

[0707] "Real-time" refers to a state of operation where data generation and processing occur instantaneously, and results are reflected immediately.

[0708] "Dynamic updating" is a process that keeps data and suggestions up-to-date whenever new information becomes available.

[0709] This invention is a system that digitizes information about a user's clothing and provides optimal outfit suggestions that take into account the user's emotional state and weather information. The system utilizes a terminal such as a smartphone, a network-connected server, and a dedicated application. The user uses the application installed on the terminal to take photos of the clothes they will wear and inputs tag information such as color, brand, material, and purpose. This information is temporarily stored on the terminal and then transmitted to the server.

[0710] The server stores the received clothing information in a database and uses a generative AI model to select suggested outfits. During this process, it obtains weather information using an external weather API and performs sentiment analysis based on the user's facial expressions and voice data collected from the device. This data is then analyzed by an AI algorithm to suggest the most suitable outfit for the user.

[0711] For example, if the emotion engine detects that a user is feeling "down," the server will prioritize suggesting clothing made of relaxing materials and outfits incorporating bright colors. This suggestion is sent from the server to the user's device and visually presented to the user through the application.

[0712] Furthermore, the system includes a feature to receive user feedback, and the suggestions are optimized based on that feedback. A novel feature of this system is its ability to dynamically update suggestions in response to real-time changes in emotional state.

[0713] An example of a prompt would be, "If a user is feeling down as they head to a casual meeting on a summer day, what outfit should be suggested?" Based on this prompt, the system can quickly generate a personalized outfit tailored to the user's state.

[0714] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0715] Step 1:

[0716] Users take photos of their clothing using their smartphones and input tag information such as color, brand, material, and intended use. This input is temporarily stored on the device as the user's clothing collection. The input at this stage consists of image data of the clothing and its metadata. This data is then sent to the server as user-specific digitized clothing data.

[0717] Step 2:

[0718] The server receives the submitted clothing data and stores it in a database. During this process, image data is preprocessed and tag information is cleaned. Specifically, images are resized and tags are standardized, resulting in a high-quality and consistent dataset. The output data is digital clothing information organized for each user.

[0719] Step 3:

[0720] The user provides facial expressions and voices during everyday activities via a device. The device collects this data and sends it to a server as input to operate the emotion engine. The emotion engine analyzes this data using image recognition and voice analysis technologies to identify the user's current emotional state. The output is quantified emotion data.

[0721] Step 4:

[0722] The server uses an external weather information API to obtain weather information related to the user's location. This operation retrieves data such as current temperature, humidity, and probability of precipitation. The retrieved weather data is used as an element when suggesting outfits. The output is weather information formatted in a parseable format.

[0723] Step 5:

[0724] The server integrates digitized clothing information, emotional data, and weather information from a stored database, and applies AI algorithms to generate the optimal clothing coordination for the user's situation. This process involves analyzing individual elements and evaluating their correlations and suitability. The output is a list of suggested outfits for the user.

[0725] Step 6:

[0726] The proposed outfits are sent from the server to the user's terminal. The terminal's application visually displays the proposed outfits to the user through its user interface. The UI includes interactive elements, allowing the user to review the proposals and provide feedback if necessary. The output is a visually verifiable outfit proposal.

[0727] Step 7:

[0728] User feedback is sent to the server and re-inputted as feedback data into the AI ​​algorithm to improve the quality of suggestions. The emotion engine also continuously monitors daily emotional changes, allowing the server to dynamically update outfits based on the latest emotional state. The final output is a personalized outfit suggestion that has evolved based on the user's emotional state and feedback.

[0729] (Application Example 2)

[0730] 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".

[0731] Traditional fashion suggestion systems only offered two-dimensional suggestions based on weather information and digital clothing data, without considering the user's emotional state. As a result, the suggested outfits often did not match the user's current emotional state, leading to a poor user experience. There is a need for technology that can solve this problem and provide more personalized outfit suggestions.

[0732] 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.

[0733] In this invention, the server includes means for digitizing the user's clothing information, means for suggesting the optimal clothing combination based on the digitized clothing information, weather information, and emotional state using an emotion engine that analyzes the user's emotional state, and means for displaying the suggested clothing combination to the user through a visual receiving device. This makes it possible to suggest personalized fashion coordinates that are appropriate to the user's emotions.

[0734] "User clothing information" refers to information about the clothing owned by the user, and is data that is digitized and managed.

[0735] "Digitalization" refers to the process of converting physical information into electronic data and managing it in a database.

[0736] An "emotion engine" is an algorithm that analyzes a user's emotional state from their facial expressions and voice to identify that state.

[0737] "Weather information" refers to data on current weather conditions and is used as an element that influences fashion suggestions.

[0738] A "visual receiving device" is a device that allows users to visually confirm digital data, and includes smartphones and head-mounted displays.

[0739] "Feedback" refers to the evaluations and comments that users provide regarding suggested outfits, and this information is useful for optimizing the system.

[0740] A "virtual space" is a digital space created by a computer, an environment that users can experience interactively.

[0741] "Outfit suggestions" refer to clothing combinations selected based on the user's individual conditions, and are generated taking into account their emotional state and environmental factors.

[0742] This invention is a system for providing users with fashion coordination based on their emotional state. In this system, the user's terminal manages the digital information of the clothing and analyzes the user's emotional state using an emotion engine.

[0743] Users take photos of their clothing using visual receiving devices such as smartphones or smart glasses, and digitize the information. Identification information is added to the clothing and saved as digital data. The user's device has a camera and microphone, which are used by an emotion engine to analyze the user's facial expressions and voice tone, employing TensorFlow and OpenCV to determine their current emotional state.

[0744] The server uses a generating AI model to calculate and provide users with appropriate clothing combinations based on digitized clothing information, weather information, and emotional state data. These outfit suggestions are visualized in a virtual space using Unity or Unreal Engine, and users can view them using visual receiving devices. User feedback on the suggested outfits is also collected to continuously optimize the system's suggestion algorithm.

[0745] As a concrete example, if a user wears smart glasses and asks aloud, "What should I wear today?", the system of this invention analyzes the input voice data and facial expression data to determine that the user is in a relaxed mood. Based on this result, a generative AI model is used to suggest an outfit with bright colors and soft materials. Through this experience, the user can receive more personalized fashion advice.

[0746] Example of a prompt:

[0747] "Please suggest an outfit that perfectly suits my mood today. I want to feel relaxed."

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The device takes a picture of the user's clothing and digitizes the image. The device uses a camera to acquire a picture of the clothing and assigns identification information to it. The input is an image of the actual clothing, and the output is digitized clothing information stored in a digital database.

[0751] Step 2:

[0752] The server receives digitized clothing information sent from the terminal. The server stores the information in a database and manages clothing navigation and history as needed. The input is digitized clothing information, and the output is an update to a detailed clothing catalog.

[0753] Step 3:

[0754] The user accesses the system and communicates their current mood through voice and facial expressions. The terminal collects the user's voice and facial expression data and sends it to the emotion engine. The input is the user's current voice tone and facial expressions, and the output is the user's emotional state obtained by the emotion engine.

[0755] Step 4:

[0756] The emotion engine processes the user's voice and facial expression data to identify their current emotional state. Text analysis and image recognition algorithms are used to determine the emotion. The input is voice and facial expression data, and the output is the user's identified emotional state.

[0757] Step 5:

[0758] The server matches clothing information, weather information, and emotional state to generate the optimal clothing combination. A generative AI model is used to calculate outfits adapted to emotions and external factors. The input is digitized clothing information, weather information, and emotional information, and the output is the proposed clothing combination.

[0759] Step 6:

[0760] The terminal displays outfit suggestions received from the server to the user. It provides a visual virtual try-on experience, giving the user the opportunity to review and select from the suggestions. The input is the data of the suggested outfits, and the output is the outfit information visualized for the user.

[0761] Step 7:

[0762] The user provides feedback on the suggested outfit. The device collects this feedback and sends it to the server. The input is the user's feedback information, and the output is algorithmic data that is updated for optimization.

[0763] Step 8:

[0764] The server analyzes the received feedback and optimizes the proposed algorithm. As a result, the accuracy of subsequent proposals improves. The input is user feedback, and the output is the updated proposed algorithm.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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."

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] A means of digitizing users' clothing information,

[0789] A means of suggesting the optimal clothing combination based on digitized clothing information and weather information,

[0790] A means for displaying the proposed clothing combination to the user,

[0791] A means for obtaining user feedback and optimizing the proposed means,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising a function to attach tag information to clothing owned by a user and to provide the tag information to the means for digitizing the information.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising means for visually simulating proposed clothing combinations.

[0797] "Example 1"

[0798] (Claim 1)

[0799] A means of digitizing users' clothing information and storing it in an information technology infrastructure,

[0800] A method for calculating the optimal clothing combination using a generative artificial intelligence model, based on digitized clothing information and acquired environmental information.

[0801] A means for presenting the calculated clothing combination to the user via display technology,

[0802] A means for collecting user evaluation information and adjusting the generated artificial intelligence model,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising a function to add identification information to clothing owned by a user and to provide the identification information to the digitization means.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for predicting calculated clothing combinations using visual technology.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of quantifying user clothing data,

[0811] A means of recommending appropriate clothing combinations based on quantified clothing data and weather data,

[0812] A means of visually presenting the aforementioned recommended outfit combinations to the user,

[0813] A means of having a virtual model try on clothes via a communication terminal and confirming the outfit,

[0814] A means for collecting user feedback and adaptively adjusting the aforementioned recommendation method,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising a function to assign identification information to clothing owned by a user and to provide the identification information to the means for digitizing it.

[0818] (Claim 3)

[0819] The system according to claim 1, further comprising means for three-dimensionally reproducing the proposed combination of outfits.

[0820] "Example 2 of combining an emotion engine"

[0821] (Claim 1)

[0822] A means of digitizing users' clothing information,

[0823] A means of suggesting the optimal clothing combination based on digitized clothing information, weather information, and user sentiment data,

[0824] A means for displaying the proposed clothing combination to the user,

[0825] A means for obtaining user feedback and optimizing the proposed means,

[0826] A means to analyze user emotions in real time and dynamically update outfit suggestions,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising a function to attach tag information to clothing owned by a user and to provide the tag information to the means for digitizing the information.

[0830] (Claim 3)

[0831] The system according to claim 1, further comprising means for visually simulating proposed clothing combinations.

[0832] "Application example 2 when combining with an emotional engine"

[0833] (Claim 1)

[0834] A means of digitizing users' clothing information,

[0835] A means of suggesting the optimal clothing combination based on digitized clothing information, weather information, and emotional state, using an emotion engine that analyzes the user's emotional state.

[0836] A means for displaying proposed clothing combinations to the user via a visual receiving device,

[0837] A means of obtaining user feedback and continuously optimizing the proposed methods,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising a function for supplying identification information attached to clothing owned by a user to the means for digitizing the above.

[0841] (Claim 3)

[0842] The system according to claim 1, further comprising means for visually simulating proposed clothing combinations in a virtual space. [Explanation of symbols]

[0843] 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 means of digitizing users' clothing information, A means of suggesting the optimal clothing combination based on digitized clothing information and weather information, A means for displaying the proposed clothing combination to the user, A means for obtaining user feedback and optimizing the proposed means, A system that includes this.

2. The system according to claim 1, further comprising a function to attach tag information to clothing owned by a user and to provide the tag information to the means for digitizing the information.

3. The system according to claim 1, further comprising means for visually simulating proposed clothing combinations.

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