system

Abstract

We provide the system. [Solution] Means of obtaining users' personal information, A means of analyzing user preferences based on acquired personal information, A means of suggesting individual accessory combinations to the user based on the analyzed information, Means for providing commercial information on decorative items that is lacking in the aforementioned proposal, A means of learning by collecting user feedback to improve the accuracy of suggestions, 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 recent years, the options of fashion items have increased, and it has become difficult for users to select ornaments that suit their lifestyle and preferences. In particular, users who want to enjoy fashion in different daily scenes have the problem of spending a lot of time and effort in selecting appropriate coordination and necessary items. Therefore, there is a need for a system that can efficiently propose optimal fashion based on the individual preferences and lifestyles of users.

Means for Solving the Problems

[0005] This invention solves this problem by providing a system that acquires users' personal information and analyzes their preferences based on that information. This system can suggest combinations of accessories suitable for the user's lifestyle based on the analyzed information, and can provide commercial information for any missing items. Furthermore, it learns from user feedback to improve the accuracy of its suggestions and supports the selection of optimal fashion items. When providing commercial information, the system takes the user's location into consideration, enabling a highly convenient selection process.

[0006] "Personal information" refers to data about users, such as their name, age, gender, fashion preferences, and lifestyle.

[0007] "Preferences" refer to the fashion-related tendencies such as colors, styles, and designs that users like.

[0008] "Analysis" refers to the process of finding specific regularities or patterns based on acquired data.

[0009] "Ornaments" refers to items that a user wears, such as clothing, accessories, and shoes, that make up their appearance.

[0010] "Combination" refers to integrating multiple decorative items to create a single fashion style.

[0011] "Commercial information" refers to information related to purchasing activities, such as the price, supplier, and stock status of decorative items.

[0012] "Feedback" refers to the opinions and evaluations that users provide regarding the suggested combinations.

[0013] "Learning" refers to the process by which a system improves the accuracy of its suggestions based on past feedback and data.

[0014] "Location information" refers to data about the user's current location and is used to provide commercial information and select the most suitable decorative items. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system according to the present invention is implemented in a way that efficiently acquires users' personal information and analyzes their fashion preferences to provide each user with the most suitable accessory coordination. A specific embodiment of this system is described below.

[0037] The system first obtains information about the user's name, age, gender, body type, fashion style preferences, and lifestyle during initial registration. The user inputs this information via a terminal, and a server on the digital platform receives this information.

[0038] The server uses machine learning algorithms to analyze the user's preferences based on the personal information they enter. This analysis detects preferences for specific colors and styles, as well as frequent behavioral patterns in their lifestyle. The server then compares this information with a trend database to generate several accessory combinations suitable for the user.

[0039] The generated outfits are displayed on the user's device and can be visually confirmed through the user interface. At the same time, the system provides commercial information from nearby online and physical stores, taking into account the user's current location, and includes features to facilitate purchasing. For example, if a user desires a "casual weekend style," a combination of a checked shirt and denim pants will be displayed along with inventory information from local stores.

[0040] Furthermore, users can submit feedback on the suggestions as they continue to use the system. The server analyzes this feedback and uses it to improve the suggestion algorithm. This allows for a better fit to the user's preferences.

[0041] Thus, the system according to the present invention can continuously provide fashion suggestions that match the individual needs of users, thereby realizing a personalized service.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] When a user first accesses the service, they create an account using their device and enter basic personal information such as their name, age, gender, and body type. This information is then sent to the server.

[0045] Step 2:

[0046] Through the device, users answer questions about their fashion preferences and lifestyle. For example, they select their favorite colors, places they frequent, and how often they make purchases. This information is also sent to the server.

[0047] Step 3:

[0048] The server analyzes the acquired personal information and preference data and stores it in a database as user profiles. Machine learning algorithms are then used to perform statistical analysis and identify user trends.

[0049] Step 4:

[0050] The server compares the analysis results with a trend database to generate accessory combinations that suit the user's lifestyle. In doing so, it refers to similar user profiles to select the optimal coordination.

[0051] Step 5:

[0052] The terminal displays suggested combinations of decorative items received from the server to the user. These suggestions include a visual format, designed for easy user review.

[0053] Step 6:

[0054] The server uses the user's current location information to provide commercial information about nearby stores and online shops where the suggested decorative items can be purchased. This includes information on price and availability.

[0055] Step 7:

[0056] Users input feedback on the presented suggestions into their device. This includes comments about styles they don't like or information that is missing.

[0057] Step 8:

[0058] The device collects user feedback and sends it to the server. The server analyzes this feedback and updates its machine learning model to improve the accuracy of future suggestions.

[0059] (Example 1)

[0060] 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."

[0061] In today's increasingly diverse consumer landscape, there is a growing need to efficiently provide fashion suggestions tailored to individual user preferences. Traditional, fixed suggestion systems struggle to adequately reflect users' detailed preferences and location information, resulting in a lack of highly satisfying personalized recommendations. Furthermore, the process of improving the accuracy of suggestions through user feedback is not functioning effectively, limiting the ability to provide services that truly meet user needs.

[0062] 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.

[0063] In this invention, the server includes means for acquiring attribute data, means for analyzing preferences using generative AI technology, and means for creating suitable accessory placement plans. This makes it possible to analyze the user's detailed preferences and provide personalized fashion suggestions while offering location-based commercial information. Furthermore, the learning function using the generative AI model allows for continuous improvement of the accuracy of suggestions by utilizing user feedback.

[0064] "Attribute data" refers to personal information such as a user's name, age, gender, and body type, and is fundamental data used for fashion recommendations.

[0065] "Generative AI technology" refers to a method that uses artificial intelligence to analyze users' preferences and tendencies and suggest the most suitable decorative items.

[0066] "Preference" refers to a user's personal fashion preferences, such as a particular color, style, or lifestyle.

[0067] "Arrangement suggestions for accessories" refers to suggestions that present combinations of clothing, accessories, and other items based on the analyzed user preferences.

[0068] A "user interface" refers to the means of interaction that allows users to visually confirm and manipulate suggestions from a system.

[0069] "Location information" refers to data that indicates the user's current geographical location and is used to provide commercial information.

[0070] "Commercial information" refers to marketing data related to products, such as inventory status of decorative items and information on stores where they can be purchased.

[0071] A "generative AI model" refers to the structure of an algorithm that continuously learns based on user feedback, aiming to improve the accuracy of its suggestions.

[0072] This invention is a system that provides fashion suggestions tailored to the individual preferences of users. The system is started when the user inputs attribute data via a terminal. This attribute data includes information about the user's name, age, gender, body type, fashion style preferences, and lifestyle. The terminal transmits this data to a server via a secure communication protocol. The server stores the received information in a database and analyzes this data using generative AI technology.

[0073] Based on the analysis results, the server identifies the user's preferences and generates suitable accessory placement suggestions. The generating AI model combines the latest fashion trend data with the user's individual preferences to optimize the suggestions. This enables fashion suggestions that respond to specific prompts such as "casual weekend style."

[0074] The generated decorative item placement suggestions are sent to the terminal and can be visually confirmed through the user interface. The system is designed so that users can view the suggested styles and proceed directly to the purchase process. The server also takes the user's location into consideration and provides relevant commercial information, such as the inventory status of nearby stores.

[0075] Furthermore, users can send feedback on the suggestions from their devices to the server. The server incorporates this feedback into the generating AI model to improve the accuracy of the suggestions. For example, based on feedback such as "This suggestion doesn't suit me," the next suggestion will be optimized to present options that are more in line with the user's preferences.

[0076] In this way, the present invention makes it possible to provide highly personalized fashion suggestions, enabling users to efficiently find and purchase the style they desire. This system offers a new fashion selection experience through suggestions that accurately reflect the user's lifestyle and preferences.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user uses a terminal to input attribute data such as name, age, gender, body type, fashion style preferences, and lifestyle. This input is sent to the server in digital format. The input data is formatted in JSON format and ready to be passed to the server. Once the server receives it, the next processing step begins.

[0080] Step 2:

[0081] The server stores the received attribute data in a structured database and supplies it to the generative AI model. The generative AI model analyzes this data to identify the user's preferences. In this process, clustering and classification algorithms are used to extract patterns of user preferences. As output, a list of tags for colors and styles preferred by the user is generated.

[0082] Step 3:

[0083] Based on the analysis results, the server compares them with an external trend database to generate suitable accessory placement suggestions. The generating AI model combines user-oriented information and trend data to create specific fashion coordinates. From this output, a list of suggested accessories is created.

[0084] Step 4:

[0085] The server sends the generated layout plan to the terminal, which displays it on the user interface. The user can visually confirm the displayed layout and interact with it. The terminal generates prompt messages as multimedia content such as images, text, and links, and displays them to the user.

[0086] Step 5:

[0087] Users can access suggested accessories and purchase items through their device. The server also uses the user's location information to provide relevant store and inventory information to support the purchase. Based on prompts such as "casual weekend style," the user receives suggestions and proceeds to the actual purchase step.

[0088] Step 6:

[0089] Users send feedback on the suggestions from their device to the server. This feedback is used by the generative AI model to learn for future suggestions. This process continuously improves the accuracy of the suggestions, enabling suggestions that are better suited to the user's preferences.

[0090] (Application Example 1)

[0091] 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."

[0092] Modern consumers have diverse tastes and lifestyles, and they seek personalized recommendations for accessories. However, traditional systems can only utilize static data, making it difficult to meet individual needs in real time. Furthermore, there was a lack of technology to optimize the in-store shopping experience. This resulted in consumers spending a lot of time finding suitable products, leading to decreased satisfaction.

[0093] 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.

[0094] In this invention, the server includes a device for acquiring a user's personal information, a device for analyzing the user's preferences based on the acquired personal information, a device for suggesting specific accessory combinations to the user based on the analyzed information, a device for providing commercial information on accessories related to the suggestion, a device for collecting user feedback and learning to improve the accuracy of the suggestions, and a device for recognizing products using a wearable device in a physical store and displaying optimal information to the user. This enables optimal product suggestions that meet consumers' real-time needs and preferences, thereby improving the in-store shopping experience.

[0095] "User personal information" refers to information necessary for providing personalized services, such as the user's name, age, gender, body type, fashion style, and lifestyle.

[0096] A "device for analyzing user preferences based on acquired personal information" is a device used to analyze collected personal information of users and identify their preferences and tendencies.

[0097] A "device for suggesting specific accessory combinations to users based on analyzed information" is a device that selects and provides the most suitable accessory combination to the user based on the results of preference analysis.

[0098] A "device for providing commercial information" is a device used to provide users with sales data and store information related to the proposed decorative items.

[0099] A "learning device" is a device that collects feedback from users and uses that data to execute a machine learning process aimed at improving the accuracy of suggestions.

[0100] A "wearable device" refers to a device that a user wears to detect their movements and location within a physical store and provide visual information.

[0101] A "device for recognizing products and displaying information best suited to the user" is a device that identifies products in a physical store and displays product information that is highly relevant to the user.

[0102] This embodiment provides a system that enhances the in-store shopping experience by allowing users to use smart devices. The system includes a server, a user terminal, and a wearable device installed in the physical store.

[0103] The server collects users' personal information and performs preference analysis based on it. The collected data includes details about name, age, gender, body type, fashion style, and lifestyle. Using this information, a machine learning algorithm analyzes the user's preferences and generates individually optimized accessory combinations. This algorithm also learns from past user feedback to improve the accuracy of its suggestions.

[0104] The user terminal is primarily used as a wearable device, such as smart glasses, and receives information from the server in real time. When the user enters a physical store, the device recognizes the products in the store and visually displays product information suggested to the user. This is to instantly present the most suitable product information for the user in the store based on data from the server, thereby stimulating purchasing intent.

[0105] Furthermore, it provides commercial information usable at physical stores, linked to the user's geographical information. This includes inventory information and special offers, playing a role in supporting the user's purchasing decisions.

[0106] For example, if a user enters a fashion store wearing smart glasses, the glasses' display will show a recommended combination of a checked shirt and denim pants for that location. Based on this information, the user can try on the items and quickly decide whether or not to purchase them.

[0107] In utilizing the generative AI model, the following prompt example is provided: "This user is male, in his 30s, prefers casual fashion, and frequently engages in outdoor activities. He is currently in a men's fashion store in the city center. Recognize the items in the store and suggest the most suitable casual outdoor outfit for this user."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user enters personal information using a terminal. The server receives this information and processes data related to name, age, gender, body type, fashion style, and lifestyle. This registers the user's basic profile information in the database.

[0111] Step 2:

[0112] The server executes machine learning algorithms based on registered personal information to analyze user preferences. It analyzes the entered personal information and uses a generative AI model to extract preferences for specific colors and styles. The analysis results output analytical data containing the characteristics of the style best suited to the user.

[0113] Step 3:

[0114] The server compares the trend database and generates specific accessory combinations based on the analysis results. Calculations are performed comparing the trend database and the analysis data, and a list of accessories to suggest to the user is output.

[0115] Step 4:

[0116] The generated combination of accessories is sent to the user's device (smart glasses). The user can then view product images through the device and decide on their actions within the store. This visually displays suggested product information to the user.

[0117] Step 5:

[0118] The terminal recognizes products in the physical store and compares them with accessory information sent from the server. It processes product images input by the camera device and searches for matches against the accessory list provided by the server. If the recognition data and accessory information match, detailed information is presented to the user.

[0119] Step 6:

[0120] Users make choices regarding trying on and purchasing suggested accessories. Wearable devices are used to examine product information in detail and confirm their purchase intent. This action is sent to the server as feedback and used to improve accuracy in the future.

[0121] Step 7:

[0122] The server accumulates evaluation data based on user behavior and uses it to improve the proposed algorithm. The data obtained as feedback is statistically processed, and the model is updated. This process further improves the fit to individual users.

[0123] 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.

[0124] This invention is implemented in a form that enables more personalized suggestions by combining a system that suggests fashion coordinates based on the user's personal information and preferences with an emotion engine that recognizes the user's emotions.

[0125] This system begins with the user creating an account using a device and entering personal information and fashion preferences. Users can also add information about their daily lifestyle via the device. The server retrieves this information, stores it in a database, and runs machine learning algorithms for preference analysis.

[0126] Furthermore, the emotion engine collects emotional data from the user's voice tone, facial expressions, and text input patterns. The server integrates this emotional data with existing preference analysis results to generate the combination of accessories that best suits the user's current emotional state.

[0127] The suggested outfits in this process are displayed on the user's device, allowing the user to visually confirm them. For example, if the system detects that the user is feeling stressed, a casual style designed to create a relaxed atmosphere will be suggested. In this case, the server also collects the necessary commercial information for the suggestions and provides purchase links for items that match the user's current mood.

[0128] User feedback is sent to the server via the terminal and used for learning to improve the overall system's suggestion accuracy. This feedback is used to evaluate how useful the suggestions were and is stored as data to further improve the accuracy of emotional responses. In this way, the present invention can realize detailed fashion suggestions that respond to the user's emotional state and improve the user experience.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users create an account using their device and enter personal information, fashion preferences, and lifestyle information. This is how initial data is collected.

[0132] Step 2:

[0133] The terminal sends the collected information to the server and stores it in a database. This information forms the basis for subsequent analysis.

[0134] Step 3:

[0135] The server uses machine learning algorithms to analyze data on user preferences and identify the user's fashion tendencies.

[0136] Step 4:

[0137] Users can manually input their current emotional state using the emotion input interface provided on the device, or the device can automatically recognize emotions from the user's voice and facial expressions. This data is processed by the emotion engine.

[0138] Step 5:

[0139] The server integrates emotional data obtained from the emotion engine with existing preference data to generate a combination of accessories suitable for the current emotional state.

[0140] Step 6:

[0141] The device visually presents the user with suggested accessory combinations. These suggestions include commercial information relevant to the user's mood, which the user can then review.

[0142] Step 7:

[0143] The server collects commercial information corresponding to the suggested decorative items, taking into account the user's location, and provides the optimal purchase option.

[0144] Step 8:

[0145] Users input feedback on the suggested outfits into their terminals and send it to the server. This feedback allows the system to more accurately adjust future suggestions.

[0146] Step 9:

[0147] The server analyzes feedback data and learns to improve the accuracy of its suggestion algorithms and emotion engine. This enhances the individual user experience.

[0148] (Example 2)

[0149] 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".

[0150] Conventional fashion recommendation systems rely solely on the user's individual characteristics and preferences, making it difficult to provide personalized recommendations that adequately consider the user's emotional state. As a result, the selection of accessories that are suitable for the user's current emotions and situation is often inadequate, leading to decreased user satisfaction.

[0151] 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.

[0152] In this invention, the server includes means for acquiring the user's personal characteristics, means for analyzing the user's preferences based on the acquired personal characteristics, and means for sensing the user's emotional state and generating accessory suggestions that take that state into consideration. This makes it possible to provide fashion suggestions optimized for the user's current emotions.

[0153] "Personal characteristics" refer to distinctive information unique to the user, such as gender, age, body type, hobbies, and occupation.

[0154] "Preferences" refer to the user's tastes in fashion, such as preferred colors, styles, and brands.

[0155] "Emotional state" refers to the psychological condition that the user is currently experiencing, such as stress, relaxation, or excitement.

[0156] "Ornaments" refer to items used to adorn an individual's appearance, such as clothing, shoes, and accessories.

[0157] "Commercial information" refers to market information related to decorative items, such as sales data, prices, inventory status, and purchase links.

[0158] "Suggestion accuracy" refers to an indicator that measures the degree to which suggested decorative items meet the user's expectations and desires, as well as their level of satisfaction.

[0159] This invention is a system that comprehensively analyzes a user's personal characteristics, preferences, and emotional state in order to provide personalized fashion suggestions to the user. The system is realized through the cooperation of a server and a terminal.

[0160] Users create an account using the device and input information about their personal characteristics and preferences. The device is equipped with voice input and a camera, which can be used to collect the user's emotional state in real time. A standard touch input interface can be used for this process.

[0161] The server stores information obtained from users in a database and performs analysis. Open-source machine learning libraries are often used as machine learning algorithms. For example, TENSORFLOW® is used to model user preferences and improve the accuracy of suggestions. General sentiment recognition APIs are utilized for analyzing sentiment data.

[0162] In implementing this system, the server generates accessory suggestions that combine the user's emotions and preferences based on analyzed data. A generative AI model is used to provide suggestions that take the latest fashion trends into account. These suggestions also include purchase links for items that match the user's emotions, making it easy for users to buy products.

[0163] As a concrete example, when a user is feeling stressed, their emotional state is sent from their device to the server, and a relaxing fashion coordinate is suggested. For instance, a prompt might be, "How can we generate appropriate fashion suggestions to provide a relaxing atmosphere when the user is feeling stressed?"

[0164] In this way, highly accurate suggestions based on the user's emotions and preferences are made through the system, improving the user experience.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users create accounts using their devices. During this process, users input personal characteristics and preferences, and the device sends this data to the server. The server stores this data in a database and uses it as foundational information to generate the user's profile. This profile is then used to improve the accuracy of future recommendations.

[0168] Step 2:

[0169] The server analyzes the user's personal characteristics and preferences stored in the database. This analysis utilizes machine learning algorithms to extract preference patterns and trend data. Using a generative AI model, it identifies the user's preferred fashion trends and obtains analysis results useful for future suggestions. The analysis results are then stored back in the database.

[0170] Step 3:

[0171] The device uses its built-in camera and microphone to capture the user's emotional state in real time. This includes facial expression data and changes in voice tone. The collected emotional data is sent from the device to a server and used as input data. On the server, a common emotion recognition API is used to analyze the emotional data and identify the user's emotional state.

[0172] Step 4:

[0173] The server integrates previously analyzed preference information and emotional state data to generate optimal fashion suggestions. This process utilizes a generative AI model to provide personalized suggestions that combine the user's emotions and preference data. For example, if the system determines the user is stressed, it will generate suggestions that promote relaxation. The output includes suggested outfits and commercial information.

[0174] Step 5:

[0175] The device visually displays fashion suggestions received from the server to the user. The displayed suggestions also include purchase links for related accessories, allowing users to easily buy items they are interested in. Users review the suggestions and provide feedback on their satisfaction level and areas for improvement. This feedback is sent to the server via the device and used as training data to improve the accuracy of future suggestions.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In fashion coordination suggestions, it is difficult to provide more personalized suggestions based on the user's personal preferences and emotional state, and there is a need for a system that accurately supports users in selecting the clothing they need at that moment. Furthermore, there is a challenge in that it is desirable to improve user satisfaction through product suggestions that respond to emotions.

[0179] 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.

[0180] In this invention, the server includes means for acquiring the user's personal information, means for analyzing the user's preferences based on the acquired personal information, means for suggesting individual clothing combinations to the user based on the analyzed information and emotional state, means for providing commercial information on clothing missing from the suggestions, means for emotional recognition to collect the user's emotional data and reflect it in the suggestions, and means for collecting evaluations from the user and learning to improve the accuracy of the suggestions. This enables highly customized fashion suggestions that reflect the user's preferences and real-time emotional state.

[0181] "Personal information" refers to basic information used to identify a user, such as their name, age, gender, address, and contact information.

[0182] "Preferences" refer to information that indicates a user's specific likes and interests, including information about fashion styles and color palette choices.

[0183] A "clothing combination" is a coordinated outfit formed by combining multiple pieces of clothing and accessories.

[0184] "Commercial information" refers to promotional data related to products, such as price, stock availability, and purchase links.

[0185] "Emotion recognition" is the process of identifying an emotion from a user's voice tone, facial expressions, and text input patterns.

[0186] "Suggestion accuracy" refers to the accuracy of a system's ability to provide appropriate fashion suggestions that match the user's needs and preferences.

[0187] This invention provides a system in which users receive fashion coordination suggestions in a virtual store using wearable devices such as smart glasses or head-mounted displays. The server implements this system using multiple means.

[0188] First, basic profile data entered by the user is collected using personal information acquisition methods. Then, the collected data is analyzed using preference analysis methods to extract information about the user's preferred style, colors, and brands.

[0189] The emotion recognition system monitors the user's voice tone, facial expressions, and text input in real time to analyze their emotional state. Emotion recognition software such as Microsoft® Azure® Cognitive Services is used for emotion analysis.

[0190] Next, the suggestion engine generates the most suitable clothing combinations for the user based on the aforementioned preference and emotion data. These outfit suggestions are then visually displayed on the wearable device using AR rendering frameworks such as Unity or ARKit.

[0191] The commercial information provision system provides detailed information and purchase links for the suggested clothing. This makes it easy for users to purchase the suggested clothing.

[0192] Furthermore, feedback from user terminals is sent back to the server and analyzed by continuous machine learning algorithms to improve the accuracy of suggestions.

[0193] For example, if a user browsing new suits in a virtual store is detected to be nervous before a presentation, the system can offer fashion items that convey a professional yet relaxed impression and play relaxing audio guidance.

[0194] Examples of prompt statements to input into the generative AI model are as follows:

[0195] "The system analyzes voice and facial expression data to suggest fashion items that suit a state of tension, and plays relaxing soundtracks to help calm the mind."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server retrieves the user's personal information from the terminal. The input is the profile data provided by the user, and the output is storing this data in a database. Personal information includes name, age, gender, fashion preferences, etc.

[0199] Step 2:

[0200] The server analyzes the acquired personal information using a preference analysis algorithm. The input is personal information, and the output is the analyzed user's preferences and style information. Specifically, it uses a machine learning model to analyze past preference data and extract the user's preferences.

[0201] Step 3:

[0202] The device transmits the user's voice, facial expressions, and text input to an emotion recognition module in real time. The input is this real-time data, and the output is the result of the analysis of the user's emotional state. Specifically, the emotion recognition software uses Microsoft Azure's Cognitive Services to analyze emotions from voice and facial expressions.

[0203] Step 4:

[0204] The server generates appropriate clothing combinations using a suggestion engine based on the analyzed user preference information and emotional state. The input is preference information and emotional data, and the output is a list of selected fashion items. Prompt sentences are used as input to the generating AI model to optimize the generated outfits.

[0205] Step 5:

[0206] The server collects commercial information and sends detailed information and purchase links for suggested clothing items to the terminal. The input is a list of suggested fashion items, and the output is commercial information that the user can purchase. Specifically, it retrieves the latest prices and inventory information from e-commerce platforms.

[0207] Step 6:

[0208] The user reviews fashion suggestions displayed on their device and provides feedback on the selected items. The input is the user's feedback, and the output is the feedback data sent to the server. Specifically, the feedback is collected in the form of a simple questionnaire and sent to the server.

[0209] Step 7:

[0210] The server analyzes user feedback and learns to improve the accuracy of its suggestions using machine learning algorithms. The input is user feedback data, and the output is an improved preference analysis model. As part of data processing, feedback is added to the dataset and used as new training data.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] The system according to the present invention is implemented in a way that efficiently acquires users' personal information and analyzes their fashion preferences to provide each user with the most suitable accessory coordination. A specific embodiment of this system is described below.

[0228] The system first obtains information about the user's name, age, gender, body type, fashion style preferences, and lifestyle during initial registration. The user inputs this information via a terminal, and a server on the digital platform receives this information.

[0229] The server uses machine learning algorithms to analyze the user's preferences based on the personal information they enter. This analysis detects preferences for specific colors and styles, as well as frequent behavioral patterns in their lifestyle. The server then compares this information with a trend database to generate multiple accessory combinations suitable for the user.

[0230] The generated outfits are displayed on the user's device and can be visually confirmed through the user interface. At the same time, the system provides commercial information from nearby online and physical stores, taking into account the user's current location, and includes features to facilitate purchasing. For example, if a user desires a "casual weekend style," a combination of a checked shirt and denim pants will be displayed along with inventory information from local stores.

[0231] Furthermore, users can submit feedback on the suggestions as they continue to use the system. The server analyzes this feedback and uses it to improve the suggestion algorithm. This allows for a better fit to the user's preferences.

[0232] Thus, the system according to the present invention can continuously provide fashion suggestions that match the individual needs of users, thereby realizing a personalized service.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] When a user first accesses the service, they create an account using their device and enter basic personal information such as their name, age, gender, and body type. This information is then sent to the server.

[0236] Step 2:

[0237] Through the device, users answer questions about their fashion preferences and lifestyle. For example, they select their favorite colors, places they frequent, and how often they make purchases. This information is also sent to the server.

[0238] Step 3:

[0239] The server analyzes the acquired personal information and preference data and stores it in a database as user profiles. Machine learning algorithms are then used to perform statistical analysis and identify user trends.

[0240] Step 4:

[0241] The server compares the analysis results with a trend database to generate accessory combinations that suit the user's lifestyle. In doing so, it refers to similar user profiles to select the optimal coordination.

[0242] Step 5:

[0243] The terminal displays suggested combinations of decorative items received from the server to the user. These suggestions include a visual format, designed for easy user review.

[0244] Step 6:

[0245] The server uses the user's current location information to provide commercial information about nearby stores and online shops where the suggested decorative items can be purchased. This includes information on price and availability.

[0246] Step 7:

[0247] Users input feedback on the presented suggestions into their device. This includes comments about styles they don't like or information that is missing.

[0248] Step 8:

[0249] The device collects user feedback and sends it to the server. The server analyzes this feedback and updates its machine learning model to improve the accuracy of future suggestions.

[0250] (Example 1)

[0251] 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 as the "terminal".

[0252] In today's increasingly diverse consumer landscape, there is a growing need to efficiently provide fashion suggestions tailored to individual user preferences. Traditional, fixed suggestion systems struggle to adequately reflect users' detailed preferences and location information, resulting in a lack of highly satisfying personalized recommendations. Furthermore, the process of improving the accuracy of suggestions through user feedback is not functioning effectively, limiting the ability to provide services that truly meet user needs.

[0253] 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.

[0254] In this invention, the server includes means for acquiring attribute data, means for analyzing preferences using generative AI technology, and means for creating suitable accessory placement plans. This makes it possible to analyze the user's detailed preferences and provide personalized fashion suggestions while offering location-based commercial information. Furthermore, the learning function using the generative AI model allows for continuous improvement of the accuracy of suggestions by utilizing user feedback.

[0255] "Attribute data" refers to personal information such as a user's name, age, gender, and body type, and is fundamental data used for fashion recommendations.

[0256] "Generative AI technology" refers to a method that uses artificial intelligence to analyze users' preferences and tendencies and suggest the most suitable decorative items.

[0257] "Preference" refers to a user's personal fashion preferences, such as a particular color, style, or lifestyle.

[0258] "Arrangement suggestions for accessories" refers to suggestions that present combinations of clothing, accessories, and other items based on the analyzed user preferences.

[0259] A "user interface" refers to the means of interaction that allows users to visually confirm and manipulate suggestions from a system.

[0260] "Location information" refers to data that indicates the user's current geographical location and is used to provide commercial information.

[0261] "Commercial information" refers to marketing data related to products, such as inventory status of decorative items and information on stores where they can be purchased.

[0262] A "generative AI model" refers to the structure of an algorithm that continuously learns based on user feedback, aiming to improve the accuracy of its suggestions.

[0263] This invention is a system that provides fashion suggestions tailored to the individual preferences of users. The system is started when the user inputs attribute data via a terminal. This attribute data includes information about the user's name, age, gender, body type, fashion style preferences, and lifestyle. The terminal transmits this data to a server via a secure communication protocol. The server stores the received information in a database and analyzes this data using generative AI technology.

[0264] Based on the analysis results, the server identifies the user's preferences and generates suitable accessory placement suggestions. The generating AI model combines the latest fashion trend data with the user's individual preferences to optimize the suggestions. This enables fashion suggestions that respond to specific prompts such as "casual weekend style."

[0265] The generated decorative item placement suggestions are sent to the terminal and can be visually confirmed through the user interface. The system is designed so that users can view the suggested styles and proceed directly to the purchase process. The server also takes the user's location into consideration and provides relevant commercial information, such as the inventory status of nearby stores.

[0266] Furthermore, users can send feedback on the suggestions from their devices to the server. The server incorporates this feedback into the generating AI model to improve the accuracy of the suggestions. For example, based on feedback such as "This suggestion doesn't suit me," the next suggestion will be optimized to present options that are more in line with the user's preferences.

[0267] In this way, the present invention makes it possible to provide highly personalized fashion suggestions, enabling users to efficiently find and purchase the style they desire. This system offers a new fashion selection experience through suggestions that accurately reflect the user's lifestyle and preferences.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user uses a terminal to input attribute data such as name, age, gender, body type, fashion style preferences, and lifestyle. This input is sent to the server in digital format. The input data is formatted in JSON format and ready to be passed to the server. Once the server receives it, the next processing step begins.

[0271] Step 2:

[0272] The server stores the received attribute data in a structured database and supplies it to the generative AI model. The generative AI model analyzes this data to identify the user's preferences. In this process, clustering and classification algorithms are used to extract patterns of user preferences. As output, a list of tags for colors and styles preferred by the user is generated.

[0273] Step 3:

[0274] Based on the analysis results, the server compares them with an external trend database to generate suitable accessory placement suggestions. The generating AI model combines user-oriented information and trend data to create specific fashion coordinates. From this output, a list of suggested accessories is created.

[0275] Step 4:

[0276] The server sends the generated layout plan to the terminal, which displays it on the user interface. The user can visually confirm the displayed layout and interact with it. The terminal generates prompt messages as multimedia content such as images, text, and links, and displays them to the user.

[0277] Step 5:

[0278] Users can access suggested accessories and purchase items through their device. The server also uses the user's location information to provide relevant store and inventory information to support the purchase. Based on prompts such as "casual weekend style," the user receives suggestions and proceeds to the actual purchase step.

[0279] Step 6:

[0280] The user sends feedback on the proposed content from the terminal to the server. This feedback is used for the generative AI model to perform a learning process for the next proposal. Through this process, the accuracy of the proposed content is continuously improved, enabling proposals that are more in line with the user's preferences.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] Modern consumers have diverse preferences and lifestyles and seek individually optimized decoration proposals. However, conventional systems can only utilize static data and have difficulty meeting individual needs in real time. Also, there was a lack of technology to optimize the purchase experience in physical stores. As a result, consumers spend a lot of time finding products that suit them, and there is an issue of reduced satisfaction.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes a device for acquiring the user's personal information, a device for analyzing the user's preferences based on the acquired personal information, a device for proposing a specific combination of decorations to the user based on the analyzed information, a device for providing commercial information of the decorations related to the proposal, a device for learning by collecting evaluations from the user to improve the accuracy of the proposal, and a device for recognizing products by a wearable device in a physical store and displaying optimal information to the user. This enables optimal product proposals according to the real-time needs and preferences of consumers and improves the purchase experience in physical stores.

[0286] "Personal information of users" refers to information such as the name, age, gender, body shape, fashion style, and lifestyle of users, which is necessary for providing personalized services.

[0287] "Device for analyzing users' preferences based on the obtained personal information" refers to a device that analyzes the collected personal information of users and is used to identify the preferences and tendencies of users.

[0288] "Device for proposing a specific combination of ornaments to users based on the analyzed information" refers to a device that selects and provides the optimal combination of ornaments for users based on the results of preference analysis.

[0289] "Device for providing commercial information" refers to a device that is used to provide users with sales data and store information related to the proposed ornaments.

[0290] "Device for performing learning" refers to a device that collects feedback from users and executes a machine learning process aimed at improving the accuracy of proposals based on that data.

[0291] "Wearable device" refers to a device that, when worn by a user, detects the movements and positions of the user within a physical store and provides visual information.

[0292] "Device for recognizing products and displaying optimal information to users" refers to a device that identifies products within a physical store and displays product information highly relevant to users.

[0293] In this embodiment, a system is provided that improves the purchasing experience in a physical store by a user using a smart device. This system includes a server, a user terminal, and a wearable device installed within the physical store.

[0294] The server collects users' personal information and performs preference analysis based on it. The collected data includes details about name, age, gender, body type, fashion style, and lifestyle. Using this information, a machine learning algorithm analyzes the user's preferences and generates individually optimized accessory combinations. This algorithm also learns from past user feedback to improve the accuracy of its suggestions.

[0295] The user terminal is primarily used as a wearable device, such as smart glasses, and receives information from the server in real time. When the user enters a physical store, the device recognizes the products in the store and visually displays product information suggested to the user. This is to instantly present the most suitable product information for the user in the store based on data from the server, thereby stimulating purchasing intent.

[0296] Furthermore, it provides commercial information usable at physical stores, linked to the user's geographical information. This includes inventory information and special offers, playing a role in supporting the user's purchasing decisions.

[0297] For example, if a user enters a fashion store wearing smart glasses, the glasses' display will show a recommended combination of a checked shirt and denim pants for that location. Based on this information, the user can try on the items and quickly decide whether or not to purchase them.

[0298] In utilizing the generative AI model, the following prompt example is provided: "This user is male, in his 30s, prefers casual fashion, and frequently engages in outdoor activities. He is currently in a men's fashion store in the city center. Recognize the items in the store and suggest the most suitable casual outdoor outfit for this user."

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The user inputs personal information using a terminal. The server receives the information and processes data regarding name, age, gender, body type, fashion style, and lifestyle. As a result, the user's basic profile information is registered in the database.

[0302] Step 2:

[0303] The server executes a machine learning algorithm based on the registered personal information to analyze the user's preferences. It analyzes the input personal information and extracts preferences for specific colors and styles using a generated AI model. As an analysis result, analysis data including the characteristics of the style optimal for the user is output.

[0304] Step 3:

[0305] The server checks the trend database and generates a specific combination of ornaments based on the analysis result. An operation is performed to compare the trend database and the analysis data, and a list of ornaments to be proposed to the user is output.

[0306] Step 4:

[0307] The generated combination of ornaments is sent to the user's terminal (smart glasses). The user can confirm the product image through the terminal and determine their actions in the store. As a result, the proposed product information is visually displayed to the user.

[0308] Step 5:

[0309] The terminal recognizes products in the physical store and collates them with the ornament information sent from the server. It processes the input of the product image by the camera device and searches for a match with the ornament list provided by the server. When the recognition data and the ornament information match, detailed information is presented to the user.

[0310] Step 6:

[0311] Users make choices regarding trying on and purchasing suggested accessories. Wearable devices are used to examine product information in detail and confirm their purchase intent. This action is sent to the server as feedback and used to improve accuracy in the future.

[0312] Step 7:

[0313] The server accumulates evaluation data based on user behavior and uses it to improve the proposed algorithm. The data obtained as feedback is statistically processed, and the model is updated. This process further improves the fit to individual users.

[0314] 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.

[0315] This invention is implemented in a form that enables more personalized suggestions by combining a system that suggests fashion coordinates based on the user's personal information and preferences with an emotion engine that recognizes the user's emotions.

[0316] This system begins with the user creating an account using a device and entering personal information and fashion preferences. Users can also add information about their daily lifestyle via the device. The server retrieves this information, stores it in a database, and runs machine learning algorithms for preference analysis.

[0317] Furthermore, the emotion engine collects emotional data from the user's voice tone, facial expressions, and text input patterns. The server integrates this emotional data with existing preference analysis results to generate the combination of accessories that best suits the user's current emotional state.

[0318] The suggested outfits in this process are displayed on the user's device, allowing the user to visually confirm them. For example, if the system detects that the user is feeling stressed, a casual style designed to create a relaxed atmosphere will be suggested. In this case, the server also collects the necessary commercial information for the suggestions and provides purchase links for items that match the user's current mood.

[0319] User feedback is sent to the server via the terminal and used for learning to improve the overall system's suggestion accuracy. This feedback is used to evaluate how useful the suggestions were and is stored as data to further improve the accuracy of emotional responses. In this way, the present invention can realize detailed fashion suggestions that respond to the user's emotional state and improve the user experience.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] Users create an account using their device and enter personal information, fashion preferences, and lifestyle information. This is how initial data is collected.

[0323] Step 2:

[0324] The terminal sends the collected information to the server and stores it in a database. This information forms the basis for subsequent analysis.

[0325] Step 3:

[0326] The server uses machine learning algorithms to analyze data on user preferences and identify the user's fashion tendencies.

[0327] Step 4:

[0328] Users can manually input their current emotional state using the emotion input interface provided on the device, or the device can automatically recognize emotions from the user's voice and facial expressions. This data is processed by the emotion engine.

[0329] Step 5:

[0330] The server integrates emotional data obtained from the emotion engine with existing preference data to generate a combination of accessories suitable for the current emotional state.

[0331] Step 6:

[0332] The device visually presents the user with suggested accessory combinations. These suggestions include commercial information relevant to the user's mood, which the user can then review.

[0333] Step 7:

[0334] The server collects commercial information corresponding to the suggested decorative items, taking into account the user's location, and provides the optimal purchase option.

[0335] Step 8:

[0336] Users input feedback on the suggested outfits into their terminals and send it to the server. This feedback allows the system to more accurately adjust future suggestions.

[0337] Step 9:

[0338] The server analyzes feedback data and learns to improve the accuracy of its suggestion algorithms and emotion engine. This enhances the individual user experience.

[0339] (Example 2)

[0340] 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".

[0341] Conventional fashion recommendation systems rely solely on the user's individual characteristics and preferences, making it difficult to provide personalized recommendations that adequately consider the user's emotional state. As a result, the selection of accessories that are suitable for the user's current emotions and situation is often inadequate, leading to decreased user satisfaction.

[0342] 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.

[0343] In this invention, the server includes means for acquiring the user's personal characteristics, means for analyzing the user's preferences based on the acquired personal characteristics, and means for sensing the user's emotional state and generating accessory suggestions that take that state into consideration. This makes it possible to provide fashion suggestions optimized for the user's current emotions.

[0344] "Personal characteristics" refer to distinctive information unique to the user, such as gender, age, body type, hobbies, and occupation.

[0345] "Preferences" refer to the user's tastes in fashion, such as preferred colors, styles, and brands.

[0346] "Emotional state" refers to the psychological condition that the user is currently experiencing, such as stress, relaxation, or excitement.

[0347] "Ornaments" refer to items used to adorn an individual's appearance, such as clothing, shoes, and accessories.

[0348] "Commercial information" refers to market information related to decorative items, such as sales data, prices, inventory status, and purchase links.

[0349] "Suggestion accuracy" refers to an indicator that measures the degree to which suggested decorative items meet the user's expectations and desires, as well as their level of satisfaction.

[0350] This invention is a system that comprehensively analyzes a user's personal characteristics, preferences, and emotional state in order to provide personalized fashion suggestions to the user. The system is realized through the cooperation of a server and a terminal.

[0351] Users create an account using the device and input information about their personal characteristics and preferences. The device is equipped with voice input and a camera, which can be used to collect the user's emotional state in real time. A standard touch input interface can be used for this process.

[0352] The server stores information obtained from users in a database and performs analysis. Open-source machine learning libraries are often used as machine learning algorithms. For example, TensorFlow is used to model user preferences and improve the accuracy of suggestions. General sentiment recognition APIs are utilized for analyzing sentiment data.

[0353] In implementing this system, the server generates accessory suggestions that combine the user's emotions and preferences based on analyzed data. A generative AI model is used to provide suggestions that take the latest fashion trends into account. These suggestions also include purchase links for items that match the user's emotions, making it easy for users to buy products.

[0354] As a concrete example, when a user is feeling stressed, their emotional state is sent from their device to the server, and a relaxing fashion coordinate is suggested. For instance, a prompt might be, "How can we generate appropriate fashion suggestions to provide a relaxing atmosphere when the user is feeling stressed?"

[0355] In this way, highly accurate suggestions based on the user's emotions and preferences are made through the system, improving the user experience.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] Users create accounts using their devices. During this process, users input personal characteristics and preferences, and the device sends this data to the server. The server stores this data in a database and uses it as foundational information to generate the user's profile. This profile is then used to improve the accuracy of future recommendations.

[0359] Step 2:

[0360] The server analyzes the user's personal characteristics and preferences stored in the database. This analysis utilizes machine learning algorithms to extract preference patterns and trend data. Using a generative AI model, it identifies the user's preferred fashion trends and obtains analysis results useful for future suggestions. The analysis results are then stored back in the database.

[0361] Step 3:

[0362] The device uses its built-in camera and microphone to capture the user's emotional state in real time. This includes facial expression data and changes in voice tone. The collected emotional data is sent from the device to a server and used as input data. On the server, a common emotion recognition API is used to analyze the emotional data and identify the user's emotional state.

[0363] Step 4:

[0364] The server integrates previously analyzed preference information and emotional state data to generate optimal fashion suggestions. This process utilizes a generative AI model to provide personalized suggestions that combine the user's emotions and preference data. For example, if the system determines the user is stressed, it will generate suggestions that promote relaxation. The output includes suggested outfits and commercial information.

[0365] Step 5:

[0366] The device visually displays fashion suggestions received from the server to the user. The displayed suggestions also include purchase links for related accessories, allowing users to easily buy items they are interested in. Users review the suggestions and provide feedback on their satisfaction level and areas for improvement. This feedback is sent to the server via the device and used as training data to improve the accuracy of future suggestions.

[0367] (Application Example 2)

[0368] 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."

[0369] In fashion coordination suggestions, it is difficult to provide more personalized suggestions based on the user's personal preferences and emotional state, and there is a need for a system that accurately supports users in selecting the clothing they need at that moment. Furthermore, there is a challenge in that it is desirable to improve user satisfaction through product suggestions that respond to emotions.

[0370] 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.

[0371] In this invention, the server includes means for acquiring the user's personal information, means for analyzing the user's preferences based on the acquired personal information, means for suggesting individual clothing combinations to the user based on the analyzed information and emotional state, means for providing commercial information on clothing missing from the suggestions, means for emotional recognition to collect the user's emotional data and reflect it in the suggestions, and means for collecting evaluations from the user and learning to improve the accuracy of the suggestions. This enables highly customized fashion suggestions that reflect the user's preferences and real-time emotional state.

[0372] "Personal information" refers to basic information used to identify a user, such as their name, age, gender, address, and contact information.

[0373] "Preferences" refer to information that indicates a user's specific likes and interests, including information about fashion styles and color palette choices.

[0374] A "clothing combination" is a coordinated outfit formed by combining multiple pieces of clothing and accessories.

[0375] "Commercial information" refers to promotional data related to products, such as price, stock availability, and purchase links.

[0376] "Emotion recognition" is the process of identifying an emotion from a user's voice tone, facial expressions, and text input patterns.

[0377] "Suggestion accuracy" refers to the accuracy of a system's ability to provide appropriate fashion suggestions that match the user's needs and preferences.

[0378] This invention provides a system in which users receive fashion coordination suggestions in a virtual store using wearable devices such as smart glasses or head-mounted displays. The server implements this system using multiple means.

[0379] First, basic profile data entered by the user is collected using personal information acquisition methods. Then, the collected data is analyzed using preference analysis methods to extract information about the user's preferred style, colors, and brands.

[0380] The emotion recognition system monitors the user's voice tone, facial expressions, and text input in real time to analyze their emotional state. Emotion recognition software, such as Microsoft Azure Cognitive Services, is used for emotion analysis.

[0381] Next, the suggestion engine generates the most suitable clothing combinations for the user based on the aforementioned preference and emotion data. These outfit suggestions are then visually displayed on the wearable device using AR rendering frameworks such as Unity or ARKit.

[0382] The commercial information provision system provides detailed information and purchase links for the suggested clothing. This makes it easy for users to purchase the suggested clothing.

[0383] Furthermore, feedback from user terminals is sent back to the server and analyzed by continuous machine learning algorithms to improve the accuracy of suggestions.

[0384] For example, if a user browsing new suits in a virtual store is detected to be nervous before a presentation, the system can offer fashion items that convey a professional yet relaxed impression and play relaxing audio guidance.

[0385] Examples of prompt statements to input into the generative AI model are as follows:

[0386] "The system analyzes voice and facial expression data to suggest fashion items that suit a state of tension, and plays relaxing soundtracks to help calm the mind."

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The server retrieves the user's personal information from the terminal. The input is the profile data provided by the user, and the output is storing this data in a database. Personal information includes name, age, gender, fashion preferences, etc.

[0390] Step 2:

[0391] The server analyzes the acquired personal information using a preference analysis algorithm. The input is personal information, and the output is the analyzed user's preferences and style information. Specifically, it uses a machine learning model to analyze past preference data and extract the user's preferences.

[0392] Step 3:

[0393] The device transmits the user's voice, facial expressions, and text input to an emotion recognition module in real time. The input is this real-time data, and the output is the result of the analysis of the user's emotional state. Specifically, the emotion recognition software uses Microsoft Azure's Cognitive Services to analyze emotions from voice and facial expressions.

[0394] Step 4:

[0395] The server generates appropriate clothing combinations using a suggestion engine based on the analyzed user preference information and emotional state. The input is preference information and emotional data, and the output is a list of selected fashion items. Prompt sentences are used as input to the generating AI model to optimize the generated outfits.

[0396] Step 5:

[0397] The server collects commercial information and sends detailed information and purchase links for suggested clothing items to the terminal. The input is a list of suggested fashion items, and the output is commercial information that the user can purchase. Specifically, it retrieves the latest prices and inventory information from e-commerce platforms.

[0398] Step 6:

[0399] The user reviews fashion suggestions displayed on their device and provides feedback on the selected items. The input is the user's feedback, and the output is the feedback data sent to the server. Specifically, the feedback is collected in the form of a simple questionnaire and sent to the server.

[0400] Step 7:

[0401] The server analyzes user feedback and learns to improve the accuracy of its suggestions using machine learning algorithms. The input is user feedback data, and the output is an improved preference analysis model. As part of data processing, feedback is added to the dataset and used as new training data.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] The system according to the present invention is implemented in a way that efficiently acquires users' personal information and analyzes their fashion preferences to provide each user with the most suitable accessory coordination. A specific embodiment of this system is described below.

[0419] The system first obtains information about the user's name, age, gender, body type, fashion style preferences, and lifestyle during initial registration. The user inputs this information via a terminal, and a server on the digital platform receives this information.

[0420] The server uses machine learning algorithms to analyze the user's preferences based on the personal information they enter. This analysis detects preferences for specific colors and styles, as well as frequent behavioral patterns in their lifestyle. The server then compares this information with a trend database to generate multiple accessory combinations suitable for the user.

[0421] The generated outfits are displayed on the user's device and can be visually confirmed through the user interface. At the same time, the system provides commercial information from nearby online and physical stores, taking into account the user's current location, and includes features to facilitate purchasing. For example, if a user desires a "casual weekend style," a combination of a checked shirt and denim pants will be displayed along with inventory information from local stores.

[0422] Furthermore, users can submit feedback on the suggestions as they continue to use the system. The server analyzes this feedback and uses it to improve the suggestion algorithm. This allows for a better fit to the user's preferences.

[0423] Thus, the system according to the present invention can continuously provide fashion suggestions that match the individual needs of users, thereby realizing a personalized service.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] When a user first accesses the service, they create an account using their device and enter basic personal information such as their name, age, gender, and body type. This information is then sent to the server.

[0427] Step 2:

[0428] Through the device, users answer questions about their fashion preferences and lifestyle. For example, they select their favorite colors, places they frequent, and how often they make purchases. This information is also sent to the server.

[0429] Step 3:

[0430] The server analyzes the acquired personal information and preference data and stores it in a database as user profiles. Machine learning algorithms are then used to perform statistical analysis and identify user trends.

[0431] Step 4:

[0432] The server compares the analysis results with a trend database to generate accessory combinations that suit the user's lifestyle. In doing so, it refers to similar user profiles to select the optimal coordination.

[0433] Step 5:

[0434] The terminal displays suggested combinations of decorative items received from the server to the user. These suggestions include a visual format, designed for easy user review.

[0435] Step 6:

[0436] The server uses the user's current location information to provide commercial information about nearby stores and online shops where the suggested decorative items can be purchased. This includes information on price and availability.

[0437] Step 7:

[0438] Users input feedback on the presented suggestions into their device. This includes comments about styles they don't like or information that is missing.

[0439] Step 8:

[0440] The device collects user feedback and sends it to the server. The server analyzes this feedback and updates its machine learning model to improve the accuracy of future suggestions.

[0441] (Example 1)

[0442] 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."

[0443] In today's increasingly diverse consumer landscape, there is a growing need to efficiently provide fashion suggestions tailored to individual user preferences. Traditional, fixed suggestion systems struggle to adequately reflect users' detailed preferences and location information, resulting in a lack of highly satisfying personalized recommendations. Furthermore, the process of improving the accuracy of suggestions through user feedback is not functioning effectively, limiting the ability to provide services that truly meet user needs.

[0444] 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.

[0445] In this invention, the server includes means for acquiring attribute data, means for analyzing preferences using generative AI technology, and means for creating suitable accessory placement plans. This makes it possible to analyze the user's detailed preferences and provide personalized fashion suggestions while offering location-based commercial information. Furthermore, the learning function using the generative AI model allows for continuous improvement of the accuracy of suggestions by utilizing user feedback.

[0446] "Attribute data" refers to personal information such as a user's name, age, gender, and body type, and is fundamental data used for fashion recommendations.

[0447] "Generative AI technology" refers to a method that uses artificial intelligence to analyze users' preferences and tendencies and suggest the most suitable decorative items.

[0448] "Preference" refers to a user's personal fashion preferences, such as a particular color, style, or lifestyle.

[0449] "Arrangement suggestions for accessories" refers to suggestions that present combinations of clothing, accessories, and other items based on the analyzed user preferences.

[0450] A "user interface" refers to the means of interaction that allows users to visually confirm and manipulate suggestions from a system.

[0451] "Location information" refers to data that indicates the user's current geographical location and is used to provide commercial information.

[0452] "Commercial information" refers to marketing data related to products, such as inventory status of decorative items and information on stores where they can be purchased.

[0453] A "generative AI model" refers to the structure of an algorithm that continuously learns based on user feedback, aiming to improve the accuracy of its suggestions.

[0454] This invention is a system that provides fashion suggestions tailored to the individual preferences of users. The system is started when the user inputs attribute data via a terminal. This attribute data includes information about the user's name, age, gender, body type, fashion style preferences, and lifestyle. The terminal transmits this data to a server via a secure communication protocol. The server stores the received information in a database and analyzes this data using generative AI technology.

[0455] Based on the analysis results, the server identifies the user's preferences and generates suitable accessory placement suggestions. The generating AI model combines the latest fashion trend data with the user's individual preferences to optimize the suggestions. This enables fashion suggestions that respond to specific prompts such as "casual weekend style."

[0456] The generated decorative item placement suggestions are sent to the terminal and can be visually confirmed through the user interface. The system is designed so that users can view the suggested styles and proceed directly to the purchase process. The server also takes the user's location into consideration and provides relevant commercial information, such as the inventory status of nearby stores.

[0457] Furthermore, users can send feedback on the suggestions from their devices to the server. The server incorporates this feedback into the generating AI model to improve the accuracy of the suggestions. For example, based on feedback such as "This suggestion doesn't suit me," the next suggestion will be optimized to present options that are more in line with the user's preferences.

[0458] In this way, the present invention makes it possible to provide highly personalized fashion suggestions, enabling users to efficiently find and purchase the style they desire. This system offers a new fashion selection experience through suggestions that accurately reflect the user's lifestyle and preferences.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The user uses a terminal to input attribute data such as name, age, gender, body type, fashion style preferences, and lifestyle. This input is sent to the server in digital format. The input data is formatted in JSON format and ready to be passed to the server. Once the server receives it, the next processing step begins.

[0462] Step 2:

[0463] The server stores the received attribute data in a structured database and supplies it to the generative AI model. The generative AI model analyzes this data to identify the user's preferences. In this process, clustering and classification algorithms are used to extract patterns of user preferences. As output, a list of tags for colors and styles preferred by the user is generated.

[0464] Step 3:

[0465] Based on the analysis results, the server compares them with an external trend database to generate suitable accessory placement suggestions. The generating AI model combines user-oriented information and trend data to create specific fashion coordinates. From this output, a list of suggested accessories is created.

[0466] Step 4:

[0467] The server sends the generated layout plan to the terminal, which displays it on the user interface. The user can visually confirm the displayed layout and interact with it. The terminal generates prompt messages as multimedia content such as images, text, and links, and displays them to the user.

[0468] Step 5:

[0469] Users can access suggested accessories and purchase items through their device. The server also uses the user's location information to provide relevant store and inventory information to support the purchase. Based on prompts such as "casual weekend style," the user receives suggestions and proceeds to the actual purchase step.

[0470] Step 6:

[0471] Users send feedback on the suggestions from their device to the server. This feedback is used by the generative AI model to learn for future suggestions. This process continuously improves the accuracy of the suggestions, enabling suggestions that are better suited to the user's preferences.

[0472] (Application Example 1)

[0473] 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."

[0474] Modern consumers have diverse tastes and lifestyles, and they seek personalized recommendations for accessories. However, traditional systems can only utilize static data, making it difficult to meet individual needs in real time. Furthermore, there was a lack of technology to optimize the in-store shopping experience. This resulted in consumers spending a lot of time finding suitable products, leading to decreased satisfaction.

[0475] 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.

[0476] In this invention, the server includes a device for acquiring a user's personal information, a device for analyzing the user's preferences based on the acquired personal information, a device for suggesting specific accessory combinations to the user based on the analyzed information, a device for providing commercial information on accessories related to the suggestion, a device for collecting user feedback and learning to improve the accuracy of the suggestions, and a device for recognizing products using a wearable device in a physical store and displaying optimal information to the user. This enables optimal product suggestions that meet consumers' real-time needs and preferences, thereby improving the in-store shopping experience.

[0477] "User personal information" refers to information necessary for providing personalized services, such as the user's name, age, gender, body type, fashion style, and lifestyle.

[0478] A "device for analyzing user preferences based on acquired personal information" is a device used to analyze collected personal information of users and identify their preferences and tendencies.

[0479] A "device for suggesting specific accessory combinations to users based on analyzed information" is a device that selects and provides the most suitable accessory combination to the user based on the results of preference analysis.

[0480] A "device for providing commercial information" is a device used to provide users with sales data and store information related to the proposed decorative items.

[0481] A "learning device" is a device that collects feedback from users and uses that data to execute a machine learning process aimed at improving the accuracy of suggestions.

[0482] A "wearable device" refers to a device that a user wears to detect their movements and location within a physical store and provide visual information.

[0483] A "device for recognizing products and displaying information best suited to the user" is a device that identifies products in a physical store and displays product information that is highly relevant to the user.

[0484] This embodiment provides a system that enhances the in-store shopping experience by allowing users to use smart devices. The system includes a server, a user terminal, and a wearable device installed in the physical store.

[0485] The server collects users' personal information and performs preference analysis based on it. The collected data includes details about name, age, gender, body type, fashion style, and lifestyle. Using this information, a machine learning algorithm analyzes the user's preferences and generates individually optimized accessory combinations. This algorithm also learns from past user feedback to improve the accuracy of its suggestions.

[0486] The user terminal is primarily used as a wearable device, such as smart glasses, and receives information from the server in real time. When the user enters a physical store, the device recognizes the products in the store and visually displays product information suggested to the user. This is to instantly present the most suitable product information for the user in the store based on data from the server, thereby stimulating purchasing intent.

[0487] Furthermore, it provides commercial information usable at physical stores, linked to the user's geographical information. This includes inventory information and special offers, playing a role in supporting the user's purchasing decisions.

[0488] For example, if a user enters a fashion store wearing smart glasses, the glasses' display will show a recommended combination of a checked shirt and denim pants for that location. Based on this information, the user can try on the items and quickly decide whether or not to purchase them.

[0489] In utilizing the generative AI model, the following prompt example is provided: "This user is male, in his 30s, prefers casual fashion, and frequently engages in outdoor activities. He is currently in a men's fashion store in the city center. Recognize the items in the store and suggest the most suitable casual outdoor outfit for this user."

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The user enters personal information using a terminal. The server receives this information and processes data related to name, age, gender, body type, fashion style, and lifestyle. This registers the user's basic profile information in the database.

[0493] Step 2:

[0494] The server executes machine learning algorithms based on registered personal information to analyze user preferences. It analyzes the entered personal information and uses a generative AI model to extract preferences for specific colors and styles. The analysis results output analytical data containing the characteristics of the style best suited to the user.

[0495] Step 3:

[0496] The server compares the trend database and generates specific accessory combinations based on the analysis results. Calculations are performed comparing the trend database and the analysis data, and a list of accessories to suggest to the user is output.

[0497] Step 4:

[0498] The generated combination of accessories is sent to the user's device (smart glasses). The user can then view product images through the device and decide on their actions within the store. This visually displays suggested product information to the user.

[0499] Step 5:

[0500] The terminal recognizes products in the physical store and compares them with accessory information sent from the server. It processes product images input by the camera device and searches for matches against the accessory list provided by the server. If the recognition data and accessory information match, detailed information is presented to the user.

[0501] Step 6:

[0502] Users make choices regarding trying on and purchasing suggested accessories. Wearable devices are used to examine product information in detail and confirm their purchase intent. This action is sent to the server as feedback and used to improve accuracy in the future.

[0503] Step 7:

[0504] The server accumulates evaluation data based on user behavior and uses it to improve the proposed algorithm. The data obtained as feedback is statistically processed, and the model is updated. This process further improves the fit to individual users.

[0505] 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.

[0506] This invention is implemented in a form that enables more personalized suggestions by combining a system that suggests fashion coordinates based on the user's personal information and preferences with an emotion engine that recognizes the user's emotions.

[0507] This system begins with the user creating an account using a device and entering personal information and fashion preferences. Users can also add information about their daily lifestyle via the device. The server retrieves this information, stores it in a database, and runs machine learning algorithms for preference analysis.

[0508] Furthermore, the emotion engine collects emotional data from the user's voice tone, facial expressions, and text input patterns. The server integrates this emotional data with existing preference analysis results to generate the combination of accessories that best suits the user's current emotional state.

[0509] The suggested outfits in this process are displayed on the user's device, allowing the user to visually confirm them. For example, if the system detects that the user is feeling stressed, a casual style designed to create a relaxed atmosphere will be suggested. In this case, the server also collects the necessary commercial information for the suggestions and provides purchase links for items that match the user's current mood.

[0510] User feedback is sent to the server via the terminal and used for learning to improve the overall system's suggestion accuracy. This feedback is used to evaluate how useful the suggestions were and is stored as data to further improve the accuracy of emotional responses. In this way, the present invention can realize detailed fashion suggestions that respond to the user's emotional state and improve the user experience.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] Users create an account using their device and enter personal information, fashion preferences, and lifestyle information. This is how initial data is collected.

[0514] Step 2:

[0515] The terminal sends the collected information to the server and stores it in a database. This information forms the basis for subsequent analysis.

[0516] Step 3:

[0517] The server uses machine learning algorithms to analyze data on user preferences and identify the user's fashion tendencies.

[0518] Step 4:

[0519] Users can manually input their current emotional state using the emotion input interface provided on the device, or the device can automatically recognize emotions from the user's voice and facial expressions. This data is processed by the emotion engine.

[0520] Step 5:

[0521] The server integrates emotional data obtained from the emotion engine with existing preference data to generate a combination of accessories suitable for the current emotional state.

[0522] Step 6:

[0523] The device visually presents the user with suggested accessory combinations. These suggestions include commercial information relevant to the user's mood, which the user can then review.

[0524] Step 7:

[0525] The server collects commercial information corresponding to the suggested decorative items, taking into account the user's location, and provides the optimal purchase option.

[0526] Step 8:

[0527] Users input feedback on the suggested outfits into their terminals and send it to the server. This feedback allows the system to more accurately adjust future suggestions.

[0528] Step 9:

[0529] The server analyzes feedback data and learns to improve the accuracy of its suggestion algorithms and emotion engine. This enhances the individual user experience.

[0530] (Example 2)

[0531] 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."

[0532] Conventional fashion recommendation systems rely solely on the user's individual characteristics and preferences, making it difficult to provide personalized recommendations that adequately consider the user's emotional state. As a result, the selection of accessories that are suitable for the user's current emotions and situation is often inadequate, leading to decreased user satisfaction.

[0533] 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.

[0534] In this invention, the server includes means for acquiring the user's personal characteristics, means for analyzing the user's preferences based on the acquired personal characteristics, and means for sensing the user's emotional state and generating accessory suggestions that take that state into consideration. This makes it possible to provide fashion suggestions optimized for the user's current emotions.

[0535] "Personal characteristics" refer to distinctive information unique to the user, such as gender, age, body type, hobbies, and occupation.

[0536] "Preferences" refer to the user's tastes in fashion, such as preferred colors, styles, and brands.

[0537] "Emotional state" refers to the psychological condition that the user is currently experiencing, such as stress, relaxation, or excitement.

[0538] "Ornaments" refer to items used to adorn an individual's appearance, such as clothing, shoes, and accessories.

[0539] "Commercial information" refers to market information related to decorative items, such as sales data, prices, inventory status, and purchase links.

[0540] "Suggestion accuracy" refers to an indicator that measures the degree to which suggested decorative items meet the user's expectations and desires, as well as their level of satisfaction.

[0541] This invention is a system that comprehensively analyzes a user's personal characteristics, preferences, and emotional state in order to provide personalized fashion suggestions to the user. The system is realized through the cooperation of a server and a terminal.

[0542] Users create an account using the device and input information about their personal characteristics and preferences. The device is equipped with voice input and a camera, which can be used to collect the user's emotional state in real time. A standard touch input interface can be used for this process.

[0543] The server stores information obtained from users in a database and performs analysis. Open-source machine learning libraries are often used as machine learning algorithms. For example, TensorFlow is used to model user preferences and improve the accuracy of suggestions. General sentiment recognition APIs are utilized for analyzing sentiment data.

[0544] In implementing this system, the server generates accessory suggestions that combine the user's emotions and preferences based on analyzed data. A generative AI model is used to provide suggestions that take the latest fashion trends into account. These suggestions also include purchase links for items that match the user's emotions, making it easy for users to buy products.

[0545] As a concrete example, when a user is feeling stressed, their emotional state is sent from their device to the server, and a relaxing fashion coordinate is suggested. For instance, a prompt might be, "How can we generate appropriate fashion suggestions to provide a relaxing atmosphere when the user is feeling stressed?"

[0546] In this way, highly accurate suggestions based on the user's emotions and preferences are made through the system, improving the user experience.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] Users create accounts using their devices. During this process, users input personal characteristics and preferences, and the device sends this data to the server. The server stores this data in a database and uses it as foundational information to generate the user's profile. This profile is then used to improve the accuracy of future recommendations.

[0550] Step 2:

[0551] The server analyzes the user's personal characteristics and preferences stored in the database. This analysis utilizes machine learning algorithms to extract preference patterns and trend data. Using a generative AI model, it identifies the user's preferred fashion trends and obtains analysis results useful for future suggestions. The analysis results are then stored back in the database.

[0552] Step 3:

[0553] The device uses its built-in camera and microphone to capture the user's emotional state in real time. This includes facial expression data and changes in voice tone. The collected emotional data is sent from the device to a server and used as input data. On the server, a common emotion recognition API is used to analyze the emotional data and identify the user's emotional state.

[0554] Step 4:

[0555] The server integrates previously analyzed preference information and emotional state data to generate optimal fashion suggestions. This process utilizes a generative AI model to provide personalized suggestions that combine the user's emotions and preference data. For example, if the system determines the user is stressed, it will generate suggestions that promote relaxation. The output includes suggested outfits and commercial information.

[0556] Step 5:

[0557] The device visually displays fashion suggestions received from the server to the user. The displayed suggestions also include purchase links for related accessories, allowing users to easily buy items they are interested in. Users review the suggestions and provide feedback on their satisfaction level and areas for improvement. This feedback is sent to the server via the device and used as training data to improve the accuracy of future suggestions.

[0558] (Application Example 2)

[0559] 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."

[0560] In fashion coordination suggestions, it is difficult to provide more personalized suggestions based on the user's personal preferences and emotional state, and there is a need for a system that accurately supports users in selecting the clothing they need at that moment. Furthermore, there is a challenge in that it is desirable to improve user satisfaction through product suggestions that respond to emotions.

[0561] 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.

[0562] In this invention, the server includes means for acquiring the user's personal information, means for analyzing the user's preferences based on the acquired personal information, means for suggesting individual clothing combinations to the user based on the analyzed information and emotional state, means for providing commercial information on clothing missing from the suggestions, means for emotional recognition to collect the user's emotional data and reflect it in the suggestions, and means for collecting evaluations from the user and learning to improve the accuracy of the suggestions. This enables highly customized fashion suggestions that reflect the user's preferences and real-time emotional state.

[0563] "Personal information" refers to basic information used to identify a user, such as their name, age, gender, address, and contact information.

[0564] "Preferences" refer to information that indicates a user's specific likes and interests, including information about fashion styles and color palette choices.

[0565] A "clothing combination" is a coordinated outfit formed by combining multiple pieces of clothing and accessories.

[0566] "Commercial information" refers to promotional data related to products, such as price, stock availability, and purchase links.

[0567] "Emotion recognition" is the process of identifying an emotion from a user's voice tone, facial expressions, and text input patterns.

[0568] "Suggestion accuracy" refers to the accuracy of a system's ability to provide appropriate fashion suggestions that match the user's needs and preferences.

[0569] This invention provides a system in which users receive fashion coordination suggestions in a virtual store using wearable devices such as smart glasses or head-mounted displays. The server implements this system using multiple means.

[0570] First, basic profile data entered by the user is collected using personal information acquisition methods. Then, the collected data is analyzed using preference analysis methods to extract information about the user's preferred style, colors, and brands.

[0571] The emotion recognition system monitors the user's voice tone, facial expressions, and text input in real time to analyze their emotional state. Emotion recognition software, such as Microsoft Azure Cognitive Services, is used for emotion analysis.

[0572] Next, the suggestion engine generates the most suitable clothing combinations for the user based on the aforementioned preference and emotion data. These outfit suggestions are then visually displayed on the wearable device using AR rendering frameworks such as Unity or ARKit.

[0573] The commercial information provision system provides detailed information and purchase links for the suggested clothing. This makes it easy for users to purchase the suggested clothing.

[0574] Furthermore, feedback from user terminals is sent back to the server and analyzed by continuous machine learning algorithms to improve the accuracy of suggestions.

[0575] For example, if a user browsing new suits in a virtual store is detected to be nervous before a presentation, the system can offer fashion items that convey a professional yet relaxed impression and play relaxing audio guidance.

[0576] Examples of prompt statements to input into the generative AI model are as follows:

[0577] "The system analyzes voice and facial expression data to suggest fashion items that suit a state of tension, and plays relaxing soundtracks to help calm the mind."

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The server retrieves the user's personal information from the terminal. The input is the profile data provided by the user, and the output is storing this data in a database. Personal information includes name, age, gender, fashion preferences, etc.

[0581] Step 2:

[0582] The server analyzes the acquired personal information using a preference analysis algorithm. The input is personal information, and the output is the analyzed user's preferences and style information. Specifically, it uses a machine learning model to analyze past preference data and extract the user's preferences.

[0583] Step 3:

[0584] The device transmits the user's voice, facial expressions, and text input to an emotion recognition module in real time. The input is this real-time data, and the output is the result of the analysis of the user's emotional state. Specifically, the emotion recognition software uses Microsoft Azure's Cognitive Services to analyze emotions from voice and facial expressions.

[0585] Step 4:

[0586] The server generates appropriate clothing combinations using a suggestion engine based on the analyzed user preference information and emotional state. The input is preference information and emotional data, and the output is a list of selected fashion items. Prompt sentences are used as input to the generating AI model to optimize the generated outfits.

[0587] Step 5:

[0588] The server collects commercial information and sends detailed information and purchase links for suggested clothing items to the terminal. The input is a list of suggested fashion items, and the output is commercial information that the user can purchase. Specifically, it retrieves the latest prices and inventory information from e-commerce platforms.

[0589] Step 6:

[0590] The user reviews fashion suggestions displayed on their device and provides feedback on the selected items. The input is the user's feedback, and the output is the feedback data sent to the server. Specifically, the feedback is collected in the form of a simple questionnaire and sent to the server.

[0591] Step 7:

[0592] The server analyzes user feedback and learns to improve the accuracy of its suggestions using machine learning algorithms. The input is user feedback data, and the output is an improved preference analysis model. As part of data processing, feedback is added to the dataset and used as new training data.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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".

[0610] The system according to the present invention is implemented in a way that efficiently acquires users' personal information and analyzes their fashion preferences to provide each user with the most suitable accessory coordination. A specific embodiment of this system is described below.

[0611] The system first obtains information about the user's name, age, gender, body type, fashion style preferences, and lifestyle during initial registration. The user inputs this information via a terminal, and a server on the digital platform receives this information.

[0612] The server uses machine learning algorithms to analyze the user's preferences based on the personal information they enter. This analysis detects preferences for specific colors and styles, as well as frequent behavioral patterns in their lifestyle. The server then compares this information with a trend database to generate multiple accessory combinations suitable for the user.

[0613] The generated outfits are displayed on the user's device and can be visually confirmed through the user interface. At the same time, the system provides commercial information from nearby online and physical stores, taking into account the user's current location, and includes features to facilitate purchasing. For example, if a user desires a "casual weekend style," a combination of a checked shirt and denim pants will be displayed along with inventory information from local stores.

[0614] Furthermore, users can submit feedback on the suggestions as they continue to use the system. The server analyzes this feedback and uses it to improve the suggestion algorithm. This allows for a better fit to the user's preferences.

[0615] Thus, the system according to the present invention can continuously provide fashion suggestions that match the individual needs of users, thereby realizing a personalized service.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] When a user first accesses the service, they create an account using their device and enter basic personal information such as their name, age, gender, and body type. This information is then sent to the server.

[0619] Step 2:

[0620] Through the device, users answer questions about their fashion preferences and lifestyle. For example, they select their favorite colors, places they frequent, and how often they make purchases. This information is also sent to the server.

[0621] Step 3:

[0622] The server analyzes the acquired personal information and preference data and stores it in a database as user profiles. Machine learning algorithms are then used to perform statistical analysis and identify user trends.

[0623] Step 4:

[0624] The server compares the analysis results with a trend database to generate accessory combinations that suit the user's lifestyle. In doing so, it refers to similar user profiles to select the optimal coordination.

[0625] Step 5:

[0626] The terminal displays suggested combinations of decorative items received from the server to the user. These suggestions include a visual format, designed for easy user review.

[0627] Step 6:

[0628] The server uses the user's current location information to provide commercial information about nearby stores and online shops where the suggested decorative items can be purchased. This includes information on price and availability.

[0629] Step 7:

[0630] Users input feedback on the presented suggestions into their device. This includes comments about styles they don't like or information that is missing.

[0631] Step 8:

[0632] The device collects user feedback and sends it to the server. The server analyzes this feedback and updates its machine learning model to improve the accuracy of future suggestions.

[0633] (Example 1)

[0634] 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".

[0635] In today's increasingly diverse consumer landscape, there is a growing need to efficiently provide fashion suggestions tailored to individual user preferences. Traditional, fixed suggestion systems struggle to adequately reflect users' detailed preferences and location information, resulting in a lack of highly satisfying personalized recommendations. Furthermore, the process of improving the accuracy of suggestions through user feedback is not functioning effectively, limiting the ability to provide services that truly meet user needs.

[0636] 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.

[0637] In this invention, the server includes means for acquiring attribute data, means for analyzing preferences using generative AI technology, and means for creating suitable accessory placement plans. This makes it possible to analyze the user's detailed preferences and provide personalized fashion suggestions while offering location-based commercial information. Furthermore, the learning function using the generative AI model allows for continuous improvement of the accuracy of suggestions by utilizing user feedback.

[0638] "Attribute data" refers to personal information such as a user's name, age, gender, and body type, and is fundamental data used for fashion recommendations.

[0639] "Generative AI technology" refers to a method that uses artificial intelligence to analyze users' preferences and tendencies and suggest the most suitable decorative items.

[0640] "Preference" refers to a user's personal fashion preferences, such as a particular color, style, or lifestyle.

[0641] "Arrangement suggestions for accessories" refers to suggestions that present combinations of clothing, accessories, and other items based on the analyzed user preferences.

[0642] A "user interface" refers to the means of interaction that allows users to visually confirm and manipulate suggestions from a system.

[0643] "Location information" refers to data that indicates the user's current geographical location and is used to provide commercial information.

[0644] "Commercial information" refers to marketing data related to products, such as inventory status of decorative items and information on stores where they can be purchased.

[0645] A "generative AI model" refers to the structure of an algorithm that continuously learns based on user feedback, aiming to improve the accuracy of its suggestions.

[0646] This invention is a system that provides fashion suggestions tailored to the individual preferences of users. The system is started when the user inputs attribute data via a terminal. This attribute data includes information about the user's name, age, gender, body type, fashion style preferences, and lifestyle. The terminal transmits this data to a server via a secure communication protocol. The server stores the received information in a database and analyzes this data using generative AI technology.

[0647] Based on the analysis results, the server identifies the user's preferences and generates suitable accessory placement suggestions. The generating AI model combines the latest fashion trend data with the user's individual preferences to optimize the suggestions. This enables fashion suggestions that respond to specific prompts such as "casual weekend style."

[0648] The generated decorative item placement suggestions are sent to the terminal and can be visually confirmed through the user interface. The system is designed so that users can view the suggested styles and proceed directly to the purchase process. The server also takes the user's location into consideration and provides relevant commercial information, such as the inventory status of nearby stores.

[0649] Furthermore, users can send feedback on the suggestions from their devices to the server. The server incorporates this feedback into the generating AI model to improve the accuracy of the suggestions. For example, based on feedback such as "This suggestion doesn't suit me," the next suggestion will be optimized to present options that are more in line with the user's preferences.

[0650] In this way, the present invention makes it possible to provide highly personalized fashion suggestions, enabling users to efficiently find and purchase the style they desire. This system offers a new fashion selection experience through suggestions that accurately reflect the user's lifestyle and preferences.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The user uses a terminal to input attribute data such as name, age, gender, body type, fashion style preferences, and lifestyle. This input is sent to the server in digital format. The input data is formatted in JSON format and ready to be passed to the server. Once the server receives it, the next processing step begins.

[0654] Step 2:

[0655] The server stores the received attribute data in a structured database and supplies it to the generative AI model. The generative AI model analyzes this data to identify the user's preferences. In this process, clustering and classification algorithms are used to extract patterns of user preferences. As output, a list of tags for colors and styles preferred by the user is generated.

[0656] Step 3:

[0657] Based on the analysis results, the server compares them with an external trend database to generate suitable accessory placement suggestions. The generating AI model combines user-oriented information and trend data to create specific fashion coordinates. From this output, a list of suggested accessories is created.

[0658] Step 4:

[0659] The server sends the generated layout plan to the terminal, which displays it on the user interface. The user can visually confirm the displayed layout and interact with it. The terminal generates prompt messages as multimedia content such as images, text, and links, and displays them to the user.

[0660] Step 5:

[0661] Users can access suggested accessories and purchase items through their device. The server also uses the user's location information to provide relevant store and inventory information to support the purchase. Based on prompts such as "casual weekend style," the user receives suggestions and proceeds to the actual purchase step.

[0662] Step 6:

[0663] Users send feedback on the suggestions from their device to the server. This feedback is used by the generative AI model to learn for future suggestions. This process continuously improves the accuracy of the suggestions, enabling suggestions that are better suited to the user's preferences.

[0664] (Application Example 1)

[0665] 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".

[0666] Modern consumers have diverse tastes and lifestyles, and they seek personalized recommendations for accessories. However, traditional systems can only utilize static data, making it difficult to meet individual needs in real time. Furthermore, there was a lack of technology to optimize the in-store shopping experience. This resulted in consumers spending a lot of time finding suitable products, leading to decreased satisfaction.

[0667] 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.

[0668] In this invention, the server includes a device for acquiring a user's personal information, a device for analyzing the user's preferences based on the acquired personal information, a device for suggesting specific accessory combinations to the user based on the analyzed information, a device for providing commercial information on accessories related to the suggestion, a device for collecting user feedback and learning to improve the accuracy of the suggestions, and a device for recognizing products using a wearable device in a physical store and displaying optimal information to the user. This enables optimal product suggestions that meet consumers' real-time needs and preferences, thereby improving the in-store shopping experience.

[0669] "User personal information" refers to information necessary for providing personalized services, such as the user's name, age, gender, body type, fashion style, and lifestyle.

[0670] A "device for analyzing user preferences based on acquired personal information" is a device used to analyze collected personal information of users and identify their preferences and tendencies.

[0671] A "device for suggesting specific accessory combinations to users based on analyzed information" is a device that selects and provides the most suitable accessory combination to the user based on the results of preference analysis.

[0672] A "device for providing commercial information" is a device used to provide users with sales data and store information related to the proposed decorative items.

[0673] A "learning device" is a device that collects feedback from users and uses that data to execute a machine learning process aimed at improving the accuracy of suggestions.

[0674] A "wearable device" refers to a device that a user wears to detect their movements and location within a physical store and provide visual information.

[0675] A "device for recognizing products and displaying information best suited to the user" is a device that identifies products in a physical store and displays product information that is highly relevant to the user.

[0676] This embodiment provides a system that enhances the in-store shopping experience by allowing users to use smart devices. The system includes a server, a user terminal, and a wearable device installed in the physical store.

[0677] The server collects users' personal information and performs preference analysis based on it. The collected data includes details about name, age, gender, body type, fashion style, and lifestyle. Using this information, a machine learning algorithm analyzes the user's preferences and generates individually optimized accessory combinations. This algorithm also learns from past user feedback to improve the accuracy of its suggestions.

[0678] The user terminal is primarily used as a wearable device, such as smart glasses, and receives information from the server in real time. When the user enters a physical store, the device recognizes the products in the store and visually displays product information suggested to the user. This is to instantly present the most suitable product information for the user in the store based on data from the server, thereby stimulating purchasing intent.

[0679] Furthermore, it provides commercial information usable at physical stores, linked to the user's geographical information. This includes inventory information and special offers, playing a role in supporting the user's purchasing decisions.

[0680] For example, if a user enters a fashion store wearing smart glasses, the glasses' display will show a recommended combination of a checked shirt and denim pants for that location. Based on this information, the user can try on the items and quickly decide whether or not to purchase them.

[0681] In utilizing the generative AI model, the following prompt example is provided: "This user is male, in his 30s, prefers casual fashion, and frequently engages in outdoor activities. He is currently in a men's fashion store in the city center. Recognize the items in the store and suggest the most suitable casual outdoor outfit for this user."

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] The user enters personal information using a terminal. The server receives this information and processes data related to name, age, gender, body type, fashion style, and lifestyle. This registers the user's basic profile information in the database.

[0685] Step 2:

[0686] The server executes machine learning algorithms based on registered personal information to analyze user preferences. It analyzes the entered personal information and uses a generative AI model to extract preferences for specific colors and styles. The analysis results output analytical data containing the characteristics of the style best suited to the user.

[0687] Step 3:

[0688] The server compares the trend database and generates specific accessory combinations based on the analysis results. Calculations are performed comparing the trend database and the analysis data, and a list of accessories to suggest to the user is output.

[0689] Step 4:

[0690] The generated combination of accessories is sent to the user's device (smart glasses). The user can then view product images through the device and decide on their actions within the store. This visually displays suggested product information to the user.

[0691] Step 5:

[0692] The terminal recognizes products in the physical store and compares them with accessory information sent from the server. It processes product images input by the camera device and searches for matches against the accessory list provided by the server. If the recognition data and accessory information match, detailed information is presented to the user.

[0693] Step 6:

[0694] Users make choices regarding trying on and purchasing suggested accessories. Wearable devices are used to examine product information in detail and confirm their purchase intent. This action is sent to the server as feedback and used to improve accuracy in the future.

[0695] Step 7:

[0696] The server accumulates evaluation data based on user behavior and uses it to improve the proposed algorithm. The data obtained as feedback is statistically processed, and the model is updated. This process further improves the fit to individual users.

[0697] 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.

[0698] This invention is implemented in a form that enables more personalized suggestions by combining a system that suggests fashion coordinates based on the user's personal information and preferences with an emotion engine that recognizes the user's emotions.

[0699] This system begins with the user creating an account using a device and entering personal information and fashion preferences. Users can also add information about their daily lifestyle via the device. The server retrieves this information, stores it in a database, and runs machine learning algorithms for preference analysis.

[0700] Furthermore, the emotion engine collects emotional data from the user's voice tone, facial expressions, and text input patterns. The server integrates this emotional data with existing preference analysis results to generate the combination of accessories that best suits the user's current emotional state.

[0701] The suggested outfits in this process are displayed on the user's device, allowing the user to visually confirm them. For example, if the system detects that the user is feeling stressed, a casual style designed to create a relaxed atmosphere will be suggested. In this case, the server also collects the necessary commercial information for the suggestions and provides purchase links for items that match the user's current mood.

[0702] User feedback is sent to the server via the terminal and used for learning to improve the overall system's suggestion accuracy. This feedback is used to evaluate how useful the suggestions were and is stored as data to further improve the accuracy of emotional responses. In this way, the present invention can realize detailed fashion suggestions that respond to the user's emotional state and improve the user experience.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] Users create an account using their device and enter personal information, fashion preferences, and lifestyle information. This is how initial data is collected.

[0706] Step 2:

[0707] The terminal sends the collected information to the server and stores it in a database. This information forms the basis for subsequent analysis.

[0708] Step 3:

[0709] The server uses machine learning algorithms to analyze data on user preferences and identify the user's fashion tendencies.

[0710] Step 4:

[0711] Users can manually input their current emotional state using the emotion input interface provided on the device, or the device can automatically recognize emotions from the user's voice and facial expressions. This data is processed by the emotion engine.

[0712] Step 5:

[0713] The server integrates emotional data obtained from the emotion engine with existing preference data to generate a combination of accessories suitable for the current emotional state.

[0714] Step 6:

[0715] The device visually presents the user with suggested accessory combinations. These suggestions include commercial information relevant to the user's mood, which the user can then review.

[0716] Step 7:

[0717] The server collects commercial information corresponding to the suggested decorative items, taking into account the user's location, and provides the optimal purchase option.

[0718] Step 8:

[0719] Users input feedback on the suggested outfits into their terminals and send it to the server. This feedback allows the system to more accurately adjust future suggestions.

[0720] Step 9:

[0721] The server analyzes feedback data and learns to improve the accuracy of its suggestion algorithms and emotion engine. This enhances the individual user experience.

[0722] (Example 2)

[0723] 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".

[0724] Conventional fashion recommendation systems rely solely on the user's individual characteristics and preferences, making it difficult to provide personalized recommendations that adequately consider the user's emotional state. As a result, the selection of accessories that are suitable for the user's current emotions and situation is often inadequate, leading to decreased user satisfaction.

[0725] 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.

[0726] In this invention, the server includes means for acquiring the user's personal characteristics, means for analyzing the user's preferences based on the acquired personal characteristics, and means for sensing the user's emotional state and generating accessory suggestions that take that state into consideration. This makes it possible to provide fashion suggestions optimized for the user's current emotions.

[0727] "Personal characteristics" refer to distinctive information unique to the user, such as gender, age, body type, hobbies, and occupation.

[0728] "Preferences" refer to the user's tastes in fashion, such as preferred colors, styles, and brands.

[0729] "Emotional state" refers to the psychological condition that the user is currently experiencing, such as stress, relaxation, or excitement.

[0730] "Ornaments" refer to items used to adorn an individual's appearance, such as clothing, shoes, and accessories.

[0731] "Commercial information" refers to market information related to decorative items, such as sales data, prices, inventory status, and purchase links.

[0732] "Suggestion accuracy" refers to an indicator that measures the degree to which suggested decorative items meet the user's expectations and desires, as well as their level of satisfaction.

[0733] This invention is a system that comprehensively analyzes a user's personal characteristics, preferences, and emotional state in order to provide personalized fashion suggestions to the user. The system is realized through the cooperation of a server and a terminal.

[0734] Users create an account using the device and input information about their personal characteristics and preferences. The device is equipped with voice input and a camera, which can be used to collect the user's emotional state in real time. A standard touch input interface can be used for this process.

[0735] The server stores information obtained from users in a database and performs analysis. Open-source machine learning libraries are often used as machine learning algorithms. For example, TensorFlow is used to model user preferences and improve the accuracy of suggestions. General sentiment recognition APIs are utilized for analyzing sentiment data.

[0736] In implementing this system, the server generates accessory suggestions that combine the user's emotions and preferences based on analyzed data. A generative AI model is used to provide suggestions that take the latest fashion trends into account. These suggestions also include purchase links for items that match the user's emotions, making it easy for users to buy products.

[0737] As a concrete example, when a user is feeling stressed, their emotional state is sent from their device to the server, and a relaxing fashion coordinate is suggested. For instance, a prompt might be, "How can we generate appropriate fashion suggestions to provide a relaxing atmosphere when the user is feeling stressed?"

[0738] In this way, highly accurate suggestions based on the user's emotions and preferences are made through the system, improving the user experience.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] Users create accounts using their devices. During this process, users input personal characteristics and preferences, and the device sends this data to the server. The server stores this data in a database and uses it as foundational information to generate the user's profile. This profile is then used to improve the accuracy of future recommendations.

[0742] Step 2:

[0743] The server analyzes the user's personal characteristics and preferences stored in the database. This analysis utilizes machine learning algorithms to extract preference patterns and trend data. Using a generative AI model, it identifies the user's preferred fashion trends and obtains analysis results useful for future suggestions. The analysis results are then stored back in the database.

[0744] Step 3:

[0745] The device uses its built-in camera and microphone to capture the user's emotional state in real time. This includes facial expression data and changes in voice tone. The collected emotional data is sent from the device to a server and used as input data. On the server, a common emotion recognition API is used to analyze the emotional data and identify the user's emotional state.

[0746] Step 4:

[0747] The server integrates previously analyzed preference information and emotional state data to generate optimal fashion suggestions. This process utilizes a generative AI model to provide personalized suggestions that combine the user's emotions and preference data. For example, if the system determines the user is stressed, it will generate suggestions that promote relaxation. The output includes suggested outfits and commercial information.

[0748] Step 5:

[0749] The device visually displays fashion suggestions received from the server to the user. The displayed suggestions also include purchase links for related accessories, allowing users to easily buy items they are interested in. Users review the suggestions and provide feedback on their satisfaction level and areas for improvement. This feedback is sent to the server via the device and used as training data to improve the accuracy of future suggestions.

[0750] (Application Example 2)

[0751] 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".

[0752] In fashion coordination suggestions, it is difficult to provide more personalized suggestions based on the user's personal preferences and emotional state, and there is a need for a system that accurately supports users in selecting the clothing they need at that moment. Furthermore, there is a challenge in that it is desirable to improve user satisfaction through product suggestions that respond to emotions.

[0753] 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.

[0754] In this invention, the server includes means for acquiring the user's personal information, means for analyzing the user's preferences based on the acquired personal information, means for suggesting individual clothing combinations to the user based on the analyzed information and emotional state, means for providing commercial information on clothing missing from the suggestions, means for emotional recognition to collect the user's emotional data and reflect it in the suggestions, and means for collecting evaluations from the user and learning to improve the accuracy of the suggestions. This enables highly customized fashion suggestions that reflect the user's preferences and real-time emotional state.

[0755] "Personal information" refers to basic information used to identify a user, such as their name, age, gender, address, and contact information.

[0756] "Preferences" refer to information that indicates a user's specific likes and interests, including information about fashion styles and color palette choices.

[0757] A "clothing combination" is a coordinated outfit formed by combining multiple pieces of clothing and accessories.

[0758] "Commercial information" refers to promotional data related to products, such as price, stock availability, and purchase links.

[0759] "Emotion recognition" is the process of identifying an emotion from a user's voice tone, facial expressions, and text input patterns.

[0760] "Suggestion accuracy" refers to the accuracy of a system's ability to provide appropriate fashion suggestions that match the user's needs and preferences.

[0761] This invention provides a system in which users receive fashion coordination suggestions in a virtual store using wearable devices such as smart glasses or head-mounted displays. The server implements this system using multiple means.

[0762] First, basic profile data entered by the user is collected using personal information acquisition methods. Then, the collected data is analyzed using preference analysis methods to extract information about the user's preferred style, colors, and brands.

[0763] The emotion recognition system monitors the user's voice tone, facial expressions, and text input in real time to analyze their emotional state. Emotion recognition software, such as Microsoft Azure Cognitive Services, is used for emotion analysis.

[0764] Next, the suggestion engine generates the most suitable clothing combinations for the user based on the aforementioned preference and emotion data. These outfit suggestions are then visually displayed on the wearable device using AR rendering frameworks such as Unity or ARKit.

[0765] The commercial information provision system provides detailed information and purchase links for the suggested clothing. This makes it easy for users to purchase the suggested clothing.

[0766] Furthermore, feedback from user terminals is sent back to the server and analyzed by continuous machine learning algorithms to improve the accuracy of suggestions.

[0767] For example, if a user browsing new suits in a virtual store is detected to be nervous before a presentation, the system can offer fashion items that convey a professional yet relaxed impression and play relaxing audio guidance.

[0768] Examples of prompt statements to input into the generative AI model are as follows:

[0769] "The system analyzes voice and facial expression data to suggest fashion items that suit a state of tension, and plays relaxing soundtracks to help calm the mind."

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The server retrieves the user's personal information from the terminal. The input is the profile data provided by the user, and the output is storing this data in a database. Personal information includes name, age, gender, fashion preferences, etc.

[0773] Step 2:

[0774] The server analyzes the acquired personal information using a preference analysis algorithm. The input is personal information, and the output is the analyzed user's preferences and style information. Specifically, it uses a machine learning model to analyze past preference data and extract the user's preferences.

[0775] Step 3:

[0776] The device transmits the user's voice, facial expressions, and text input to an emotion recognition module in real time. The input is this real-time data, and the output is the result of the analysis of the user's emotional state. Specifically, the emotion recognition software uses Microsoft Azure's Cognitive Services to analyze emotions from voice and facial expressions.

[0777] Step 4:

[0778] The server generates appropriate clothing combinations using a suggestion engine based on the analyzed user preference information and emotional state. The input is preference information and emotional data, and the output is a list of selected fashion items. Prompt sentences are used as input to the generating AI model to optimize the generated outfits.

[0779] Step 5:

[0780] The server collects commercial information and sends detailed information and purchase links for suggested clothing items to the terminal. The input is a list of suggested fashion items, and the output is commercial information that the user can purchase. Specifically, it retrieves the latest prices and inventory information from e-commerce platforms.

[0781] Step 6:

[0782] The user reviews fashion suggestions displayed on their device and provides feedback on the selected items. The input is the user's feedback, and the output is the feedback data sent to the server. Specifically, the feedback is collected in the form of a simple questionnaire and sent to the server.

[0783] Step 7:

[0784] The server analyzes user feedback and learns to improve the accuracy of its suggestions using machine learning algorithms. The input is user feedback data, and the output is an improved preference analysis model. As part of data processing, feedback is added to the dataset and used as new training data.

[0785] 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.

[0786] 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.

[0787] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] Means of obtaining users' personal information,

[0809] A means of analyzing user preferences based on acquired personal information,

[0810] A means of suggesting individual accessory combinations to the user based on the analyzed information,

[0811] Means for providing commercial information on decorative items that is lacking in the aforementioned proposal,

[0812] A means of learning by collecting user feedback to improve the accuracy of suggestions,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, which, when collecting commercial information, selects the most suitable decorative item while taking into account the user's location information.

[0816] (Claim 3)

[0817] The system according to claim 1, which classifies and displays suggested combinations of multiple decorative items according to the user's preferences and lifestyle.

[0818] "Example 1"

[0819] (Claim 1)

[0820] Means for obtaining user attribute data,

[0821] A means of analyzing user preferences using generational AI technology based on acquired attribute data,

[0822] Based on the analysis results, a means to create a suitable arrangement plan for decorative items for the user,

[0823] Means for displaying the aforementioned layout plan through a user interface for visual presentation,

[0824] A means of providing relevant commercial information based on the user's location information,

[0825] A means by which a generative AI model learns to collect feedback from users and improve the effectiveness of its suggestions,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, which takes into account the user's location and links inventory information of selected decorative items.

[0829] (Claim 3)

[0830] The system according to claim 1, which classifies and visually presents multiple layout options according to the user's preferences and lifestyle.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] A device for acquiring users' personal information,

[0834] A device for analyzing user preferences based on acquired personal information,

[0835] A device for suggesting specific combinations of decorative items to the user based on analyzed information,

[0836] A device for providing commercial information on decorative items related to the above proposal,

[0837] A device that collects user feedback and learns to improve the accuracy of its suggestions,

[0838] A device that recognizes products using wearable devices within a physical store and displays information optimized for the user,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which selects the most suitable decorative item considering the user's geographical information and proposes it using product information from physical stores.

[0842] (Claim 3)

[0843] The system according to claim 1, which categorizes and visually displays suggested combinations of multiple decorative items according to the user's preferences or lifestyle.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] Means for obtaining the personal characteristics of users,

[0847] A means of analyzing user preferences based on acquired personal characteristics,

[0848] A means of suggesting individual accessory combinations to the user based on the analyzed information,

[0849] A means for sensing the emotional state of a user and generating decorative item suggestions that take that state into consideration,

[0850] Means for providing commercial information on decorative items that is lacking in the aforementioned proposal,

[0851] A means of learning by collecting user feedback to improve the accuracy of suggestions,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, which, when collecting commercial information, selects the most suitable decorative item considering the user's geographical location.

[0855] (Claim 3)

[0856] The system according to claim 1, which classifies and displays suggested combinations of multiple decorative items according to the user's preferences and lifestyle.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] Means of obtaining users' personal information,

[0860] A means of analyzing user preferences based on acquired personal information,

[0861] A means of suggesting individual clothing combinations to users based on analyzed information and emotional state,

[0862] Means for providing commercial information on clothing that is lacking in the aforementioned proposal,

[0863] A means of recognizing emotions to collect user emotion data and reflect it in suggestions,

[0864] A means of learning by collecting user feedback to improve the accuracy of suggestions,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, which, when collecting commercial information, selects the most suitable clothing while taking into account the user's location information.

[0868] (Claim 3)

[0869] The system according to claim 1, which classifies and displays multiple clothing combination suggestions according to the user's preferences, lifestyle, and emotional state. [Explanation of Symbols]

[0870] 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. Means of obtaining users' personal information, A means of analyzing user preferences based on acquired personal information, A means of suggesting individual accessory combinations to the user based on the analyzed information, Means for providing commercial information on decorative items that is lacking in the aforementioned proposal, A means of learning by collecting user feedback to improve the accuracy of suggestions, A system that includes this.

2. The system according to claim 1, which, when collecting commercial information, selects the most suitable decorative item while taking into account the user's location information.

3. The system according to claim 1, which classifies and displays suggested combinations of multiple decorative items according to the user's preferences and lifestyle.

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