system

Abstract

We provide the system. [Solution] A system comprising: information input means for inputting user preferences and conditions; information collection means for collecting newly released product information based on the user preferences and conditions; generation means for generating visual content for products based on the collected product information; listing means for identifying and listing products that match the user preferences and conditions; recommendation means for recommending relevant products based on past user behavior data; analysis means for analyzing user feedback and reviews and extracting trends; and notification means for notifying the user of the listed product information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional online shopping, there was a problem that it took a great deal of time and effort for users to find fashion items that met their preferences and conditions. Furthermore, it was difficult to collect the necessary information from a vast number of products and propose the most suitable options for users. The present invention aims to improve the shopping experience of users and reduce stress.

Means for Solving the Problems

[0005] The present invention provides an information input means for inputting user preferences and conditions, and an information collection means for collecting newly released product information based on user preferences and conditions. It also includes a generation means for generating visual product content based on the collected product information. It includes a listing means for identifying and listing products that match user preferences and conditions, a recommendation means for recommending relevant products based on past user behavior data, and an analysis means for extracting trends by analyzing user feedback and reviews, and a notification means for notifying the user of the listed product information, thereby enabling the rapid and accurate suggestion of the most suitable products to the user.

[0006] "User" refers to an individual or end-user who uses this system.

[0007] "Preferences" refer to personal tastes regarding specific colors, styles, brands, etc., as specified by the user.

[0008] "Conditions" refer to specific parameters in product selection, such as price range, size, and available region, as specified by the user.

[0009] "Information input means" refers to an interface or function that allows users to input information about their preferences and conditions.

[0010] "Information gathering means" refers to the technology or function used to collect data on new products from partners.

[0011] "Generation means" refers to a function or technology for automatically generating visual content based on collected product information.

[0012] "Listing method" refers to a function that identifies and displays products that match the user's preferences and criteria.

[0013] "Recommendation methods" refer to technologies used to recommend relevant products based on past user behavior data.

[0014] "Analysis methods" refer to techniques that use natural language processing technology to analyze user feedback and reviews and extract trends.

[0015] "Notification method" refers to the technology or process used to deliver listed product information to the user's device. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that efficiently suggests fashion items based on user preferences. This system mainly consists of a server, terminals, and users.

[0038] User actions:

[0039] Users will be able to easily input their preferences and requirements through an application on their device. Items selectable at this stage include color, style, brand, size, and budget.

[0040] Data collection and processing:

[0041] The server regularly retrieves data on new products from partner fashion brands and online stores. This data includes a wide range of information, such as product name, category, price, and release date.

[0042] The server uses AI to generate high-quality visual content based on the collected data, making it easier for users to visually recognize products.

[0043] Product recommendations and notifications:

[0044] The server uses the central algorithm of this invention to identify products based on user preferences and conditions and create an appropriate list. It also analyzes past user behavior data to further recommend highly relevant products.

[0045] The server uses natural language processing to analyze user reviews and feedback and extract current trend information. This information is then used to provide more accurate recommendations.

[0046] The device receives notifications sent from the server and displays a list of products on the user's screen. The user can then review the displayed products in detail and consider purchasing them.

[0047] Specific example:

[0048] For example, if a user registers their search criteria as "a simple design, blue bag, and under 10,000 yen," the server extracts matching items from partner stores' data based on those criteria. It then uses AI to generate product images and create a recommendation list. The device then notifies the user of this list and provides an interface to encourage purchase.

[0049] In this way, the system has a mechanism in which each element works together to streamline the user experience and reduce stress.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The user launches the application using their device and accesses a screen where they can enter their fashion preferences and requirements. Here, they select or enter information such as color, style, brand, size, and budget.

[0053] Step 2:

[0054] The device receives information about the user's preferences and conditions, sends it to the server, and saves it as a profile.

[0055] Step 3:

[0056] The server periodically collects information on new products via APIs from partner brands and online stores. This information includes product name, category, price, release date, color, and availability.

[0057] Step 4:

[0058] The server uses collected data on new products and employs generative AI to generate visual content for those products. The images and short videos generated at this stage are then processed to be visually appealing and easy to view.

[0059] Step 5:

[0060] The server filters and scores product data based on the user's preferences, past purchase history, and browsing history, and lists the most relevant products for each user.

[0061] Step 6:

[0062] The server uses natural language processing to collect and analyze user reviews and feedback data, and extracts new trends and potential needs from this information.

[0063] Step 7:

[0064] The server generates a list of products relevant to the user and sends it to the device as a push notification. This notification includes a product overview and links.

[0065] Step 8:

[0066] The device notifies the user of received notifications and displays a list of related products on the screen. The user can select products of interest from this list and view details.

[0067] (Example 1)

[0068] 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."

[0069] Traditional fashion recommendation systems have struggled to accurately reflect diverse user preferences and criteria, making it difficult to improve the user experience. Furthermore, there were limitations in the quality of visual recognition of products and the provision of highly relevant items. This resulted in decreased user satisfaction.

[0070] 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.

[0071] In this invention, the server includes means for acquiring user preferences and conditions via an information processing device, means for generating visual content based on the acquired data, and means for analyzing feedback and evaluation information and extracting trends. This makes it possible to recommend high-quality fashion items that meet the diverse preferences of users.

[0072] "Means for obtaining user preferences and conditions via information processing equipment" refers to functions and technologies that allow users to input their preferences and requests into a system and for the system to recognize them.

[0073] "Means of acquiring data on new products" refers to functions and technologies for collecting the latest product information from partner stores and online platforms.

[0074] "Generative means for generating visual content" refers to technologies for creating content that is easily recognizable to users visually, based on collected product information.

[0075] "Means for selecting and listing products" refers to functions and technologies that select products that match the user's preferences and criteria and present them in a list format.

[0076] "Selection methods for suggesting relevant products based on history" refers to functions and technologies that analyze past user behavior records and recommend highly relevant products to users based on that information.

[0077] "Means for analyzing feedback and evaluation information and extracting trends" refers to technologies for analyzing opinions and evaluations submitted by users and extracting trends and patterns from them.

[0078] "Means of notifying users of product information" refers to functions and technologies that efficiently convey a list of selected products and related information to users.

[0079] The embodiments for carrying out the present invention are as follows.

[0080] This invention comprises a server, a terminal, and a user to efficiently recommend fashion items based on user preferences. The user inputs their preferences and criteria into an application using the terminal. This application can retrieve information such as color, style, brand, size, and budget.

[0081] The server retrieves product data from partner online stores and brands. This data includes various information such as product name, category, price, and release date. The server stores this data in a database and updates it as needed. The server also uses a generative AI model to generate visual content for products that match the user's criteria. In this process, the AI ​​model is given prompts such as, "Generate an image of a simple blue skirt."

[0082] The server extracts trend information by analyzing reviews and feedback using natural language processing technology based on data collected from users. This allows it to create recommendation lists of items that are suitable for the user's preferences. Past user behavior data is also incorporated into the recommendations.

[0083] The product list sent from the server to the terminal is then notified to the user by the terminal. The terminal displays the product information received on the user's screen, allowing the user to review the details and consider purchasing. This creates a system that improves the user's shopping experience and reduces stress.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user enters their preferences and criteria using an application on their device. This information includes color, style, brand, size, and budget. The entered information is sent to the server. At this point, the entered data represents the user's individual preferences, and the server begins processing based on this information.

[0087] Step 2:

[0088] The server uses acquired user preference information to collect product data from partner online stores and brands via APIs. This product data includes product name, category, price, and release date. The server organizes this data and stores it in a database. The input is product information, and the output is a structured database. This database is used for processing in the next step.

[0089] Step 3:

[0090] The server filters product data to match the user's preferences and uses a generative AI model to generate relevant visual content. The AI ​​model receives a prompt such as, "Generate an image of a simple blue skirt." Based on this, the AI ​​generates product images and prepares them for the user. The input is filtered product information, and the output is high-quality visual content.

[0091] Step 4:

[0092] The server creates a product list best suited to the user's preferences based on the generated visual content. This list is also analyzed in conjunction with past user behavior data to improve recommendation accuracy. Natural language processing techniques are used to analyze user reviews and feedback data to extract trend information. At this point, the input is user preference information and behavioral history, and the output is a refined product list.

[0093] Step 5:

[0094] The terminal displays a list of products received from the server to the user's screen. Upon receiving the notification, the user reviews the product information and generated visual content displayed on the screen and begins the process of considering a purchase. The input is the notification data from the server, and the output is the display result of the user interface. Through this process, the user can efficiently select the desired product.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern retail, consumers are required to efficiently find products that suit their preferences from a wide variety of options. At the same time, there is a growing demand to visually examine products and experience the feeling of trying them on from the comfort of their homes, without having to visit physical stores. Traditional online shopping platforms, however, are limited to simply displaying product images, lacking the visual experience and the feeling of trying things on, thus failing to adequately stimulate consumer purchasing intent.

[0098] 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.

[0099] In this invention, the server includes data collection means for collecting newly released products based on the user's preferences and conditions, generation means for generating visual content based on the collected data, and virtual try-on means for trying on products in a virtual space. This allows users to have a detailed visual product experience from the comfort of their homes and efficiently find products that suit their preferences.

[0100] An "information input method" is a mechanism that allows users to input their preferences and conditions into a terminal.

[0101] "Data collection methods" refer to methods for obtaining data on newly released products from partner sellers.

[0102] "Generation method" refers to a method of generating visual content for a product based on collected data.

[0103] A "list creation method" is a method of identifying products that match the user's preferences and conditions and organizing them into a list.

[0104] "Recommendation methods" refer to methods of selecting relevant products based on past user behavior.

[0105] "Analysis methods" refer to methods of analyzing user opinions and evaluations to extract recent trends.

[0106] "Notification method" refers to a method of informing users of a list of product information.

[0107] A "virtual try-on method" is a system that allows users to try on products in a virtual space.

[0108] This system is designed to suggest appropriate fashion items based on the user's preferences and circumstances. The system mainly consists of a server, terminals, and users.

[0109] The server uses data collection methods to acquire data on new products from partner sellers and online platforms. This data includes information such as product name, category, price, and release date. The server then uses a generative AI model to generate high-quality visual content based on this product data. The generated visual content allows users to intuitively understand the features of the product. For example, a prompt to the generative AI model might be, "Generate a visual of a casual jacket for autumn / winter based on the user's preferences."

[0110] The device receives user preferences and criteria through a simple interface. These include selections such as color, style, brand, size, and budget. The device also receives notifications from the server and displays a list of product information to the user. This display allows the user to review product details and simulate trying on items using virtual try-on features.

[0111] Users can use their devices to view a list of products and virtually try them on, allowing them to choose the product that best suits them from the available options. The data collected through user interaction is used for analysis to improve the accuracy of recommendations in the future.

[0112] This system achieves efficient data calculation and processing by utilizing machine learning libraries such as TENSORFLOW® and PyTorch for data processing, and Unity and Unreal Engine for visual content generation.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user launches an application on their device and enters their fashion preferences and requirements. This input data includes items such as color, style, brand, size, and budget. The device then sends the entered data to the server.

[0116] Step 2:

[0117] The server collects data on new products from partner sellers using data collection methods. This collected data includes product names, categories, prices, and release dates. The server systematically organizes this data and searches for products that match the user's input preferences.

[0118] Step 3:

[0119] The server uses a generative AI model to generate visual content based on the collected product data. In this step, prompts are created based on the collected product labels and category information and input into the model. For example, by setting the prompt to "Generate a visual of a casual, autumn / winter blue jacket," the server will output an image that specifically visualizes the product's characteristics.

[0120] Step 4:

[0121] The server uses the generated visual content to create a product list based on the user's preferences and criteria. This list matching process also utilizes past user behavior data and trend analysis data to suggest more relevant products.

[0122] Step 5:

[0123] The server sends a list of product information to the user's terminal using a notification system. The terminal displays the received data on its screen, providing an interface that allows the user to check product details on the spot.

[0124] Step 6:

[0125] Users can virtually try on selected items by operating a device. The device utilizes Unity or Unreal Engine to project the products into a virtual space, providing users with a feeling close to actual try-on.

[0126] Step 7:

[0127] User activity history and feedback are analyzed to improve the accuracy of future recommendations. The server uses this feedback data to retrain machine learning models to improve the accuracy of product recommendations tailored to the user profile.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention is a system that efficiently suggests relevant fashion items based on the user's preferences and conditions, and this system further incorporates an emotion engine. The system mainly consists of a server, terminals, and users, and comprises several main functions: interface, data collection and processing, emotion analysis, recommendation, and result notification.

[0130] User input and sentiment analysis:

[0131] Users can input their fashion preferences and requirements using an application on their device. This information is collected in real time and is powered by an emotion engine.

[0132] The device recognizes emotions not only from the user's input data but also from their actions, facial expressions, and voice during the input process, and sends this information to the server. The emotion engine analyzes this data to understand the user's current emotional state.

[0133] Data collection and content generation.

[0134] The server regularly collects the latest product information from partner fashion brands and online stores. This includes product names, categories, prices, release dates, colors, and availability.

[0135] Furthermore, the server uses a generation AI to create visual content for collected new products. This content is adjusted according to the user's emotional state and presented in a more visually appealing way.

[0136] Recommendations and notifications:

[0137] The server lists the most suitable products based on the user's preferences, conditions, emotional state, and past user behavior data. By leveraging the output of the emotion engine, product recommendations are more personalized, enhancing the user experience.

[0138] The device receives a list of products sent from the server and notifies the user of this information via push notification. By clicking on this notification, the user can access detailed product information and consider making a purchase.

[0139] Specific example:

[0140] For example, if a user sets the conditions as "casual style, red items, and a budget of under 5,000 yen," and the emotion engine also detects that the user is in a relaxed mood while viewing content, the server will extract casual red fashion items that match the conditions and generate content with a color scheme and background that promotes emotional relaxation. The device will then notify the user of this information, further enriching the shopping experience.

[0141] This invention, by combining emotion recognition technology, enables superior user adaptability compared to conventional systems and supports purchasing decisions. Furthermore, the application of visual content enhances the appeal of the information provided, thereby increasing user engagement.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user launches the application on their device and is taken to a screen where they enter their fashion preferences (e.g., color, style, brand) and requirements (e.g., budget, size). The data entered here is recorded as the user's profile information.

[0145] Step 2:

[0146] The device sends profile data, including user input information, to the server. During this process, the emotion engine analyzes the user's facial expressions and voice while they are typing to recognize their emotional state.

[0147] Step 3:

[0148] The server collects data on new products (e.g., product name, price, color, release date, etc.) from partner fashion brands and online stores via APIs. This information serves as the foundational data for content generation and recommendations by the generative AI.

[0149] Step 4:

[0150] The server takes in the analysis results from the emotion engine and uses generative AI to generate visual content that is appropriate for the user's emotional state. For example, if the user is relaxed, it will select displays with soft colors and simple designs.

[0151] Step 5:

[0152] The server filters and lists relevant products based on the user's preferences, criteria, past browsing history, and sentiment data, and then uses a recommendation algorithm to select the most suitable product.

[0153] Step 6:

[0154] The server sends the generated recommendation list, along with visual content of the product information, as a push notification to the user's device.

[0155] Step 7:

[0156] The device receives notifications from the server and displays a product list on the user's screen. The user can select products of interest from the list and view detailed information, ensuring a seamless shopping experience.

[0157] (Example 2)

[0158] 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 will be referred to as the "terminal."

[0159] In modern online shopping, users often struggle to find products that match their preferences and criteria. Furthermore, the lack of personalized product recommendations limits the user experience. Solving these challenges and improving the user experience is crucial.

[0160] 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.

[0161] In this invention, the server includes information input means for inputting the user's hobbies and conditions, emotion analysis means for analyzing the user's emotions based on the user's hobbies and conditions, and information collection means for collecting new product information based on the user's hobbies, conditions, and emotions. This makes it possible to suggest products based on the emotional state and past behavior of individual users.

[0162] "Information input means" refers to devices or methods for users to input their hobbies and preferences.

[0163] "Emotion analysis means" refers to devices or methods that identify and analyze emotions from user input information and their actions at that time.

[0164] "Information gathering means" refers to devices or methods for obtaining new product information from external sources.

[0165] "Generation means" refers to devices or methods that generate content visually or in other forms based on collected data and analysis results.

[0166] A "listing method" is a device or method for identifying and neatly arranging products that suit a user's tastes, conditions, and feelings.

[0167] A "recommendation tool" is a device or method that suggests relevant products to a user based on their past user behavior and current analysis results.

[0168] "Notification means" refers to devices or methods for informing users of listed product information.

[0169] An "artificial intelligence algorithm" is a program that enables a machine to learn and autonomously optimize itself.

[0170] "User preferences" refer to the tastes that users have for a particular genre or style.

[0171] "Conditions" refer to specific requirements or constraints set by the user.

[0172] This invention is a system that analyzes a user's emotions based on their hobbies and circumstances, and provides personalized product recommendations. The system consists of a server, terminals, and users, and combines various technologies to enhance the user experience.

[0173] User input and sentiment analysis

[0174] Users utilize an application on their smart devices to input their fashion preferences and criteria. For example, they might enter "casual style," "red items," and "budget under 5,000 yen."

[0175] The device uses sensors such as cameras and microphones to monitor the user's facial expressions and voice, and analyzes this data using emotion analysis technology. This analysis allows the user's emotional state to be deciphered in real time, and the collected data is sent to a server.

[0176] Data collection and content generation

[0177] The server periodically retrieves the latest product information from partner online platforms via APIs. This information includes product name, category, price, and stock availability.

[0178] The server then uses a generative AI model to generate visual content based on the collected data and the user's emotional state. This generation process utilizes the prompt "I'd like suggestions for casual red items under 5,000 yen in a relaxed atmosphere" to create content that is more relevant to the user.

[0179] Notifications and product suggestions

[0180] The device receives a product list generated from the server and displays it to the user as a push notification. When the user opens the notification, product details and purchase options are provided.

[0181] This system allows users to receive product suggestions tailored to their own emotions and preferences, enriching their shopping experience. This leads to increased engagement and support for purchasing decisions.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The user launches an application on their smart device and enters their fashion preferences and criteria. This input includes style type, color, price range, etc. Once this information is entered, the device receives the data, formats it appropriately, and prepares it for the next processing step.

[0185] Step 2:

[0186] The device collects user facial expressions and voice data using its camera and microphone, along with user input. This data is analyzed by an emotion analysis engine to extract the user's emotional state (e.g., relaxed state). This emotional information is then transferred to the server as specific parameters.

[0187] Step 3:

[0188] The server receives data on the user's hobbies, preferences, and emotional state from the terminal as input. The server uses APIs from partner online platforms to collect product information that matches the criteria (e.g., casual red items, priced under 5,000 yen). This information is stored in a database and used in the next generation step.

[0189] Step 4:

[0190] The server uses a generative AI model to generate visual content based on collected product data and user sentiment information. This process uses the prompt "Please suggest casual red items for under 5,000 yen in a relaxed atmosphere." The generated content features color schemes and designs that match the user's emotional state.

[0191] Step 5:

[0192] The device receives visual content and product lists sent from the server and provides suggestions to the user via push notifications. When the user taps the notification, they can access detailed information. At this time, a visually appealing user interface is displayed. The user can further review the products and make a purchase decision on the spot.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] In modern fashion shopping, there is a challenge in recommending products that suit the diverse preferences and emotions of consumers. Therefore, there is a growing demand for systems that efficiently and individually suggest products suitable for users, thereby increasing their purchasing intent. Furthermore, there is a need to improve the user experience through visually appealing content. This invention aims to solve these problems.

[0196] 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.

[0197] In this invention, the server includes data input means for inputting user preferences and conditions, product information collection means for collecting product information, visual generation means for generating visual content for products, and emotion analysis means for recognizing emotional states and personalizing product recommendations. This makes it possible to find products that match the consumer's emotions and preferences and provide a highly personalized shopping experience. Furthermore, the purchasing experience can be further enriched by intuitively presenting product information to the user through a visual device.

[0198] A "data input means" is a mechanism for inputting information necessary to collect user preferences and conditions.

[0199] A "product information gathering mechanism" is a system for efficiently collecting information about newly released products.

[0200] A "visual generation means" is a mechanism for creating visually appealing content based on collected product information.

[0201] A "product identification method" is a mechanism for finding, organizing, and providing products that match the user's preferences and conditions.

[0202] An "emotional analysis tool" is a mechanism that analyzes a user's emotional state and uses that information to personalize product recommendations.

[0203] A "recommendation system" is a mechanism for suggesting relevant products based on past user behavior data.

[0204] A "presentation means" is a mechanism that uses visual devices to directly and intuitively present product information to the user.

[0205] A specific embodiment of this invention is a system realized through the cooperation of a user, a terminal, and a server. The user can input their fashion preferences and requirements using smart glasses or a device and receive information visually. This information is collected through data input means.

[0206] The device detects information entered by the user, as well as facial expressions and voice, and analyzes them using emotion analysis tools. For example, emotion analysis AI software is used to analyze the user's emotional state in real time based on available data and send the results to a server.

[0207] The server stores product information obtained from partner fashion providers through product information collection methods and uses machine learning and generative AI to create personalized visual content using visual generation methods. This generation process is controlled using prompts that respond to the user's preferences and emotions. Specifically, prompts such as "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items to the generative AI model" are used.

[0208] The visual content generated in this way is presented to the user on a display via a visual device through a presentation means. This allows the user to easily confirm visual content that matches their emotional state and make the purchasing decision process more intuitive.

[0209] This system provides suggestions tailored to the user's emotions and preferences, improving consumer satisfaction. Furthermore, the visual presentation of products goes beyond mere screen display, offering users a richer and more interactive shopping experience.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] Users input their fashion preferences and requirements using smart glasses or devices. The entered information is collected through a data entry device. The data entry device receives the user's input and transmits it to the terminal as digital data.

[0213] Step 2:

[0214] The device senses the user's facial expressions and voice data in addition to the data entered by the user, and analyzes this data using emotion analysis tools. The device receives sensor data obtained from the camera and microphone as input and uses emotion analysis AI software to analyze the user's emotional state in real time. The analyzed emotion data is sent to a server.

[0215] Step 3:

[0216] The server periodically retrieves product information from external fashion providers using product information collection methods. This includes data such as product name, category, and price. The server stores this information in a database.

[0217] Step 4:

[0218] The server generates visual content using a generative AI model based on data obtained from emotion analysis, user preferences and conditions, and accumulated product information. Specifically, it creates visual content that matches the conditions, following the prompt message, "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items." The generated content is output from the visual generation device.

[0219] Step 5:

[0220] The server transmits the generated visual content to the user terminal via a presentation device and displays it on the visual device. The terminal then presents this content to the user through smart glasses or similar devices. This allows the user to visually confirm personalized product information.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is a system that efficiently suggests fashion items based on user preferences. This system mainly consists of a server, terminals, and users.

[0238] User actions:

[0239] Users will be able to easily input their preferences and requirements through an application on their device. Items selectable at this stage include color, style, brand, size, and budget.

[0240] Data collection and processing:

[0241] The server regularly retrieves data on new products from partner fashion brands and online stores. This data includes a wide range of information, such as product name, category, price, and release date.

[0242] The server uses AI to generate high-quality visual content based on the collected data, making it easier for users to visually recognize products.

[0243] Product recommendations and notifications:

[0244] The server uses the central algorithm of this invention to identify products based on user preferences and conditions and create an appropriate list. It also analyzes past user behavior data to further recommend highly relevant products.

[0245] The server uses natural language processing to analyze user reviews and feedback and extract current trend information. This information is then used to provide more accurate recommendations.

[0246] The device receives notifications sent from the server and displays a list of products on the user's screen. The user can then review the displayed products in detail and consider purchasing them.

[0247] Specific example:

[0248] For example, if a user registers their search criteria as "a simple design, blue bag, and under 10,000 yen," the server extracts matching items from partner stores' data based on those criteria. It then uses AI to generate product images and create a recommendation list. The device then notifies the user of this list and provides an interface to encourage purchase.

[0249] In this way, the system has a mechanism in which each element works together to streamline the user experience and reduce stress.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The user launches the application using their device and accesses a screen where they can enter their fashion preferences and requirements. Here, they select or enter information such as color, style, brand, size, and budget.

[0253] Step 2:

[0254] The device receives information about the user's preferences and conditions, sends it to the server, and saves it as a profile.

[0255] Step 3:

[0256] The server periodically collects information on new products via APIs from partner brands and online stores. This information includes product name, category, price, release date, color, and availability.

[0257] Step 4:

[0258] The server uses collected data on new products and employs generative AI to generate visual content for those products. The images and short videos generated at this stage are then processed to be visually appealing and easy to view.

[0259] Step 5:

[0260] The server filters and scores product data based on the user's preferences, past purchase history, and browsing history, and lists the most relevant products for each user.

[0261] Step 6:

[0262] The server uses natural language processing to collect and analyze user reviews and feedback data, and extracts new trends and potential needs from this information.

[0263] Step 7:

[0264] The server generates a list of products relevant to the user and sends it to the device as a push notification. This notification includes a product overview and links.

[0265] Step 8:

[0266] The device notifies the user of received notifications and displays a list of related products on the screen. The user can select products of interest from this list and view details.

[0267] (Example 1)

[0268] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] Traditional fashion recommendation systems have struggled to accurately reflect diverse user preferences and criteria, making it difficult to improve the user experience. Furthermore, there were limitations in the quality of visual recognition of products and the provision of highly relevant items. This resulted in decreased user satisfaction.

[0270] 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.

[0271] In this invention, the server includes means for acquiring user preferences and conditions via an information processing device, means for generating visual content based on the acquired data, and means for analyzing feedback and evaluation information and extracting trends. This makes it possible to recommend high-quality fashion items that meet the diverse preferences of users.

[0272] "Means for obtaining user preferences and conditions via information processing equipment" refers to functions and technologies that allow users to input their preferences and requests into a system and for the system to recognize them.

[0273] "Means of acquiring data on new products" refers to functions and technologies for collecting the latest product information from partner stores and online platforms.

[0274] "Generative means for generating visual content" refers to technologies for creating content that is easily recognizable to users visually, based on collected product information.

[0275] "Means for selecting and listing products" refers to functions and technologies that select products that match the user's preferences and criteria and present them in a list format.

[0276] "Selection methods for suggesting relevant products based on history" refers to functions and technologies that analyze past user behavior records and recommend highly relevant products to users based on that information.

[0277] "Means for analyzing feedback and evaluation information and extracting trends" refers to technologies for analyzing opinions and evaluations submitted by users and extracting trends and patterns from them.

[0278] "Means of notifying users of product information" refers to functions and technologies that efficiently convey a list of selected products and related information to users.

[0279] The embodiments for carrying out the present invention are as follows.

[0280] The present invention is composed of a server, a terminal, and a user in order to efficiently recommend fashion items based on the user's preferences. The user uses the terminal to input their preferences and conditions into the application. This application can obtain information such as color, style, brand, size, and budget.

[0281] The server obtains product data from partner online stores and brands. This data includes various information such as product names, categories, prices, release dates, etc. The server stores this in a database and updates it as needed. Also, the server uses a generative AI model to generate visual content of products that meet the user's conditions. At this time, a prompt sentence such as "Please generate an image of a simple blue skirt" is input into the AI model.

[0282] The server extracts trend information by analyzing reviews and feedback using natural language processing technology based on the data collected from users. Thereby, a list for recommending items suitable for the user's preferences can be created. Past user behavior data is also incorporated into the recommendation.

[0283] The product list sent from the server to the terminal is notified to the user by the terminal. The terminal displays the received product information on the user's screen, enabling the user to check the details and consider purchasing. Thereby, a system that improves the user's shopping experience and reduces stress is realized.

[0284] The flow of specific processing in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user inputs their preferences and conditions through an application on the terminal. The information input includes color, style, brand, size, and budget. The input information is sent to the server. The input data at this point is the user's individual preference information, based on which the server starts processing.

[0287] Step 2:

[0288] The server uses the acquired user preference information to collect product data through APIs from partnered online stores and brands. The product data includes product name, category, price, release date, etc. The server organizes these data and stores them in a database. The input is product information, and the output is a structured database. This database is used for processing in the next step.

[0289] Step 3:

[0290] The server filters the product data that matches the user's preferences and uses a generated AI model to generate relevant visual content. Instructions such as "Please generate an image of a simple blue skirt" are input into the AI model as prompt text. The AI generates product images based on this and prepares to provide them to the user. The input is the filtered product information, and the output is high-quality visual content.

[0291] Step 4:

[0292] The server creates a product list that is optimal for the user's preferences based on the generated visual content. This list is also analyzed in combination with past user behavior data to improve the recommendation degree. Trend information is extracted by analyzing user review and feedback data using natural language processing technology. The input at this point is the user preference information and behavior history, and the output is a refined product list.

[0293] Step 5:

[0294] The terminal displays a list of products received from the server to the user's screen. Upon receiving the notification, the user reviews the product information and generated visual content displayed on the screen and begins the process of considering a purchase. The input is the notification data from the server, and the output is the display result of the user interface. Through this process, the user can efficiently select the desired product.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] In modern retail, consumers are required to efficiently find products that suit their preferences from a wide variety of options. At the same time, there is a growing demand to visually examine products and experience the feeling of trying them on from the comfort of their homes, without having to visit physical stores. Traditional online shopping platforms, however, are limited to simply displaying product images, lacking the visual experience and the feeling of trying things on, thus failing to adequately stimulate consumer purchasing intent.

[0298] 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.

[0299] In this invention, the server includes data collection means for collecting newly released products based on the user's preferences and conditions, generation means for generating visual content based on the collected data, and virtual try-on means for trying on products in a virtual space. This allows users to have a detailed visual product experience from the comfort of their homes and efficiently find products that suit their preferences.

[0300] An "information input method" is a mechanism that allows users to input their preferences and conditions into a terminal.

[0301] "Data collection means" is a method for obtaining data on newly released products from partner sellers.

[0302] "Generation means" is a method for generating visual content of products based on the collected data.

[0303] "List creation means" is a method for identifying products that match the user's preferences and conditions and organizing them into a list.

[0304] "Recommendation means" is a method for selecting relevant products based on past user behavior.

[0305] "Analysis means" is a method for analyzing opinions and evaluations from users and extracting recent trends.

[0306] "Notification means" is a method for informing users of the information on the listed products.

[0307] "Virtual try-on means" is a mechanism that allows users to experience trying on products within a virtual space.

[0308] This system is designed to propose appropriate fashion items based on the user's preferences and conditions. The system mainly consists of a server, a terminal, and a user.

[0309] The server uses data collection means to obtain data on new products from partner sellers or online platforms. This data includes information such as product names, categories, prices, release dates, etc. The server further uses a generation AI model to generate high-quality visual content based on these product data. The generated visual content enables users to intuitively grasp the features of the products. For example, as a prompt sentence for the generation AI model, content such as "Please generate a visual of a casual jacket for autumn and winter based on the user's preferences" can be considered.

[0310] The device receives user preferences and criteria through a simple interface. These include selections such as color, style, brand, size, and budget. The device also receives notifications from the server and displays a list of product information to the user. This display allows the user to review product details and simulate trying on items using virtual try-on features.

[0311] Users can use their devices to view a list of products and virtually try them on, allowing them to choose the product that best suits them from the available options. The data collected through user interaction is used for analysis to improve the accuracy of recommendations in the future.

[0312] This system achieves efficient data computation and processing by utilizing machine learning libraries such as TensorFlow and PyTorch for data processing, and Unity and Unreal Engine for visual content generation.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The user launches an application on their device and enters their fashion preferences and requirements. This input data includes items such as color, style, brand, size, and budget. The device then sends the entered data to the server.

[0316] Step 2:

[0317] The server collects data on new products from partner sellers using data collection methods. This collected data includes product names, categories, prices, and release dates. The server systematically organizes this data and searches for products that match the user's input preferences.

[0318] Step 3:

[0319] The server uses a generative AI model to generate visual content based on the collected product data. In this step, prompts are created based on the collected product labels and category information and input into the model. For example, by setting the prompt to "Generate a visual of a casual, autumn / winter blue jacket," the server will output an image that specifically visualizes the product's characteristics.

[0320] Step 4:

[0321] The server uses the generated visual content to create a product list based on the user's preferences and criteria. This list matching process also utilizes past user behavior data and trend analysis data to suggest more relevant products.

[0322] Step 5:

[0323] The server sends a list of product information to the user's terminal using a notification system. The terminal displays the received data on its screen, providing an interface that allows the user to check product details on the spot.

[0324] Step 6:

[0325] Users can virtually try on selected items by operating a device. The device utilizes Unity or Unreal Engine to project the products into a virtual space, providing users with a feeling close to actual try-on.

[0326] Step 7:

[0327] User activity history and feedback are analyzed to improve the accuracy of future recommendations. The server uses this feedback data to retrain machine learning models to improve the accuracy of product recommendations tailored to the user profile.

[0328] 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.

[0329] This invention is a system that efficiently suggests relevant fashion items based on the user's preferences and conditions, and this system further incorporates an emotion engine. The system mainly consists of a server, terminals, and users, and comprises several main functions: interface, data collection and processing, emotion analysis, recommendation, and result notification.

[0330] User input and sentiment analysis:

[0331] Users can input their fashion preferences and requirements using an application on their device. This information is collected in real time and is powered by an emotion engine.

[0332] The device recognizes emotions not only from the user's input data but also from their actions, facial expressions, and voice during the input process, and sends this information to the server. The emotion engine analyzes this data to understand the user's current emotional state.

[0333] Data collection and content generation.

[0334] The server regularly collects the latest product information from partner fashion brands and online stores. This includes product names, categories, prices, release dates, colors, and availability.

[0335] Furthermore, the server uses a generation AI to create visual content for collected new products. This content is adjusted according to the user's emotional state and presented in a more visually appealing way.

[0336] Recommendations and notifications:

[0337] The server lists the most suitable products based on the user's preferences, conditions, emotional state, and past user behavior data. By leveraging the output of the emotion engine, product recommendations are more personalized, enhancing the user experience.

[0338] The device receives a list of products sent from the server and notifies the user of this information via push notification. By clicking on this notification, the user can access detailed product information and consider making a purchase.

[0339] Specific example:

[0340] For example, if a user sets the conditions as "casual style, red items, and a budget of under 5,000 yen," and the emotion engine also detects that the user is in a relaxed mood while viewing content, the server will extract casual red fashion items that match the conditions and generate content with a color scheme and background that promotes emotional relaxation. The device will then notify the user of this information, further enriching the shopping experience.

[0341] This invention, by combining emotion recognition technology, enables superior user adaptability compared to conventional systems and supports purchasing decisions. Furthermore, the application of visual content enhances the appeal of the information provided, thereby increasing user engagement.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user launches the application on their device and is taken to a screen where they enter their fashion preferences (e.g., color, style, brand) and requirements (e.g., budget, size). The data entered here is recorded as the user's profile information.

[0345] Step 2:

[0346] The device sends profile data, including user input information, to the server. During this process, the emotion engine analyzes the user's facial expressions and voice while they are typing to recognize their emotional state.

[0347] Step 3:

[0348] The server collects data on new products (e.g., product name, price, color, release date, etc.) from partner fashion brands and online stores via APIs. This information serves as the foundational data for content generation and recommendations by the generative AI.

[0349] Step 4:

[0350] The server takes in the analysis results from the emotion engine and uses generative AI to generate visual content that is appropriate for the user's emotional state. For example, if the user is relaxed, it will select displays with soft colors and simple designs.

[0351] Step 5:

[0352] The server filters and lists relevant products based on the user's preferences, criteria, past browsing history, and sentiment data, and then uses a recommendation algorithm to select the most suitable product.

[0353] Step 6:

[0354] The server sends the generated recommendation list, along with visual content of the product information, as a push notification to the user's device.

[0355] Step 7:

[0356] The device receives notifications from the server and displays a product list on the user's screen. The user can select products of interest from the list and view detailed information, ensuring a seamless shopping experience.

[0357] (Example 2)

[0358] 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".

[0359] In modern online shopping, users often struggle to find products that match their preferences and criteria. Furthermore, the lack of personalized product recommendations limits the user experience. Solving these challenges and improving the user experience is crucial.

[0360] 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.

[0361] In this invention, the server includes information input means for inputting the user's hobbies and conditions, emotion analysis means for analyzing the user's emotions based on the user's hobbies and conditions, and information collection means for collecting new product information based on the user's hobbies, conditions, and emotions. This makes it possible to suggest products based on the emotional state and past behavior of individual users.

[0362] "Information input means" refers to devices or methods for users to input their hobbies and preferences.

[0363] "Emotion analysis means" refers to devices or methods that identify and analyze emotions from user input information and their actions at that time.

[0364] "Information gathering means" refers to devices or methods for obtaining new product information from external sources.

[0365] "Generation means" refers to devices or methods that generate content visually or in other forms based on collected data and analysis results.

[0366] A "listing method" is a device or method for identifying and neatly arranging products that suit a user's tastes, conditions, and feelings.

[0367] A "recommendation tool" is a device or method that suggests relevant products to a user based on their past user behavior and current analysis results.

[0368] "Notification means" refers to devices or methods for informing users of listed product information.

[0369] An "artificial intelligence algorithm" is a program that enables a machine to learn and autonomously optimize itself.

[0370] "User preferences" refer to the tastes that users have for a particular genre or style.

[0371] "Conditions" refer to specific requirements or constraints set by the user.

[0372] This invention is a system that analyzes a user's emotions based on their hobbies and circumstances, and provides personalized product recommendations. The system consists of a server, terminals, and users, and combines various technologies to enhance the user experience.

[0373] User input and sentiment analysis

[0374] Users utilize an application on their smart devices to input their fashion preferences and criteria. For example, they might enter "casual style," "red items," and "budget under 5,000 yen."

[0375] The device uses sensors such as cameras and microphones to monitor the user's facial expressions and voice, and analyzes this data using emotion analysis technology. This analysis allows the user's emotional state to be deciphered in real time, and the collected data is sent to a server.

[0376] Data collection and content generation

[0377] The server periodically retrieves the latest product information from partner online platforms via APIs. This information includes product name, category, price, and stock availability.

[0378] The server then uses a generative AI model to generate visual content based on the collected data and the user's emotional state. This generation process utilizes the prompt "I'd like suggestions for casual red items under 5,000 yen in a relaxed atmosphere" to create content that is more relevant to the user.

[0379] Notifications and product suggestions

[0380] The device receives a product list generated from the server and displays it to the user as a push notification. When the user opens the notification, product details and purchase options are provided.

[0381] This system allows users to receive product suggestions tailored to their own emotions and preferences, enriching their shopping experience. This leads to increased engagement and support for purchasing decisions.

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

[0383] Step 1:

[0384] The user launches an application on their smart device and enters their fashion preferences and criteria. This input includes style type, color, price range, etc. Once this information is entered, the device receives the data, formats it appropriately, and prepares it for the next processing step.

[0385] Step 2:

[0386] The device collects user facial expressions and voice data using its camera and microphone, along with user input. This data is analyzed by an emotion analysis engine to extract the user's emotional state (e.g., relaxed state). This emotional information is then transferred to the server as specific parameters.

[0387] Step 3:

[0388] The server receives data on the user's hobbies, preferences, and emotional state from the terminal as input. The server uses APIs from partner online platforms to collect product information that matches the criteria (e.g., casual red items, priced under 5,000 yen). This information is stored in a database and used in the next generation step.

[0389] Step 4:

[0390] The server uses a generative AI model to generate visual content based on collected product data and user sentiment information. This process uses the prompt "Please suggest casual red items for under 5,000 yen in a relaxed atmosphere." The generated content features color schemes and designs that match the user's emotional state.

[0391] Step 5:

[0392] The device receives visual content and product lists sent from the server and provides suggestions to the user via push notifications. When the user taps the notification, they can access detailed information. At this time, a visually appealing user interface is displayed. The user can further review the products and make a purchase decision on the spot.

[0393] (Application Example 2)

[0394] 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."

[0395] In modern fashion shopping, there is a challenge in recommending products that suit the diverse preferences and emotions of consumers. Therefore, there is a growing demand for systems that efficiently and individually suggest products suitable for users, thereby increasing their purchasing intent. Furthermore, there is a need to improve the user experience through visually appealing content. This invention aims to solve these problems.

[0396] 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.

[0397] In this invention, the server includes data input means for inputting user preferences and conditions, product information collection means for collecting product information, visual generation means for generating visual content for products, and emotion analysis means for recognizing emotional states and personalizing product recommendations. This makes it possible to find products that match the consumer's emotions and preferences and provide a highly personalized shopping experience. Furthermore, the purchasing experience can be further enriched by intuitively presenting product information to the user through a visual device.

[0398] A "data input means" is a mechanism for inputting information necessary to collect user preferences and conditions.

[0399] A "product information gathering mechanism" is a system for efficiently collecting information about newly released products.

[0400] A "visual generation means" is a mechanism for creating visually appealing content based on collected product information.

[0401] A "product identification method" is a mechanism for finding, organizing, and providing products that match the user's preferences and conditions.

[0402] An "emotional analysis tool" is a mechanism that analyzes a user's emotional state and uses that information to personalize product recommendations.

[0403] A "recommendation system" is a mechanism for suggesting relevant products based on past user behavior data.

[0404] A "presentation means" is a mechanism that uses visual devices to directly and intuitively present product information to the user.

[0405] A specific embodiment of this invention is a system realized through the cooperation of a user, a terminal, and a server. The user can input their fashion preferences and requirements using smart glasses or a device and receive information visually. This information is collected through data input means.

[0406] The device detects information entered by the user, as well as facial expressions and voice, and analyzes them using emotion analysis tools. For example, emotion analysis AI software is used to analyze the user's emotional state in real time based on available data and send the results to a server.

[0407] The server stores product information obtained from partner fashion providers through product information collection methods and uses machine learning and generative AI to create personalized visual content using visual generation methods. This generation process is controlled using prompts that respond to the user's preferences and emotions. Specifically, prompts such as "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items to the generative AI model" are used.

[0408] The visual content generated in this way is presented to the user on a display via a visual device through a presentation means. This allows the user to easily confirm visual content that matches their emotional state and make the purchasing decision process more intuitive.

[0409] This system provides suggestions tailored to the user's emotions and preferences, improving consumer satisfaction. Furthermore, the visual presentation of products goes beyond mere screen display, offering users a richer and more interactive shopping experience.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] Users input their fashion preferences and requirements using smart glasses or devices. The entered information is collected through a data entry device. The data entry device receives the user's input and transmits it to the terminal as digital data.

[0413] Step 2:

[0414] The device senses the user's facial expressions and voice data in addition to the data entered by the user, and analyzes this data using emotion analysis tools. The device receives sensor data obtained from the camera and microphone as input and uses emotion analysis AI software to analyze the user's emotional state in real time. The analyzed emotion data is sent to a server.

[0415] Step 3:

[0416] The server periodically retrieves product information from external fashion providers using product information collection methods. This includes data such as product name, category, and price. The server stores this information in a database.

[0417] Step 4:

[0418] The server generates visual content using a generative AI model based on data obtained from emotion analysis, user preferences and conditions, and accumulated product information. Specifically, it creates visual content that matches the conditions, following the prompt message, "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items." The generated content is output from the visual generation device.

[0419] Step 5:

[0420] The server transmits the generated visual content to the user terminal via a presentation device and displays it on the visual device. The terminal then presents this content to the user through smart glasses or similar devices. This allows the user to visually confirm personalized product information.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention is a system that efficiently suggests fashion items based on user preferences. This system mainly consists of a server, terminals, and users.

[0438] User actions:

[0439] Users will be able to easily input their preferences and requirements through an application on their device. Items selectable at this stage include color, style, brand, size, and budget.

[0440] Data collection and processing:

[0441] The server regularly retrieves data on new products from partner fashion brands and online stores. This data includes a wide range of information, such as product name, category, price, and release date.

[0442] The server uses AI to generate high-quality visual content based on the collected data, making it easier for users to visually recognize products.

[0443] Product recommendations and notifications:

[0444] The server uses the central algorithm of this invention to identify products based on user preferences and conditions and create an appropriate list. It also analyzes past user behavior data to further recommend highly relevant products.

[0445] The server uses natural language processing to analyze user reviews and feedback and extract current trend information. This information is then used to provide more accurate recommendations.

[0446] The device receives notifications sent from the server and displays a list of products on the user's screen. The user can then review the displayed products in detail and consider purchasing them.

[0447] Specific example:

[0448] For example, if a user registers their search criteria as "a simple design, blue bag, and under 10,000 yen," the server extracts matching items from partner stores' data based on those criteria. It then uses AI to generate product images and create a recommendation list. The device then notifies the user of this list and provides an interface to encourage purchase.

[0449] In this way, the system has a mechanism in which each element works together to streamline the user experience and reduce stress.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The user launches the application using their device and accesses a screen where they can enter their fashion preferences and requirements. Here, they select or enter information such as color, style, brand, size, and budget.

[0453] Step 2:

[0454] The device receives information about the user's preferences and conditions, sends it to the server, and saves it as a profile.

[0455] Step 3:

[0456] The server periodically collects information on new products via APIs from partner brands and online stores. This information includes product name, category, price, release date, color, and availability.

[0457] Step 4:

[0458] The server uses collected data on new products and employs generative AI to generate visual content for those products. The images and short videos generated at this stage are then processed to be visually appealing and easy to view.

[0459] Step 5:

[0460] The server filters and scores product data based on the user's preferences, past purchase history, and browsing history, and lists the most relevant products for each user.

[0461] Step 6:

[0462] The server uses natural language processing to collect and analyze user reviews and feedback data, and extracts new trends and potential needs from this information.

[0463] Step 7:

[0464] The server generates a list of products relevant to the user and sends it to the device as a push notification. This notification includes a product overview and links.

[0465] Step 8:

[0466] The device notifies the user of received notifications and displays a list of related products on the screen. The user can select products of interest from this list and view details.

[0467] (Example 1)

[0468] 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."

[0469] Traditional fashion recommendation systems have struggled to accurately reflect diverse user preferences and criteria, making it difficult to improve the user experience. Furthermore, there were limitations in the quality of visual recognition of products and the provision of highly relevant items. This resulted in decreased user satisfaction.

[0470] 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.

[0471] In this invention, the server includes means for acquiring user preferences and conditions via an information processing device, means for generating visual content based on the acquired data, and means for analyzing feedback and evaluation information and extracting trends. This makes it possible to recommend high-quality fashion items that meet the diverse preferences of users.

[0472] "Means for obtaining user preferences and conditions via information processing equipment" refers to functions and technologies that allow users to input their preferences and requests into a system and for the system to recognize them.

[0473] "Means of acquiring data on new products" refers to functions and technologies for collecting the latest product information from partner stores and online platforms.

[0474] "Generative means for generating visual content" refers to technologies for creating content that is easily recognizable to users visually, based on collected product information.

[0475] "Means for selecting and listing products" refers to functions and technologies that select products that match the user's preferences and criteria and present them in a list format.

[0476] "Selection methods for suggesting relevant products based on history" refers to functions and technologies that analyze past user behavior records and recommend highly relevant products to users based on that information.

[0477] "Means for analyzing feedback and evaluation information and extracting trends" refers to technologies for analyzing opinions and evaluations submitted by users and extracting trends and patterns from them.

[0478] "Means of notifying users of product information" refers to functions and technologies that efficiently convey a list of selected products and related information to users.

[0479] The embodiments for carrying out the present invention are as follows.

[0480] This invention comprises a server, a terminal, and a user to efficiently recommend fashion items based on user preferences. The user inputs their preferences and criteria into an application using the terminal. This application can retrieve information such as color, style, brand, size, and budget.

[0481] The server retrieves product data from partner online stores and brands. This data includes various information such as product name, category, price, and release date. The server stores this data in a database and updates it as needed. The server also uses a generative AI model to generate visual content for products that match the user's criteria. In this process, the AI ​​model is given prompts such as, "Generate an image of a simple blue skirt."

[0482] The server extracts trend information by analyzing reviews and feedback using natural language processing technology based on data collected from users. This allows it to create recommendation lists of items that are suitable for the user's preferences. Past user behavior data is also incorporated into the recommendations.

[0483] The product list sent from the server to the terminal is then notified to the user by the terminal. The terminal displays the product information received on the user's screen, allowing the user to review the details and consider purchasing. This creates a system that improves the user's shopping experience and reduces stress.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] The user enters their preferences and criteria using an application on their device. This information includes color, style, brand, size, and budget. The entered information is sent to the server. At this point, the entered data represents the user's individual preferences, and the server begins processing based on this information.

[0487] Step 2:

[0488] The server uses acquired user preference information to collect product data from partner online stores and brands via APIs. This product data includes product name, category, price, and release date. The server organizes this data and stores it in a database. The input is product information, and the output is a structured database. This database is used for processing in the next step.

[0489] Step 3:

[0490] The server filters product data to match the user's preferences and uses a generative AI model to generate relevant visual content. The AI ​​model receives a prompt such as, "Generate an image of a simple blue skirt." Based on this, the AI ​​generates product images and prepares them for the user. The input is filtered product information, and the output is high-quality visual content.

[0491] Step 4:

[0492] The server creates a product list best suited to the user's preferences based on the generated visual content. This list is also analyzed in conjunction with past user behavior data to improve recommendation accuracy. Natural language processing techniques are used to analyze user reviews and feedback data to extract trend information. At this point, the input is user preference information and behavioral history, and the output is a refined product list.

[0493] Step 5:

[0494] The terminal displays a list of products received from the server to the user's screen. Upon receiving the notification, the user reviews the product information and generated visual content displayed on the screen and begins the process of considering a purchase. The input is the notification data from the server, and the output is the display result of the user interface. Through this process, the user can efficiently select the desired product.

[0495] (Application Example 1)

[0496] 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."

[0497] In modern retail, consumers are required to efficiently find products that suit their preferences from a wide variety of options. At the same time, there is a growing demand to visually examine products and experience the feeling of trying them on from the comfort of their homes, without having to visit physical stores. Traditional online shopping platforms, however, are limited to simply displaying product images, lacking the visual experience and the feeling of trying things on, thus failing to adequately stimulate consumer purchasing intent.

[0498] 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.

[0499] In this invention, the server includes data collection means for collecting newly released products based on the user's preferences and conditions, generation means for generating visual content based on the collected data, and virtual try-on means for trying on products in a virtual space. This allows users to have a detailed visual product experience from the comfort of their homes and efficiently find products that suit their preferences.

[0500] An "information input method" is a mechanism that allows users to input their preferences and conditions into a terminal.

[0501] "Data collection methods" refer to methods for obtaining data on newly released products from partner sellers.

[0502] "Generation method" refers to a method of generating visual content for a product based on collected data.

[0503] A "list creation method" is a method of identifying products that match the user's preferences and conditions and organizing them into a list.

[0504] "Recommendation methods" refer to methods of selecting relevant products based on past user behavior.

[0505] "Analysis methods" refer to methods of analyzing user opinions and evaluations to extract recent trends.

[0506] "Notification method" refers to a method of informing users of a list of product information.

[0507] A "virtual try-on method" is a system that allows users to try on products in a virtual space.

[0508] This system is designed to suggest appropriate fashion items based on the user's preferences and circumstances. The system mainly consists of a server, terminals, and users.

[0509] The server uses data collection methods to acquire data on new products from partner sellers and online platforms. This data includes information such as product name, category, price, and release date. The server then uses a generative AI model to generate high-quality visual content based on this product data. The generated visual content allows users to intuitively understand the features of the product. For example, a prompt to the generative AI model might be, "Generate a visual of a casual jacket for autumn / winter based on the user's preferences."

[0510] The device receives user preferences and criteria through a simple interface. These include selections such as color, style, brand, size, and budget. The device also receives notifications from the server and displays a list of product information to the user. This display allows the user to review product details and simulate trying on items using virtual try-on features.

[0511] Users can use their devices to view a list of products and virtually try them on, allowing them to choose the product that best suits them from the available options. The data collected through user interaction is used for analysis to improve the accuracy of recommendations in the future.

[0512] This system achieves efficient data computation and processing by utilizing machine learning libraries such as TensorFlow and PyTorch for data processing, and Unity and Unreal Engine for visual content generation.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The user launches an application on their device and enters their fashion preferences and requirements. This input data includes items such as color, style, brand, size, and budget. The device then sends the entered data to the server.

[0516] Step 2:

[0517] The server collects data on new products from partner sellers using data collection methods. This collected data includes product names, categories, prices, and release dates. The server systematically organizes this data and searches for products that match the user's input preferences.

[0518] Step 3:

[0519] The server uses a generative AI model to generate visual content based on the collected product data. In this step, prompts are created based on the collected product labels and category information and input into the model. For example, by setting the prompt to "Generate a visual of a casual, autumn / winter blue jacket," the server will output an image that specifically visualizes the product's characteristics.

[0520] Step 4:

[0521] The server uses the generated visual content to create a product list based on the user's preferences and criteria. This list matching process also utilizes past user behavior data and trend analysis data to suggest more relevant products.

[0522] Step 5:

[0523] The server sends a list of product information to the user's terminal using a notification system. The terminal displays the received data on its screen, providing an interface that allows the user to check product details on the spot.

[0524] Step 6:

[0525] Users can virtually try on selected items by operating a device. The device utilizes Unity or Unreal Engine to project the products into a virtual space, providing users with a feeling close to actual try-on.

[0526] Step 7:

[0527] User activity history and feedback are analyzed to improve the accuracy of future recommendations. The server uses this feedback data to retrain machine learning models to improve the accuracy of product recommendations tailored to the user profile.

[0528] 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.

[0529] This invention is a system that efficiently suggests relevant fashion items based on the user's preferences and conditions, and this system further incorporates an emotion engine. The system mainly consists of a server, terminals, and users, and comprises several main functions: interface, data collection and processing, emotion analysis, recommendation, and result notification.

[0530] User input and sentiment analysis:

[0531] Users can input their fashion preferences and requirements using an application on their device. This information is collected in real time and is powered by an emotion engine.

[0532] The device recognizes emotions not only from the user's input data but also from their actions, facial expressions, and voice during the input process, and sends this information to the server. The emotion engine analyzes this data to understand the user's current emotional state.

[0533] Data collection and content generation.

[0534] The server regularly collects the latest product information from partner fashion brands and online stores. This includes product names, categories, prices, release dates, colors, and availability.

[0535] Furthermore, the server uses a generation AI to create visual content for collected new products. This content is adjusted according to the user's emotional state and presented in a more visually appealing way.

[0536] Recommendations and notifications:

[0537] The server lists the most suitable products based on the user's preferences, conditions, emotional state, and past user behavior data. By leveraging the output of the emotion engine, product recommendations are more personalized, enhancing the user experience.

[0538] The device receives a list of products sent from the server and notifies the user of this information via push notification. By clicking on this notification, the user can access detailed product information and consider making a purchase.

[0539] Specific example:

[0540] For example, if a user sets the conditions as "casual style, red items, and a budget of under 5,000 yen," and the emotion engine also detects that the user is in a relaxed mood while viewing content, the server will extract casual red fashion items that match the conditions and generate content with a color scheme and background that promotes emotional relaxation. The device will then notify the user of this information, further enriching the shopping experience.

[0541] This invention, by combining emotion recognition technology, enables superior user adaptability compared to conventional systems and supports purchasing decisions. Furthermore, the application of visual content enhances the appeal of the information provided, thereby increasing user engagement.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The user launches the application on their device and is taken to a screen where they enter their fashion preferences (e.g., color, style, brand) and requirements (e.g., budget, size). The data entered here is recorded as the user's profile information.

[0545] Step 2:

[0546] The device sends profile data, including user input information, to the server. During this process, the emotion engine analyzes the user's facial expressions and voice while they are typing to recognize their emotional state.

[0547] Step 3:

[0548] The server collects data on new products (e.g., product name, price, color, release date, etc.) from partner fashion brands and online stores via APIs. This information serves as the foundational data for content generation and recommendations by the generative AI.

[0549] Step 4:

[0550] The server takes in the analysis results from the emotion engine and uses generative AI to generate visual content that is appropriate for the user's emotional state. For example, if the user is relaxed, it will select displays with soft colors and simple designs.

[0551] Step 5:

[0552] The server filters and lists relevant products based on the user's preferences, criteria, past browsing history, and sentiment data, and then uses a recommendation algorithm to select the most suitable product.

[0553] Step 6:

[0554] The server sends the generated recommendation list, along with visual content of the product information, as a push notification to the user's device.

[0555] Step 7:

[0556] The device receives notifications from the server and displays a product list on the user's screen. The user can select products of interest from the list and view detailed information, ensuring a seamless shopping experience.

[0557] (Example 2)

[0558] 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."

[0559] In modern online shopping, users often struggle to find products that match their preferences and criteria. Furthermore, the lack of personalized product recommendations limits the user experience. Solving these challenges and improving the user experience is crucial.

[0560] 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.

[0561] In this invention, the server includes information input means for inputting the user's hobbies and conditions, emotion analysis means for analyzing the user's emotions based on the user's hobbies and conditions, and information collection means for collecting new product information based on the user's hobbies, conditions, and emotions. This makes it possible to suggest products based on the emotional state and past behavior of individual users.

[0562] "Information input means" refers to devices or methods for users to input their hobbies and preferences.

[0563] "Emotion analysis means" refers to devices or methods that identify and analyze emotions from user input information and their actions at that time.

[0564] "Information gathering means" refers to devices or methods for obtaining new product information from external sources.

[0565] "Generation means" refers to devices or methods that generate content visually or in other forms based on collected data and analysis results.

[0566] A "listing method" is a device or method for identifying and neatly arranging products that suit a user's tastes, conditions, and feelings.

[0567] A "recommendation tool" is a device or method that suggests relevant products to a user based on their past user behavior and current analysis results.

[0568] "Notification means" refers to devices or methods for informing users of listed product information.

[0569] An "artificial intelligence algorithm" is a program that enables a machine to learn and autonomously optimize itself.

[0570] "User preferences" refer to the tastes that users have for a particular genre or style.

[0571] "Conditions" refer to specific requirements or constraints set by the user.

[0572] This invention is a system that analyzes a user's emotions based on their hobbies and circumstances, and provides personalized product recommendations. The system consists of a server, terminals, and users, and combines various technologies to enhance the user experience.

[0573] User input and sentiment analysis

[0574] Users utilize an application on their smart devices to input their fashion preferences and criteria. For example, they might enter "casual style," "red items," and "budget under 5,000 yen."

[0575] The device uses sensors such as cameras and microphones to monitor the user's facial expressions and voice, and analyzes this data using emotion analysis technology. This analysis allows the user's emotional state to be deciphered in real time, and the collected data is sent to a server.

[0576] Data collection and content generation

[0577] The server periodically retrieves the latest product information from partner online platforms via APIs. This information includes product name, category, price, and stock availability.

[0578] The server then uses a generative AI model to generate visual content based on the collected data and the user's emotional state. This generation process utilizes the prompt "I'd like suggestions for casual red items under 5,000 yen in a relaxed atmosphere" to create content that is more relevant to the user.

[0579] Notifications and product suggestions

[0580] The device receives a product list generated from the server and displays it to the user as a push notification. When the user opens the notification, product details and purchase options are provided.

[0581] This system allows users to receive product suggestions tailored to their own emotions and preferences, enriching their shopping experience. This leads to increased engagement and support for purchasing decisions.

[0582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0583] Step 1:

[0584] The user launches an application on their smart device and enters their fashion preferences and criteria. This input includes style type, color, price range, etc. Once this information is entered, the device receives the data, formats it appropriately, and prepares it for the next processing step.

[0585] Step 2:

[0586] The device collects user facial expressions and voice data using its camera and microphone, along with user input. This data is analyzed by an emotion analysis engine to extract the user's emotional state (e.g., relaxed state). This emotional information is then transferred to the server as specific parameters.

[0587] Step 3:

[0588] The server receives data on the user's hobbies, preferences, and emotional state from the terminal as input. The server uses APIs from partner online platforms to collect product information that matches the criteria (e.g., casual red items, priced under 5,000 yen). This information is stored in a database and used in the next generation step.

[0589] Step 4:

[0590] The server uses a generative AI model to generate visual content based on collected product data and user sentiment information. This process uses the prompt "Please suggest casual red items for under 5,000 yen in a relaxed atmosphere." The generated content features color schemes and designs that match the user's emotional state.

[0591] Step 5:

[0592] The device receives visual content and product lists sent from the server and provides suggestions to the user via push notifications. When the user taps the notification, they can access detailed information. At this time, a visually appealing user interface is displayed. The user can further review the products and make a purchase decision on the spot.

[0593] (Application Example 2)

[0594] 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."

[0595] In modern fashion shopping, there is a challenge in recommending products that suit the diverse preferences and emotions of consumers. Therefore, there is a growing demand for systems that efficiently and individually suggest products suitable for users, thereby increasing their purchasing intent. Furthermore, there is a need to improve the user experience through visually appealing content. This invention aims to solve these problems.

[0596] 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.

[0597] In this invention, the server includes data input means for inputting user preferences and conditions, product information collection means for collecting product information, visual generation means for generating visual content for products, and emotion analysis means for recognizing emotional states and personalizing product recommendations. This makes it possible to find products that match the consumer's emotions and preferences and provide a highly personalized shopping experience. Furthermore, the purchasing experience can be further enriched by intuitively presenting product information to the user through a visual device.

[0598] A "data input means" is a mechanism for inputting information necessary to collect user preferences and conditions.

[0599] A "product information gathering mechanism" is a system for efficiently collecting information about newly released products.

[0600] A "visual generation means" is a mechanism for creating visually appealing content based on collected product information.

[0601] A "product identification method" is a mechanism for finding, organizing, and providing products that match the user's preferences and conditions.

[0602] An "emotional analysis tool" is a mechanism that analyzes a user's emotional state and uses that information to personalize product recommendations.

[0603] A "recommendation system" is a mechanism for suggesting relevant products based on past user behavior data.

[0604] A "presentation means" is a mechanism that uses visual devices to directly and intuitively present product information to the user.

[0605] A specific embodiment of this invention is a system realized through the cooperation of a user, a terminal, and a server. The user can input their fashion preferences and requirements using smart glasses or a device and receive information visually. This information is collected through data input means.

[0606] The device detects information entered by the user, as well as facial expressions and voice, and analyzes them using emotion analysis tools. For example, emotion analysis AI software is used to analyze the user's emotional state in real time based on available data and send the results to a server.

[0607] The server stores product information obtained from partner fashion providers through product information collection methods and uses machine learning and generative AI to create personalized visual content using visual generation methods. This generation process is controlled using prompts that respond to the user's preferences and emotions. Specifically, prompts such as "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items to the generative AI model" are used.

[0608] The visual content generated in this way is presented to the user on a display via a visual device through a presentation means. This allows the user to easily confirm visual content that matches their emotional state and make the purchasing decision process more intuitive.

[0609] This system provides suggestions tailored to the user's emotions and preferences, improving consumer satisfaction. Furthermore, the visual presentation of products goes beyond mere screen display, offering users a richer and more interactive shopping experience.

[0610] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0611] Step 1:

[0612] Users input their fashion preferences and requirements using smart glasses or devices. The entered information is collected through a data entry device. The data entry device receives the user's input and transmits it to the terminal as digital data.

[0613] Step 2:

[0614] The device senses the user's facial expressions and voice data in addition to the data entered by the user, and analyzes this data using emotion analysis tools. The device receives sensor data obtained from the camera and microphone as input and uses emotion analysis AI software to analyze the user's emotional state in real time. The analyzed emotion data is sent to a server.

[0615] Step 3:

[0616] The server periodically retrieves product information from external fashion providers using product information collection methods. This includes data such as product name, category, and price. The server stores this information in a database.

[0617] Step 4:

[0618] The server generates visual content using a generative AI model based on data obtained from emotion analysis, user preferences and conditions, and accumulated product information. Specifically, it creates visual content that matches the conditions, following the prompt message, "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items." The generated content is output from the visual generation device.

[0619] Step 5:

[0620] The server transmits the generated visual content to the user terminal via a presentation device and displays it on the visual device. The terminal then presents this content to the user through smart glasses or similar devices. This allows the user to visually confirm personalized product information.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] This invention is a system that efficiently suggests fashion items based on user preferences. This system mainly consists of a server, terminals, and users.

[0639] User actions:

[0640] Users will be able to easily input their preferences and requirements through an application on their device. Items selectable at this stage include color, style, brand, size, and budget.

[0641] Data collection and processing:

[0642] The server regularly retrieves data on new products from partner fashion brands and online stores. This data includes a wide range of information, such as product name, category, price, and release date.

[0643] The server uses AI to generate high-quality visual content based on the collected data, making it easier for users to visually recognize products.

[0644] Product recommendations and notifications:

[0645] The server uses the central algorithm of this invention to identify products based on user preferences and conditions and create an appropriate list. It also analyzes past user behavior data to further recommend highly relevant products.

[0646] The server uses natural language processing to analyze user reviews and feedback and extract current trend information. This information is then used to provide more accurate recommendations.

[0647] The device receives notifications sent from the server and displays a list of products on the user's screen. The user can then review the displayed products in detail and consider purchasing them.

[0648] Specific example:

[0649] For example, if a user registers their search criteria as "a simple design, blue bag, and under 10,000 yen," the server extracts matching items from partner stores' data based on those criteria. It then uses AI to generate product images and create a recommendation list. The device then notifies the user of this list and provides an interface to encourage purchase.

[0650] In this way, the system has a mechanism in which each element works together to streamline the user experience and reduce stress.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user launches the application using their device and accesses a screen where they can enter their fashion preferences and requirements. Here, they select or enter information such as color, style, brand, size, and budget.

[0654] Step 2:

[0655] The device receives information about the user's preferences and conditions, sends it to the server, and saves it as a profile.

[0656] Step 3:

[0657] The server periodically collects information on new products via APIs from partner brands and online stores. This information includes product name, category, price, release date, color, and availability.

[0658] Step 4:

[0659] The server uses collected data on new products and employs generative AI to generate visual content for those products. The images and short videos generated at this stage are then processed to be visually appealing and easy to view.

[0660] Step 5:

[0661] The server filters and scores product data based on the user's preferences, past purchase history, and browsing history, and lists the most relevant products for each user.

[0662] Step 6:

[0663] The server uses natural language processing to collect and analyze user reviews and feedback data, and extracts new trends and potential needs from this information.

[0664] Step 7:

[0665] The server generates a list of products relevant to the user and sends it to the device as a push notification. This notification includes a product overview and links.

[0666] Step 8:

[0667] The device notifies the user of received notifications and displays a list of related products on the screen. The user can select products of interest from this list and view details.

[0668] (Example 1)

[0669] 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".

[0670] Traditional fashion recommendation systems have struggled to accurately reflect diverse user preferences and criteria, making it difficult to improve the user experience. Furthermore, there were limitations in the quality of visual recognition of products and the provision of highly relevant items. This resulted in decreased user satisfaction.

[0671] 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.

[0672] In this invention, the server includes means for acquiring user preferences and conditions via an information processing device, means for generating visual content based on the acquired data, and means for analyzing feedback and evaluation information and extracting trends. This makes it possible to recommend high-quality fashion items that meet the diverse preferences of users.

[0673] "Means for obtaining user preferences and conditions via information processing equipment" refers to functions and technologies that allow users to input their preferences and requests into a system and for the system to recognize them.

[0674] "Means of acquiring data on new products" refers to functions and technologies for collecting the latest product information from partner stores and online platforms.

[0675] "Generative means for generating visual content" refers to technologies for creating content that is easily recognizable to users visually, based on collected product information.

[0676] "Means for selecting and listing products" refers to functions and technologies that select products that match the user's preferences and criteria and present them in a list format.

[0677] "Selection methods for suggesting relevant products based on history" refers to functions and technologies that analyze past user behavior records and recommend highly relevant products to users based on that information.

[0678] "Means for analyzing feedback and evaluation information and extracting trends" refers to technologies for analyzing opinions and evaluations submitted by users and extracting trends and patterns from them.

[0679] "Means of notifying users of product information" refers to functions and technologies that efficiently convey a list of selected products and related information to users.

[0680] The embodiments for carrying out the present invention are as follows.

[0681] This invention comprises a server, a terminal, and a user to efficiently recommend fashion items based on user preferences. The user inputs their preferences and criteria into an application using the terminal. This application can retrieve information such as color, style, brand, size, and budget.

[0682] The server retrieves product data from partner online stores and brands. This data includes various information such as product name, category, price, and release date. The server stores this data in a database and updates it as needed. The server also uses a generative AI model to generate visual content for products that match the user's criteria. In this process, the AI ​​model is given prompts such as, "Generate an image of a simple blue skirt."

[0683] The server extracts trend information by analyzing reviews and feedback using natural language processing technology based on data collected from users. This allows it to create recommendation lists of items that are suitable for the user's preferences. Past user behavior data is also incorporated into the recommendations.

[0684] The product list sent from the server to the terminal is then notified to the user by the terminal. The terminal displays the product information received on the user's screen, allowing the user to review the details and consider purchasing. This creates a system that improves the user's shopping experience and reduces stress.

[0685] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0686] Step 1:

[0687] The user enters their preferences and criteria using an application on their device. This information includes color, style, brand, size, and budget. The entered information is sent to the server. At this point, the entered data represents the user's individual preferences, and the server begins processing based on this information.

[0688] Step 2:

[0689] The server uses acquired user preference information to collect product data from partner online stores and brands via APIs. This product data includes product name, category, price, and release date. The server organizes this data and stores it in a database. The input is product information, and the output is a structured database. This database is used for processing in the next step.

[0690] Step 3:

[0691] The server filters product data to match the user's preferences and uses a generative AI model to generate relevant visual content. The AI ​​model receives a prompt such as, "Generate an image of a simple blue skirt." Based on this, the AI ​​generates product images and prepares them for the user. The input is filtered product information, and the output is high-quality visual content.

[0692] Step 4:

[0693] The server creates a product list best suited to the user's preferences based on the generated visual content. This list is also analyzed in conjunction with past user behavior data to improve recommendation accuracy. Natural language processing techniques are used to analyze user reviews and feedback data to extract trend information. At this point, the input is user preference information and behavioral history, and the output is a refined product list.

[0694] Step 5:

[0695] The terminal displays a list of products received from the server to the user's screen. Upon receiving the notification, the user reviews the product information and generated visual content displayed on the screen and begins the process of considering a purchase. The input is the notification data from the server, and the output is the display result of the user interface. Through this process, the user can efficiently select the desired product.

[0696] (Application Example 1)

[0697] 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".

[0698] In modern retail, consumers are required to efficiently find products that suit their preferences from a wide variety of options. At the same time, there is a growing demand to visually examine products and experience the feeling of trying them on from the comfort of their homes, without having to visit physical stores. Traditional online shopping platforms, however, are limited to simply displaying product images, lacking the visual experience and the feeling of trying things on, thus failing to adequately stimulate consumer purchasing intent.

[0699] 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.

[0700] In this invention, the server includes data collection means for collecting newly released products based on the user's preferences and conditions, generation means for generating visual content based on the collected data, and virtual try-on means for trying on products in a virtual space. This allows users to have a detailed visual product experience from the comfort of their homes and efficiently find products that suit their preferences.

[0701] An "information input method" is a mechanism that allows users to input their preferences and conditions into a terminal.

[0702] "Data collection methods" refer to methods for obtaining data on newly released products from partner sellers.

[0703] "Generation method" refers to a method of generating visual content for a product based on collected data.

[0704] A "list creation method" is a method of identifying products that match the user's preferences and conditions and organizing them into a list.

[0705] "Recommendation methods" refer to methods of selecting relevant products based on past user behavior.

[0706] "Analysis methods" refer to methods of analyzing user opinions and evaluations to extract recent trends.

[0707] "Notification method" refers to a method of informing users of a list of product information.

[0708] A "virtual try-on method" is a system that allows users to try on products in a virtual space.

[0709] This system is designed to suggest appropriate fashion items based on the user's preferences and circumstances. The system mainly consists of a server, terminals, and users.

[0710] The server uses data collection methods to acquire data on new products from partner sellers and online platforms. This data includes information such as product name, category, price, and release date. The server then uses a generative AI model to generate high-quality visual content based on this product data. The generated visual content allows users to intuitively understand the features of the product. For example, a prompt to the generative AI model might be, "Generate a visual of a casual jacket for autumn / winter based on the user's preferences."

[0711] The device receives user preferences and criteria through a simple interface. These include selections such as color, style, brand, size, and budget. The device also receives notifications from the server and displays a list of product information to the user. This display allows the user to review product details and simulate trying on items using virtual try-on features.

[0712] Users can use their devices to view a list of products and virtually try them on, allowing them to choose the product that best suits them from the available options. The data collected through user interaction is used for analysis to improve the accuracy of recommendations in the future.

[0713] This system achieves efficient data computation and processing by utilizing machine learning libraries such as TensorFlow and PyTorch for data processing, and Unity and Unreal Engine for visual content generation.

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The user launches an application on their device and enters their fashion preferences and requirements. This input data includes items such as color, style, brand, size, and budget. The device then sends the entered data to the server.

[0717] Step 2:

[0718] The server collects data on new products from partner sellers using data collection methods. This collected data includes product names, categories, prices, and release dates. The server systematically organizes this data and searches for products that match the user's input preferences.

[0719] Step 3:

[0720] The server uses a generative AI model to generate visual content based on the collected product data. In this step, prompts are created based on the collected product labels and category information and input into the model. For example, by setting the prompt to "Generate a visual of a casual, autumn / winter blue jacket," the server will output an image that specifically visualizes the product's characteristics.

[0721] Step 4:

[0722] The server uses the generated visual content to create a product list based on the user's preferences and criteria. This list matching process also utilizes past user behavior data and trend analysis data to suggest more relevant products.

[0723] Step 5:

[0724] The server sends a list of product information to the user's terminal using a notification system. The terminal displays the received data on its screen, providing an interface that allows the user to check product details on the spot.

[0725] Step 6:

[0726] Users can virtually try on selected items by operating a device. The device utilizes Unity or Unreal Engine to project the products into a virtual space, providing users with a feeling close to actual try-on.

[0727] Step 7:

[0728] User activity history and feedback are analyzed to improve the accuracy of future recommendations. The server uses this feedback data to retrain machine learning models to improve the accuracy of product recommendations tailored to the user profile.

[0729] 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.

[0730] This invention is a system that efficiently suggests relevant fashion items based on the user's preferences and conditions, and this system further incorporates an emotion engine. The system mainly consists of a server, terminals, and users, and comprises several main functions: interface, data collection and processing, emotion analysis, recommendation, and result notification.

[0731] User input and sentiment analysis:

[0732] Users can input their fashion preferences and requirements using an application on their device. This information is collected in real time and is powered by an emotion engine.

[0733] The device recognizes emotions not only from the user's input data but also from their actions, facial expressions, and voice during the input process, and sends this information to the server. The emotion engine analyzes this data to understand the user's current emotional state.

[0734] Data collection and content generation.

[0735] The server regularly collects the latest product information from partner fashion brands and online stores. This includes product names, categories, prices, release dates, colors, and availability.

[0736] Furthermore, the server uses a generation AI to create visual content for collected new products. This content is adjusted according to the user's emotional state and presented in a more visually appealing way.

[0737] Recommendations and notifications:

[0738] The server lists the most suitable products based on the user's preferences, conditions, emotional state, and past user behavior data. By leveraging the output of the emotion engine, product recommendations are more personalized, enhancing the user experience.

[0739] The device receives a list of products sent from the server and notifies the user of this information via push notification. By clicking on this notification, the user can access detailed product information and consider making a purchase.

[0740] Specific example:

[0741] For example, if a user sets the conditions as "casual style, red items, and a budget of under 5,000 yen," and the emotion engine also detects that the user is in a relaxed mood while viewing content, the server will extract casual red fashion items that match the conditions and generate content with a color scheme and background that promotes emotional relaxation. The device will then notify the user of this information, further enriching the shopping experience.

[0742] This invention, by combining emotion recognition technology, enables superior user adaptability compared to conventional systems and supports purchasing decisions. Furthermore, the application of visual content enhances the appeal of the information provided, thereby increasing user engagement.

[0743] The following describes the processing flow.

[0744] Step 1:

[0745] The user launches the application on their device and is taken to a screen where they enter their fashion preferences (e.g., color, style, brand) and requirements (e.g., budget, size). The data entered here is recorded as the user's profile information.

[0746] Step 2:

[0747] The device sends profile data, including user input information, to the server. During this process, the emotion engine analyzes the user's facial expressions and voice while they are typing to recognize their emotional state.

[0748] Step 3:

[0749] The server collects data on new products (e.g., product name, price, color, release date, etc.) from partner fashion brands and online stores via APIs. This information serves as the foundational data for content generation and recommendations by the generative AI.

[0750] Step 4:

[0751] The server takes in the analysis results from the emotion engine and uses generative AI to generate visual content that is appropriate for the user's emotional state. For example, if the user is relaxed, it will select displays with soft colors and simple designs.

[0752] Step 5:

[0753] The server filters and lists relevant products based on the user's preferences, criteria, past browsing history, and sentiment data, and then uses a recommendation algorithm to select the most suitable product.

[0754] Step 6:

[0755] The server sends the generated recommendation list, along with visual content of the product information, as a push notification to the user's device.

[0756] Step 7:

[0757] The device receives notifications from the server and displays a product list on the user's screen. The user can select products of interest from the list and view detailed information, ensuring a seamless shopping experience.

[0758] (Example 2)

[0759] 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".

[0760] In modern online shopping, users often struggle to find products that match their preferences and criteria. Furthermore, the lack of personalized product recommendations limits the user experience. Solving these challenges and improving the user experience is crucial.

[0761] 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.

[0762] In this invention, the server includes information input means for inputting the user's hobbies and conditions, emotion analysis means for analyzing the user's emotions based on the user's hobbies and conditions, and information collection means for collecting new product information based on the user's hobbies, conditions, and emotions. This makes it possible to suggest products based on the emotional state and past behavior of individual users.

[0763] "Information input means" refers to devices or methods for users to input their hobbies and preferences.

[0764] "Emotion analysis means" refers to devices or methods that identify and analyze emotions from user input information and their actions at that time.

[0765] "Information gathering means" refers to devices or methods for obtaining new product information from external sources.

[0766] "Generation means" refers to devices or methods that generate content visually or in other forms based on collected data and analysis results.

[0767] A "listing method" is a device or method for identifying and neatly arranging products that suit a user's tastes, conditions, and feelings.

[0768] A "recommendation tool" is a device or method that suggests relevant products to a user based on their past user behavior and current analysis results.

[0769] "Notification means" refers to devices or methods for informing users of listed product information.

[0770] An "artificial intelligence algorithm" is a program that enables a machine to learn and autonomously optimize itself.

[0771] "User preferences" refer to the tastes that users have for a particular genre or style.

[0772] "Conditions" refer to specific requirements or constraints set by the user.

[0773] This invention is a system that analyzes a user's emotions based on their hobbies and circumstances, and provides personalized product recommendations. The system consists of a server, terminals, and users, and combines various technologies to enhance the user experience.

[0774] User input and sentiment analysis

[0775] Users utilize an application on their smart devices to input their fashion preferences and criteria. For example, they might enter "casual style," "red items," and "budget under 5,000 yen."

[0776] The device uses sensors such as cameras and microphones to monitor the user's facial expressions and voice, and analyzes this data using emotion analysis technology. This analysis allows the user's emotional state to be deciphered in real time, and the collected data is sent to a server.

[0777] Data collection and content generation

[0778] The server periodically retrieves the latest product information from partner online platforms via APIs. This information includes product name, category, price, and stock availability.

[0779] The server then uses a generative AI model to generate visual content based on the collected data and the user's emotional state. This generation process utilizes the prompt "I'd like suggestions for casual red items under 5,000 yen in a relaxed atmosphere" to create content that is more relevant to the user.

[0780] Notifications and product suggestions

[0781] The device receives a product list generated from the server and displays it to the user as a push notification. When the user opens the notification, product details and purchase options are provided.

[0782] This system allows users to receive product suggestions tailored to their own emotions and preferences, enriching their shopping experience. This leads to increased engagement and support for purchasing decisions.

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] The user launches an application on their smart device and enters their fashion preferences and criteria. This input includes style type, color, price range, etc. Once this information is entered, the device receives the data, formats it appropriately, and prepares it for the next processing step.

[0786] Step 2:

[0787] The device collects user facial expressions and voice data using its camera and microphone, along with user input. This data is analyzed by an emotion analysis engine to extract the user's emotional state (e.g., relaxed state). This emotional information is then transferred to the server as specific parameters.

[0788] Step 3:

[0789] The server receives data on the user's hobbies, preferences, and emotional state from the terminal as input. The server uses APIs from partner online platforms to collect product information that matches the criteria (e.g., casual red items, priced under 5,000 yen). This information is stored in a database and used in the next generation step.

[0790] Step 4:

[0791] The server uses a generative AI model to generate visual content based on collected product data and user sentiment information. This process uses the prompt "Please suggest casual red items for under 5,000 yen in a relaxed atmosphere." The generated content features color schemes and designs that match the user's emotional state.

[0792] Step 5:

[0793] The device receives visual content and product lists sent from the server and provides suggestions to the user via push notifications. When the user taps the notification, they can access detailed information. At this time, a visually appealing user interface is displayed. The user can further review the products and make a purchase decision on the spot.

[0794] (Application Example 2)

[0795] 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".

[0796] In modern fashion shopping, there is a challenge in recommending products that suit the diverse preferences and emotions of consumers. Therefore, there is a growing demand for systems that efficiently and individually suggest products suitable for users, thereby increasing their purchasing intent. Furthermore, there is a need to improve the user experience through visually appealing content. This invention aims to solve these problems.

[0797] 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.

[0798] In this invention, the server includes data input means for inputting user preferences and conditions, product information collection means for collecting product information, visual generation means for generating visual content for products, and emotion analysis means for recognizing emotional states and personalizing product recommendations. This makes it possible to find products that match the consumer's emotions and preferences and provide a highly personalized shopping experience. Furthermore, the purchasing experience can be further enriched by intuitively presenting product information to the user through a visual device.

[0799] A "data input means" is a mechanism for inputting information necessary to collect user preferences and conditions.

[0800] A "product information gathering mechanism" is a system for efficiently collecting information about newly released products.

[0801] A "visual generation means" is a mechanism for creating visually appealing content based on collected product information.

[0802] A "product identification method" is a mechanism for finding, organizing, and providing products that match the user's preferences and conditions.

[0803] An "emotional analysis tool" is a mechanism that analyzes a user's emotional state and uses that information to personalize product recommendations.

[0804] A "recommendation system" is a mechanism for suggesting relevant products based on past user behavior data.

[0805] A "presentation means" is a mechanism that uses visual devices to directly and intuitively present product information to the user.

[0806] A specific embodiment of this invention is a system realized through the cooperation of a user, a terminal, and a server. The user can input their fashion preferences and requirements using smart glasses or a device and receive information visually. This information is collected through data input means.

[0807] The device detects information entered by the user, as well as facial expressions and voice, and analyzes them using emotion analysis tools. For example, emotion analysis AI software is used to analyze the user's emotional state in real time based on available data and send the results to a server.

[0808] The server stores product information obtained from partner fashion providers through product information collection methods and uses machine learning and generative AI to create personalized visual content using visual generation methods. This generation process is controlled using prompts that respond to the user's preferences and emotions. Specifically, prompts such as "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items to the generative AI model" are used.

[0809] The visual content generated in this way is presented to the user on a display via a visual device through a presentation means. This allows the user to easily confirm visual content that matches their emotional state and make the purchasing decision process more intuitive.

[0810] This system provides suggestions tailored to the user's emotions and preferences, improving consumer satisfaction. Furthermore, the visual presentation of products goes beyond mere screen display, offering users a richer and more interactive shopping experience.

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] Users input their fashion preferences and requirements using smart glasses or devices. The entered information is collected through a data entry device. The data entry device receives the user's input and transmits it to the terminal as digital data.

[0814] Step 2:

[0815] The device senses the user's facial expressions and voice data in addition to the data entered by the user, and analyzes this data using emotion analysis tools. The device receives sensor data obtained from the camera and microphone as input and uses emotion analysis AI software to analyze the user's emotional state in real time. The analyzed emotion data is sent to a server.

[0816] Step 3:

[0817] The server periodically retrieves product information from external fashion providers using product information collection methods. This includes data such as product name, category, and price. The server stores this information in a database.

[0818] Step 4:

[0819] The server generates visual content using a generative AI model based on data obtained from emotion analysis, user preferences and conditions, and accumulated product information. Specifically, it creates visual content that matches the conditions, following the prompt message, "When the user's emotional state is relaxed, generate and present natural, blue-toned fashion items." The generated content is output from the visual generation device.

[0820] Step 5:

[0821] The server transmits the generated visual content to the user terminal via a presentation device and displays it on the visual device. The terminal then presents this content to the user through smart glasses or similar devices. This allows the user to visually confirm personalized product information.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] Information input means for entering user preferences and conditions,

[0846] Information gathering means for collecting information on newly released products based on the user's preferences and conditions,

[0847] Based on the collected product information, a generation means for generating visual content for the product,

[0848] A listing means for identifying and listing products that match the user's preferences and conditions,

[0849] A recommendation system for suggesting relevant products based on past user behavior data,

[0850] An analytical means for analyzing user feedback and reviews and extracting trends,

[0851] A notification means for notifying the user of the listed product information,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that the generation means uses a machine learning algorithm to improve the generated visual content.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that the analysis means analyzes user reviews using natural language processing technology.

[0857] "Example 1"

[0858] (Claim 1)

[0859] Means for obtaining user preferences and conditions via an information processing device,

[0860] A means for acquiring data on new products based on the user's preferences and conditions,

[0861] A generation means for generating visual content based on the acquired data,

[0862] A means for selecting and listing products that match the user's preferences and conditions,

[0863] A selection method for presenting relevant products based on history,

[0864] A means for analyzing feedback and evaluation information and extracting trends,

[0865] A means of notifying the user of the product information listed above,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, characterized in that the generation means uses a machine learning algorithm to improve the generated visual content.

[0869] (Claim 3)

[0870] The system according to claim 1, characterized in that the analysis means analyzes user evaluation information using a natural language processing method.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] Information input means for entering user preferences and conditions,

[0874] A data collection means for collecting data on newly released products based on the user's preferences and conditions,

[0875] Based on the collected product data, a generation means for generating visual content for the product,

[0876] A means for creating a list of products that match the user's preferences and conditions,

[0877] A recommendation system for recommending relevant products based on past user behavior,

[0878] An analytical means for analyzing user opinions and evaluations and extracting trends,

[0879] A notification means for informing the user of the data of the listed products,

[0880] A virtual try-on method for trying on products in a virtual space,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, characterized in that the generation means uses a machine learning algorithm to improve the generated visual content.

[0884] (Claim 3)

[0885] The system according to claim 1, characterized in that the analysis means analyzes user evaluations using natural language processing technology.

[0886] "Example 2 of combining an emotion engine"

[0887] (Claim 1)

[0888] A means for inputting information about the user's hobbies and preferences,

[0889] An emotion analysis means for analyzing the user's emotions based on the user's hobbies and conditions,

[0890] Information gathering means for collecting new product information based on the user's hobbies, conditions, and emotions,

[0891] A generation means for generating product images based on the collected product information and sentiment analysis results,

[0892] A listing means for identifying and listing products that match the user's hobbies, conditions, and emotions,

[0893] A recommendation system for suggesting relevant products based on past user behavior data,

[0894] A notification means for notifying the user of the listed product information,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, characterized in that the generation means uses an artificial intelligence algorithm to improve the generated product image.

[0898] (Claim 3)

[0899] The system according to claim 1, characterized in that the emotion analysis means evolves the personalization of suggestions by identifying the user's emotional state from the input data.

[0900] "Application example 2 when combining with an emotional engine"

[0901] (Claim 1)

[0902] A data entry method for inputting user preferences and conditions,

[0903] A product information collection means for collecting information on newly released products based on the user's preferences and conditions,

[0904] Based on the collected product information, a visual generation means for generating visual content for the product,

[0905] Product identification means for identifying and listing products that match the user's preferences and conditions,

[0906] A means of sentiment analysis to recognize the user's emotional state and personalize product recommendations,

[0907] A recommendation system for suggesting relevant products based on past user behavior data,

[0908] A presentation means for presenting a list of product information to the user via a visual device,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, characterized in that the visual generation means uses machine learning processing to improve the generated visual content.

[0912] (Claim 3)

[0913] The system according to claim 1, characterized in that the emotion analysis means optimizes recommendations based on the emotional state. [Explanation of symbols]

[0914] 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. Information input means for entering user preferences and conditions, Information gathering means for collecting information on newly released products based on the user's preferences and conditions, Based on the collected product information, a generation means for generating visual content for the product, A listing means for identifying and listing products that match the user's preferences and conditions, A recommendation system for suggesting relevant products based on past user behavior data, An analytical means for analyzing user feedback and reviews and extracting trends, A notification means for notifying the user of the listed product information, A system that includes this.

2. The system according to claim 1, characterized in that the generation means uses a machine learning algorithm to improve the generated visual content.

3. The system according to claim 1, characterized in that the analysis means analyzes user reviews using natural language processing technology.

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