system

Abstract

We provide the system. [Solution] A display device that provides images to the user's field of view, A camera that captures images displayed in the user's field of view, An analysis device that analyzes captured images and extracts features, A generator that generates product search queries based on extracted features, A search device that sends the generated search query to an online shopping platform to search for products, A display means for displaying searched product information within the user's field of view, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, in order for a user to quickly obtain detailed information about a product at a physical store or an online shopping site, manual search and navigation are required, which is time-consuming and laborious. In addition, there has been a lack of means for immediately searching and comparing prices for products that visually arouse interest. For this reason, there has been a demand for a new system for improving the user experience and efficiently obtaining product information.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes a display device that provides information displayed in the user's field of view, a shooting device that captures images displayed in the user's field of view, an analysis device that analyzes the captured images and extracts features, a generation device that generates product search queries based on the extracted features, a search device that transmits the generated search queries to an online shopping platform to search for products, and a display means that displays the searched product information in the user's field of view. With this system, the user can quickly obtain product information about objects in their field of view and make purchase decisions efficiently.

[0006]

[0007] A "user" refers to a person who uses the system.

[0008] "Field of view" refers to the area that the user is directly seeing with their eyes.

[0009] "Image" refers to visual information provided to the user by a display device.

[0010] A "display device" refers to a device that provides images to the user's field of vision.

[0011] A "shooting device" refers to a device used to capture images that appear in the user's field of vision.

[0012] "Image" refers to two-dimensional or three-dimensional visual information captured by a camera.

[0013] An "analysis device" refers to a computer device used to analyze captured images and extract features.

[0014] "Features" refer to the characteristics and attributes of an object extracted through image analysis.

[0015] A "generation device" refers to a computer device used to generate search queries based on extracted features.

[0016] The "search query" refers to keywords or phrases used to search for products on an online shopping platform.

[0017] The "online shopping platform" refers to a website or application that sells products on the Internet.

[0018] The "search device" refers to a computer device used to search for products on an online shopping platform using the generated search query.

[0019] The "product information" refers to the detailed information of the product obtained from the online shopping platform.

[0020] The "display means" refers to a method or device for displaying the searched product information within the user's field of vision.

[0021] The "overlay display" refers to a method of superimposing additional information on an existing field of vision for display.

Brief Description of the Drawings

[0022] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It 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 Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] 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.

[0028] 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).

[0029] 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."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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".

[0043] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is implemented by the following means:

[0044] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is then transmitted to a server via the internet.

[0045] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[0046] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a search query such as "men's black sneakers" is generated. This search query is then sent to the online shopping platform.

[0047] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[0048] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[0049] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, compare them, and make purchases. In particular, the ability to obtain information in a natural way through a visual interface significantly improves the user experience.

[0050] The following describes the processing flow.

[0051] Step 1:

[0052] The user wears smart glasses and focuses on specific objects in everyday situations.

[0053] Step 2:

[0054] The device (smart glasses) captures images of objects within the user's field of view. Capture is triggered at regular intervals or by specific actions (for example, pressing a button).

[0055] Step 3:

[0056] The device sends the captured image data to the server via the internet. A secure protocol (e.g., HTTPS) is used for this transmission.

[0057] Step 4:

[0058] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[0059] Step 5:

[0060] The server generates appropriate search queries based on the extracted feature information. For example, if the recognized object is sneakers, it will generate queries such as "black sneakers men's".

[0061] Step 6:

[0062] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[0063] Step 7:

[0064] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[0065] Step 8:

[0066] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity.

[0067] Step 9:

[0068] The server then sends the organized and formatted search results back to the terminal.

[0069] Step 10:

[0070] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[0071] (Example 1)

[0072] 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."

[0073] Traditional online shopping systems have made it difficult for users to instantly obtain detailed information about products they are interested in in real time. Furthermore, methods for efficiently identifying specific objects and displaying related product information have been limited. To solve these problems, a system is needed that automatically recognizes objects within the user's field of view and quickly searches for and displays relevant information.

[0074] 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.

[0075] In this invention, the server includes a display means for capturing the user's visual information, an image capture means for photographing the user's visual information, a data analysis means for analyzing the captured image and extracting features, a query generation means for generating product search queries based on the extracted features, a data transmission means for sending the generated search queries to an online shopping platform, and an information display means for receiving search results and presenting them to the user. This makes it possible for the user to easily obtain detailed information about products that they are visually interested in in real time.

[0076] A "display means for capturing user's visual information" is a device for displaying information that is visible to the user.

[0077] "Image capture means for capturing user's visual information" refers to a device for capturing video or images that come into the user's field of vision.

[0078] "Data analysis means for analyzing captured images and extracting features" refers to a device or program for analyzing captured image data and extracting important features and attributes from it.

[0079] "Query generation means for generating product search queries based on extracted features" refers to a device or program for automatically generating appropriate product search queries based on analyzed features.

[0080] "Data transmission means for sending generated search queries to an online shopping platform" refers to a device or program for sending queries to an online shopping platform via the internet.

[0081] "Information display means for receiving search results and presenting them to the user" refers to a device or program for receiving search results from an online shopping platform and visually presenting them to the user.

[0082] "Object identification means for identifying objects of user interest from captured images" refers to a device or program for identifying specific objects of user interest from captured images.

[0083] "Visualization means for overlaying searched product information onto the user's visual field" refers to a device or program for displaying product information obtained as search results superimposed on the user's field of view.

[0084] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is specifically implemented by the following means.

[0085] System Overview

[0086] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is transmitted to a server via the internet. The server analyzes the received image and extracts the features of the object the user is focusing on. Machine learning and image recognition technologies are used for this feature extraction. For example, deep learning frameworks (such as TENSORFLOW® or PyTorch) can be used. Based on the extracted features, the server generates an appropriate search query. The generated search query is sent to an online shopping platform, and the search results are returned to the server. The server organizes these results, formats them in a user-friendly format, and sends them to the terminal, where they are overlaid on the user's field of view through the smart glasses' display device.

[0087] Hardware and software used

[0088] Hardware:

[0089] Smart glasses (with built-in display and imaging devices for capturing visual information)

[0090] High-speed communication lines (5G and fiber optics)

[0091] software:

[0092] Image analysis module (using technologies such as TensorFlow and PyTorch)

[0093] Query generation module

[0094] Data transmission and reception module

[0095] API for online shopping platforms

[0096] Database management systems (MySQL®, PostgreSQL)

[0097] Interface software for overlay display

[0098] Specific examples of how the system works

[0099] The following is a specific example of how this system works.

[0100] 1. The user is looking at branded gloves in a shopping mall.

[0101] 2. The smart glasses capture an image of the glove and send it to the server.

[0102] 3. The server extracts the features of the gloves through image analysis and generates a search query: "women's winter gloves brand".

[0103] 4. The generated search query is sent to the online shopping platform.

[0104] 5. Glove price and review information obtained from each platform is integrated and organized on the server.

[0105] 6. Organized information is overlaid onto the user's field of view through smart glasses.

[0106] Example of a prompt

[0107] Examples of prompt messages are shown below.

[0108] "I'm looking at a pair of gloves from a certain brand. Please search for more information about these gloves and display related product prices and reviews."

[0109] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, making comparisons and purchases easy. Because information is obtained naturally through a visual interface, the user experience is significantly improved.

[0110] Based on the above description, the present invention is now presented in its specific embodiments. The scope of the present invention is limited by the claims, and the above specific embodiments are merely examples to illustrate its technical scope.

[0111] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0112] Step 1: The user focuses on the object.

[0113] The user wears smart glasses and focuses on a specific object. Focusing on a specific object within their field of view for a certain period of time triggers image capture.

[0114] Input: User's visual information (object of focus)

[0115] Specific action: The user stares at a specific object within their field of view.

[0116] Step 2: The device captures the image.

[0117] The smart glasses, acting as the device, use their built-in camera to capture images within the user's field of view. The captured images are temporarily stored on the device.

[0118] Input: Images seen through the user's field of view

[0119] Output: Captured image file

[0120] Specific operation: The smart glasses' camera takes an image and saves the data.

[0121] Step 3: The device sends the image to the server.

[0122] The captured images are sent from the smart glasses to a server via the internet. Encryption is used during transmission to ensure secure communication.

[0123] Input: Captured image file

[0124] Output: Image data sent to the server

[0125] Specific operation: The device uses 5G or Wi-Fi to send image data to the server.

[0126] Step 4: The server receives the image.

[0127] The server receives image data sent from the terminal. The received images are stored in the database.

[0128] Input: Image data sent from the device

[0129] Output: Image data stored in the database

[0130] Specific operation: The server's receiving module acquires image data and saves it to the database.

[0131] Step 5: The server analyzes the image.

[0132] The server analyzes the received image data and extracts features of the object the user is interested in. Feature extraction is performed using deep learning frameworks (such as TensorFlow or PyTorch).

[0133] Input: Received image data

[0134] Output: Extracted feature data

[0135] Specific operation: The server's image analysis module analyzes the data and extracts features.

[0136] Step 6: The server generates the search query.

[0137] The server generates search queries based on the extracted features. Queries appropriate to the object are generated, such as "men's black sneakers."

[0138] Input: Extracted feature data

[0139] Output: Generated search query

[0140] Specific operation: The server's query generation algorithm creates a query based on the feature data.

[0141] Step 7: The server sends a query to the online shopping platform.

[0142] The generated search query is sent to the API of an online shopping platform. For example, the APIs of Amazon or Rakuten are used.

[0143] Input: Generated search query

[0144] Output: Query sent to the online shopping platform

[0145] Specific operation: The server formats the API request and sends the query.

[0146] Step 8: The server receives the search results.

[0147] The server receives search results from various online shopping platforms. The received data is then parsed in formats such as JSON.

[0148] Input: Search results data from an online shopping platform

[0149] Output: Analyzed search result data

[0150] Specific operation: The server parses the received data using a JSON parser and extracts the necessary information.

[0151] Step 9: The server organizes the search results.

[0152] The server that receives the search results organizes them by price or popularity and formats them into a user-friendly format.

[0153] Input: Analyzed search result data

[0154] Output: Formatted search result data

[0155] Specific operation: The server's data organization algorithm sorts the data and arranges it into an easily understandable format.

[0156] Step 10: Send organized search results to your device.

[0157] The server sends the organized search results back to the smart glasses. Encryption is used during transmission to ensure secure communication.

[0158] Input: Formatted search result data

[0159] Output: Search result data sent to the terminal

[0160] Specific operation: The server encrypts the data and securely transmits it to the terminal.

[0161] Step 11: The device displays information as an overlay.

[0162] Smart glasses overlay information onto the user's field of view. The display design is optimized for easy viewing of the information.

[0163] Input: Search result data sent to the device

[0164] Output: Product information displayed as an overlay in the user's field of view.

[0165] Specific operation: The smart glasses overlay the received data onto the screen and provide it to the user.

[0166] (Application Example 1)

[0167] 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."

[0168] Traditional online shopping systems made it difficult for users to quickly obtain information about products they were interested in within a physical store, and they had limited means of efficiently comparing detailed information and multiple search results when selecting products. This limited the user's purchasing experience, making it difficult to make informed purchasing decisions. Furthermore, users had to go through the trouble of searching online for the same product after seeing it in the physical world.

[0169] 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.

[0170] In this invention, the server includes display means for providing images to the user's field of view, shooting means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating search queries based on the extracted features, search means for sending the generated search queries to an online platform to search for products, display means for displaying the searched product information to the user's field of view, and means for displaying product information, price, ratings, inventory status, and information on similar products in real time by focusing on real-world objects. This makes it easy for users to instantly obtain detailed information on products they find in physical stores, compare them, and make purchases. As a result, the user's purchasing experience is improved, and purchasing decisions can be made efficiently using the information.

[0171] "Display means that provide images to the user's field of view" refers to devices such as smart glasses or head-mounted displays worn by the user that visually present information to the user's field of view.

[0172] "A means of capturing images displayed in the user's field of view" refers to devices such as cameras mounted on smart glasses or head-mounted displays that capture the video of the user's field of view and save it as digital image data.

[0173] An "analysis method for analyzing captured images and extracting features" is a system that uses machine learning and image recognition technologies to analyze features such as the shape and texture of objects from captured field-of-view images and extract important information.

[0174] A "generation means for generating search queries based on extracted features" is a device that automatically generates appropriate keywords and phrases for searching for products online based on the features of an object extracted by an analysis means.

[0175] "A search method that sends generated search queries to an online platform to search for products" refers to a method that sends search queries generated via an internet connection to an online shopping platform and searches for related products on that platform.

[0176] A "display means for displaying searched product information within the user's field of view" is a device that organizes product information received from an online platform and presents it to the user by overlaying it on the user's smart glasses or head-mounted display.

[0177] "A means of displaying product information, price, ratings, stock status, and information on similar products in real time by focusing on real-world objects" refers to a device that has the function of instantly displaying detailed product information related to an object in the user's field of view simply by pointing smart glasses at an object that the user is interested in in the real world.

[0178] This invention relates to a system that provides real-time product information when a user wears smart glasses in a physical store and focuses on a product. The system captures images within the user's field of view, analyzes those images, and displays search results from an online shopping platform.

[0179] First, the user puts on smart glasses. These smart glasses have built-in display and camera functions, allowing the user to capture images of products by looking at them. The captured images are then sent from the device to a server via the internet.

[0180] The server analyzes received images using a deep learning framework such as TensorFlow. Through image analysis, the server extracts features such as the shape and texture of objects. Based on these extracted features, it generates a search query. This generated search query is sent to an online shopping platform to search for relevant products.

[0181] Product information obtained as search results (price, ratings, stock status, information on similar products, etc.) is organized on the server. The organized information is then sent back to the terminal and presented to the user through the smart glasses display. At this time, the searched product information is displayed as an overlay in the user's field of view, allowing for extremely intuitive information acquisition.

[0182] A concrete example would be when a user finds a summer dress in a shopping mall. By pointing smart glasses at the dress, the price, customer reviews, and stock availability of the product would instantly appear in their field of view. This would allow users to compare products in detail within the physical store and make it easier to choose the most suitable item on the spot.

[0183] An example of a prompt message for a generative AI model is as follows:

[0184] "Extract the product features from this image and generate search queries for an online shopping platform. Based on the results, write code to display the appropriate product information."

[0185] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0186] Step 1:

[0187] The user puts on smart glasses and looks at a specific product.

[0188] Input: The actual product as seen by the user.

[0189] Operation: The smart glasses' camera captures images of products that come into the user's field of view.

[0190] Output: Captured image data.

[0191] Step 2:

[0192] The captured image data is sent from the smart glasses to the server.

[0193] Input: Captured image data.

[0194] Operation: The smart glasses upload image data to a server via the internet.

[0195] Output: Image data stored on the server.

[0196] Step 3:

[0197] The server analyzes the received image data and extracts the characteristics of the target object.

[0198] Input: Image data stored on the server.

[0199] Operation: The server analyzes images using deep learning frameworks such as TensorFlow to identify the shape and texture of objects.

[0200] Output: Feature data of the object.

[0201] Step 4:

[0202] The server generates search queries based on the feature data.

[0203] Input: Feature data of an object.

[0204] Operation: The server automatically generates appropriate search queries (e.g., "summer dress blue women's") based on the extracted features.

[0205] Output: The generated search query.

[0206] Step 5:

[0207] The generated search query is sent to the online shopping platform.

[0208] Input: The generated search query.

[0209] Operation: The server sends queries to the online shopping platform's API via the internet to search for relevant products.

[0210] Output: Search results (product information) returned from the online shopping platform.

[0211] Step 6:

[0212] The search results are organized on the server.

[0213] Input: Search results from an online shopping platform.

[0214] Operation: The server reorganizes information such as price, ratings, stock status, and related products, and presents it in a user-friendly format.

[0215] Output: Organized product information.

[0216] Step 7:

[0217] The organized product information is sent to the smart glasses and displayed as an overlay in the user's field of view.

[0218] Input: Organized product information.

[0219] Operation: Information is retransmitted from the server to the smart glasses and overlaid onto the field of view through the smart glasses' display mechanism.

[0220] Output: Product information displayed within the user's field of view.

[0221] In this way, the system for implementing the present invention provides users with immediate detailed information about products they see in physical stores, and supports them in making efficient purchasing decisions.

[0222] 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.

[0223] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. By combining this system with an emotion engine that recognizes the user's emotions, more appropriate search results can be provided. This system is implemented by the following means.

[0224] The user wears a display device such as smart glasses and focuses on a specific object in an everyday scene. The smart glasses, acting as the terminal, use a camera to capture images of what is in the user's field of view. These images are then transmitted to a server via the internet.

[0225] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[0226] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a query such as "black sneakers men" is generated. This search query is then sent to the online shopping platform.

[0227] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[0228] A distinctive feature of this invention is the introduction of an emotion engine, which enables the optimization of search results based on the user's emotions. The emotion engine recognizes the user's emotional state from their facial expressions and voice. The server analyzes the emotional data obtained from the emotion engine and adjusts the search query based on the user's current emotional state. For example, if the user is enjoying themselves, products that match that emotion can be displayed preferentially. Furthermore, a learning engine that learns the user's preferences using emotional data can also be incorporated. This learning engine analyzes the user's past emotional data and purchase history to provide individually optimized product suggestions.

[0229] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[0230] Furthermore, when a user looks at gloves, the emotion engine recognizes the user's emotions, and if the user is excited, it prioritizes displaying particularly popular gloves. In this way, users can instantly obtain detailed information about products that visually interest them and make efficient purchasing decisions. In addition, the accumulation of emotion data will allow future search results to be more tailored to the user's preferences.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user wears smart glasses and focuses on specific objects in everyday situations.

[0234] Step 2:

[0235] The device (smart glasses) captures images of objects within the user's field of vision. It also collects emotional data from the user's facial expressions and voice.

[0236] Step 3:

[0237] The device transmits captured image data and emotion data to a server via the internet. A secure protocol (e.g., HTTPS) is used for transmission.

[0238] Step 4:

[0239] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[0240] Step 5:

[0241] The server uses an emotion engine to analyze emotion data sent from the terminal and recognize the user's emotional state. For example, it identifies emotions such as surprise, enjoyment, or interest.

[0242] Step 6:

[0243] The server generates appropriate search queries based on extracted feature information and sentiment data. For example, if a user is interested in and enjoying sneakers, a query such as "recommended black sneakers for men" might be generated.

[0244] Step 7:

[0245] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[0246] Step 8:

[0247] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[0248] Step 9:

[0249] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity based on emotional state.

[0250] Step 10:

[0251] The server then sends the organized and formatted search results back to the terminal.

[0252] Step 11:

[0253] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[0254] Step 12:

[0255] The device captures additional user reactions and sends this sentiment data to the server. This data is analyzed by a learning engine and used to improve future search accuracy.

[0256] (Example 2)

[0257] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0258] Traditional online shopping systems lacked efficient methods for acquiring information about items users were interested in and their characteristics. Furthermore, they couldn't present search results that considered the user's emotional state, making it difficult to recommend the most suitable products. This resulted in users spending a significant amount of time and effort searching for products and making purchase decisions.

[0259] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means that recognize the user's emotions and correct the search query based on those emotions, a learning device that analyzes past emotion data and purchase history to optimize search results, and display means that overlays the searched product information onto the user's field of view. This enables product recommendations that correspond to the user's emotions, resulting in efficient searching and optimal product suggestions.

[0260] A "display device" is a device that provides images to the user's field of vision.

[0261] A "shooting device" is a device used to capture images that are displayed in the user's field of view.

[0262] An "analysis device" is a device used to analyze captured images and extract features.

[0263] A "generation device" is a device that generates product search queries based on extracted features.

[0264] A "search device" is a device that sends generated product search queries to an online shopping platform to search for products.

[0265] "Display means" refers to the means of displaying searched product information within the user's field of view.

[0266] "Emotion recognition means" refers to a means of recognizing a user's emotions and correcting search queries based on those emotions.

[0267] A "learning device" is a device that analyzes past sentiment data and purchase history to optimize search results.

[0268] "Identification means" refers to means for identifying objects that the user is interested in from a captured image.

[0269] This invention relates to a system that efficiently acquires information displayed within a user's field of vision and presents search results on an online shopping platform. The system aims to efficiently understand the user's interests and provide appropriate search results by having the user wear a display device such as smart glasses and focus on specific objects in everyday scenes.

[0270] Hardware and software to use

[0271] The smart glasses (display devices) used by the user are equipped with a built-in camera (image capture device) and a display. The camera captures images within the user's field of view in real time, and this data is transmitted to a server via the internet. For analysis, the server uses deep learning frameworks such as TensorFlow and PyTorch to analyze the captured image data.

[0272] Emotion recognition and query generation

[0273] The server analyzes the received image data to extract features of the object the user is focusing on. Specifically, the server identifies objects within the image and extracts features such as their color, shape, and brand logo. Once feature extraction is complete, the server generates a product search query based on this information. For example, if the user is looking at black gloves, a search query such as "black gloves women's brand" will be generated.

[0274] Search and results provision

[0275] The generated search query is sent from the server to the online shopping platform. For example, it searches for products using APIs from Amazon or Rakuten. The information on multiple products obtained as search results is returned to the server. The server organizes these search results and formats them in a user-friendly format (such as by price or popularity). This information is then sent back to the smart glasses and displayed as an overlay in the user's field of view.

[0276] Emotional engine and learning engine

[0277] Furthermore, this invention incorporates an emotion engine to optimize search results by taking user emotions into consideration. The smart glasses monitor the user's facial expressions and voice, collecting emotion data in real time. This data is transmitted to a server and analyzed. For example, if the user is excited, the most suitable products based on that emotion (such as popular products) are displayed preferentially. In addition, the learning engine analyzes past emotion data and purchase history to optimize future search results to match the user's preferences.

[0278] Specific example

[0279] Let's say a user is in a shopping mall and becomes interested in a brand of gloves, and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server analyzes the image and generates a search query such as "black gloves women brand" and sends it to online shopping platforms. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses. In addition, an emotion engine recognizes the user's emotions, and if the user is excited, particularly popular gloves are prioritized in the display.

[0280] Example of a prompt

[0281] "Please describe in detail each step of the system's processing: when a user focuses on a specific object, smart glasses capture the image, a server performs image analysis, and then the system presents optimized shopping search results based on the user's emotion recognition by an emotion engine."

[0282] The above describes the embodiments for carrying out the present invention. This system efficiently acquires information about objects visible to the user and provides optimal product search results that take emotions into consideration, thereby improving the user's online shopping experience.

[0283] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0284] Step 1:

[0285] Capture of user's gaze information

[0286] The user wears smart glasses and gazes at a specific object in daily scenes.

[0287] The terminal (smart glasses) uses the built-in camera to capture in real time the image reflected in the user's field of view.

[0288] Input: Image of the object reflected in the user's field of view.

[0289] Output: Captured image data.

[0290] Specific operation: For example, when the user stares at brand gloves in a shopping mall, the image of the gloves is captured by the smart glasses camera.

[0291] Step 2:<000092T>

[0292] Transmission of image data

[0293] The terminal (smart glasses) transmits the captured image data to the server via the Internet.

[0294] Input: Captured image data.

[0295] Output: Image data transmitted to the server. <00009T3>

[0296] <Q000935>Specific operation: The smart glasses use Wi-Fi or a mobile network to upload the image to the server in real time.

[0297] Step 3:

[0298] Analysis of image data

[0299] The server analyzes the received image data and extracts the features of the object that the user is gazing at.

[0300] Deep learning frameworks such as TensorFlow and PyTorch are used for the analysis.

[0301] Input: Image data sent to the server.

[0302] Output: Feature data of the extracted object.

[0303] [[ID=1十七]]

[0304] Step 4: <G

[0305] Generation of a search query

[0306] The server generates a product search query based on the extracted feature data.

[0307] Input: Feature data of the extracted object.

[0308] Output: Generated product search query.

[0309] Specific operation: For example, automatically generate a query such as "black gloves for ladies brand".

[0310] Step 5:

[0311] Transmission of the search query

[0312] The server transmits the generated search query to an online shopping platform.

[0313] Input: Generated product search query.

[0314] Output: Results of query submission to the online shopping platform.

[0315] Specific operation: Perform product searches using APIs from Amazon, Rakuten Market, etc.

[0316] Step 6:

[0317] Retrieving search results

[0318] The server receives search results obtained from the online shopping platform.

[0319] Input: Search results returned from an online shopping platform.

[0320] Output: Received search result data.

[0321] Specific operation: Retrieve data such as product information, prices, and reviews for multiple products.

[0322] Step 7:

[0323] Organizing and formatting search results

[0324] The server organizes the search results it retrieves and formats them into a user-friendly format.

[0325] Input: Received search result data.

[0326] Output: Organized and formatted search results data.

[0327] Specific actions: Sort by price or popularity, and organize product images and price information to display in a unified format.

[0328] Step 8:

[0329] Displaying search results

[0330] The server resends the organized and formatted search results to the device (smart glasses).

[0331] The device (smart glasses) overlays the search results onto the user's field of view.

[0332] Input: Organized and formatted search results data.

[0333] Output: Search results displayed on smart glasses.

[0334] Specific operation: Search results are overlaid on the smart glasses' display, aligned with the user's field of view.

[0335] Step 9:

[0336] Acquisition and analysis of emotional data

[0337] The device (smart glasses) uses its built-in camera and microphone to acquire emotional data from the user's facial expressions and voice.

[0338] The server analyzes the received emotional data to identify the user's emotional state.

[0339] Input: User facial expression data, voice data.

[0340] Output: Analyzed user emotional state data.

[0341] Specific operation: Use a facial recognition algorithm to determine whether the user is excited or relaxed.

[0342] Step 10:

[0343] Optimizing search results

[0344] The search results are optimized based on sentiment data acquired by the server.

[0345] The learning device analyzes past sentiment data and purchase history to further optimize future search results to match the user's preferences.

[0346] Input: User's emotional state data, past emotional data, purchase history.

[0347] Output: Optimized search results.

[0348] Specific operation: When the user is excited, popular products are displayed first, and the most suitable products are recommended individually based on past search history.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0351] Traditional online shopping systems lacked a way for users to instantly obtain detailed information about products they viewed in physical stores. Furthermore, they failed to provide optimal product information tailored to the user's emotional state, resulting in users spending considerable time selecting products. Additionally, personalized product recommendations based on user preferences and past purchase history were insufficient. This system aims to address these challenges and improve the user's purchasing experience.

[0352] 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.

[0353] In this invention, the server includes display means for providing images to the user's field of view, imaging means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating product search queries based on the extracted features, search means for sending the generated search queries to an online shopping platform to search for products, display means for displaying the searched product information in the user's field of view, emotion analysis means for recognizing the user's emotions, and optimization means for optimizing the searched product information based on the emotion information obtained from the emotion analysis means. This makes it possible to obtain detailed information about products viewed by the user in a physical store in real time, and further enables the provision of optimal product information according to the user's emotional state, thereby enabling personalized product suggestions.

[0354] A "display means" is a device that provides images to the user's field of vision.

[0355] An "imaging means" is a device for capturing images displayed in the user's field of view.

[0356] An "analysis means" is a device used to analyze captured images and extract features.

[0357] A "generation means" is a device for generating product search queries based on extracted features.

[0358] A "search device" is a device that sends a generated search query to an online shopping platform to search for products.

[0359] An "emotion analysis device" is a device used to recognize a user's emotions.

[0360] An "optimization means" is a device for optimizing product information retrieved based on emotional information obtained from an emotional analysis means.

[0361] This invention relates to a system that provides detailed information about a product in real time when a user views the product in a physical store. This system includes display means, imaging means, analysis means, generation means, search means, sentiment analysis means, and optimization means.

[0362] When a user wears a device such as a smartphone or smart glasses and is looking at products in a physical store, an imaging device captures images of the products that come into their field of view. The imaging device can be the built-in camera of the smart glasses or the camera of the smartphone. The captured images are transmitted to a server via the internet.

[0363] The server analyzes the received images using analysis tools and employs machine learning and image recognition technologies to extract product features. For example, deep learning frameworks such as TensorFlow can be used. This extracts features such as product type, brand, and color.

[0364] Next, the generation method generates a search query based on the extracted features. For example, a query such as "black sneakers men" is generated. The search query is sent to the online shopping platform by the search method. In this case, the search is performed using an API.

[0365] The search results returned from the online shopping platform are sent back to the server. Here, the emotion analysis system analyzes emotional information obtained from the user's facial expressions and voice to determine the user's current emotional state. OpenCV's facial recognition function and emotion analysis models can be used.

[0366] The optimization mechanism optimizes search results based on analyzed sentiment information. For example, if the user is enjoying themselves, products that match that sentiment, i.e., popular products, are displayed preferentially. The optimized search results are then sent back to the device and overlaid on the user's field of view through the display mechanism.

[0367] This allows users to instantly obtain detailed information about products that visually interest them, enabling them to make efficient purchase decisions. Furthermore, the accumulation of emotional data will allow future search results to better match the user's preferences.

[0368] Specific example

[0369] Let's say a user is looking at a specific pair of sneakers in a shopping mall. When the user views the sneakers through smart glasses, the camera captures an image and sends it to a server. The server analyzes the image and generates a search query, "black sneakers men," which is then sent to an online shopping platform. The search results are returned to the server and sorted by popularity based on the user's sentiment. The final search results are displayed as an overlay on the smart glasses.

[0370] Example of a prompt

[0371] "Create a feature that uses the image capture function of smart glasses to identify products currently in the user's field of view and then searches for them on an online shopping platform. Also, incorporate an algorithm that uses an emotion engine to analyze the user's emotions and optimize search results accordingly."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] When a user views a product in a physical store using a smartphone or smart glasses, the device's camera captures an image of that product. The input is a real-time captured image, and the output is image data. The device then sends this image data to a server via the internet.

[0375] Step 2:

[0376] The server sends the received image data to the analysis tool. The analysis tool uses a deep learning model such as TensorFlow to extract product features from the image. The input is the captured image data, and the output is product feature data (e.g., color, shape, brand, etc.).

[0377] Step 3:

[0378] The server generates appropriate product search queries based on extracted feature data. For example, if the feature data is "black sneakers," the generated search query will be "black sneakers men's." The input is feature data, and the output is a search query.

[0379] Step 4:

[0380] The generated search query is sent to the online shopping platform via the search mechanism. The input is the search query, and the output is the search results returned by the online shopping platform.

[0381] Step 5:

[0382] The server receives the returned search results. In parallel, it uses the terminal's camera and microphone to capture the user's facial expressions and voice and sends them to the emotion analysis system. The emotion analysis system uses OpenCV's face recognition function and emotion analysis model to analyze the user's emotions. The input is the user's real-time video and audio data, and the output is emotion information (e.g., joy, surprise, excitement).

[0383] Step 6:

[0384] The server's optimization method optimizes search results based on acquired sentiment information. For example, if a user is excited, popular products are displayed preferentially. The input is sentiment information and search results, and the output is the optimized search results.

[0385] Step 7:

[0386] Optimized search results are transmitted to the device via the internet. They are then overlaid on the user's screen through the device's display. The input is the optimized search results, and the output is the product information displayed on the user's screen. This allows the user to check product information in real time and make efficient purchase decisions.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Second Embodiment]

[0391] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is implemented by the following means:

[0404] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is then transmitted to a server via the internet.

[0405] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[0406] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a search query such as "men's black sneakers" is generated. This search query is then sent to the online shopping platform.

[0407] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[0408] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[0409] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, compare them, and make purchases. In particular, the ability to obtain information in a natural way through a visual interface significantly improves the user experience.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The user wears smart glasses and focuses on specific objects in everyday situations.

[0413] Step 2:

[0414] The device (smart glasses) captures images of objects within the user's field of view. Capture is triggered at regular intervals or by specific actions (for example, pressing a button).

[0415] Step 3:

[0416] The device sends the captured image data to the server via the internet. A secure protocol (e.g., HTTPS) is used for this transmission.

[0417] Step 4:

[0418] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[0419] Step 5:

[0420] The server generates appropriate search queries based on the extracted feature information. For example, if the recognized object is sneakers, it will generate queries such as "black sneakers men's".

[0421] Step 6:

[0422] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[0423] Step 7:

[0424] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[0425] Step 8:

[0426] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity.

[0427] Step 9:

[0428] The server then sends the organized and formatted search results back to the terminal.

[0429] Step 10:

[0430] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[0431] (Example 1)

[0432] 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".

[0433] Traditional online shopping systems have made it difficult for users to instantly obtain detailed information about products they are interested in in real time. Furthermore, methods for efficiently identifying specific objects and displaying related product information have been limited. To solve these problems, a system is needed that automatically recognizes objects within the user's field of view and quickly searches for and displays relevant information.

[0434] 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.

[0435] In this invention, the server includes a display means for capturing the user's visual information, an image capture means for photographing the user's visual information, a data analysis means for analyzing the captured image and extracting features, a query generation means for generating product search queries based on the extracted features, a data transmission means for sending the generated search queries to an online shopping platform, and an information display means for receiving search results and presenting them to the user. This makes it possible for the user to easily obtain detailed information about products that they are visually interested in in real time.

[0436] A "display means for capturing user's visual information" is a device for displaying information that is visible to the user.

[0437] "Image capture means for capturing user's visual information" refers to a device for capturing video or images that come into the user's field of vision.

[0438] "Data analysis means for analyzing captured images and extracting features" refers to a device or program for analyzing captured image data and extracting important features and attributes from it.

[0439] "Query generation means for generating product search queries based on extracted features" refers to a device or program for automatically generating appropriate product search queries based on analyzed features.

[0440] "Data transmission means for sending generated search queries to an online shopping platform" refers to a device or program for sending queries to an online shopping platform via the internet.

[0441] "Information display means for receiving search results and presenting them to the user" refers to a device or program for receiving search results from an online shopping platform and visually presenting them to the user.

[0442] "Object identification means for identifying objects of user interest from captured images" refers to a device or program for identifying specific objects of user interest from captured images.

[0443] "Visualization means for overlaying searched product information onto the user's visual field" refers to a device or program for displaying product information obtained as search results superimposed on the user's field of view.

[0444] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is specifically implemented by the following means.

[0445] System Overview

[0446] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is sent to a server via the internet. The server analyzes the received image and extracts the features of the object the user is focusing on. Machine learning and image recognition technologies are used for this feature extraction. For example, deep learning frameworks (such as TensorFlow or PyTorch) could be used. Based on the extracted features, the server generates an appropriate search query. The generated search query is sent to an online shopping platform, and the search results are returned to the server. The server organizes these results, formats them in a user-friendly format, and sends them to the terminal, where they are overlaid on the user's field of view through the smart glasses' display device.

[0447] Hardware and software used

[0448] Hardware:

[0449] Smart glasses (with built-in display and imaging devices for capturing visual information)

[0450] High-speed communication lines (5G and fiber optics)

[0451] software:

[0452] Image analysis module (using technologies such as TensorFlow and PyTorch)

[0453] Query generation module

[0454] Data transmission and reception module

[0455] API for online shopping platforms

[0456] Database management systems (MySQL, PostgreSQL)

[0457] Interface software for overlay display

[0458] Specific examples of how the system works

[0459] The following is a specific example of how this system works.

[0460] 1. The user is looking at branded gloves in a shopping mall.

[0461] 2. The smart glasses capture an image of the glove and send it to the server.

[0462] 3. The server extracts the features of the gloves through image analysis and generates a search query: "women's winter gloves brand".

[0463] 4. The generated search query is sent to the online shopping platform.

[0464] 5. Glove price and review information obtained from each platform is integrated and organized on the server.

[0465] 6. Organized information is overlaid onto the user's field of view through smart glasses.

[0466] Example of a prompt

[0467] Examples of prompt messages are shown below.

[0468] "I'm looking at a pair of gloves from a certain brand. Please search for more information about these gloves and display related product prices and reviews."

[0469] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, making comparisons and purchases easy. Because information is obtained naturally through a visual interface, the user experience is significantly improved.

[0470] Based on the above description, the present invention is now presented in its specific embodiments. The scope of the present invention is limited by the claims, and the above specific embodiments are merely examples to illustrate its technical scope.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1: The user focuses on the object.

[0473] The user wears smart glasses and focuses on a specific object. Focusing on a specific object within their field of view for a certain period of time triggers image capture.

[0474] Input: User's visual information (object of focus)

[0475] Specific action: The user stares at a specific object within their field of view.

[0476] Step 2: The device captures the image.

[0477] The smart glasses, acting as the device, use their built-in camera to capture images within the user's field of view. The captured images are temporarily stored on the device.

[0478] Input: Images seen through the user's field of view

[0479] Output: Captured image file

[0480] Specific operation: The smart glasses' camera takes an image and saves the data.

[0481] Step 3: The device sends the image to the server.

[0482] The captured images are sent from the smart glasses to a server via the internet. Encryption is used during transmission to ensure secure communication.

[0483] Input: Captured image file

[0484] Output: Image data sent to the server

[0485] Specific operation: The device uses 5G or Wi-Fi to send image data to the server.

[0486] Step 4: The server receives the image.

[0487] The server receives image data sent from the terminal. The received images are stored in the database.

[0488] Input: Image data sent from the device

[0489] Output: Image data stored in the database

[0490] Specific operation: The server's receiving module acquires image data and saves it to the database.

[0491] Step 5: The server analyzes the image.

[0492] The server analyzes the received image data and extracts features of the object the user is interested in. Feature extraction is performed using deep learning frameworks (such as TensorFlow or PyTorch).

[0493] Input: Received image data

[0494] Output: Extracted feature data

[0495] Specific operation: The server's image analysis module analyzes the data and extracts features.

[0496] Step 6: The server generates the search query.

[0497] The server generates search queries based on the extracted features. Queries appropriate to the object are generated, such as "men's black sneakers."

[0498] Input: Extracted feature data

[0499] Output: Generated search query

[0500] Specific operation: The server's query generation algorithm creates a query based on the feature data.

[0501] Step 7: The server sends a query to the online shopping platform.

[0502] The generated search query is sent to the API of an online shopping platform. For example, the APIs of Amazon or Rakuten are used.

[0503] Input: Generated search query

[0504] Output: Query sent to the online shopping platform

[0505] Specific operation: The server formats the API request and sends the query.

[0506] Step 8: The server receives the search results.

[0507] The server receives search results from various online shopping platforms. The received data is then parsed in formats such as JSON.

[0508] Input: Search results data from an online shopping platform

[0509] Output: Analyzed search result data

[0510] Specific operation: The server parses the received data using a JSON parser and extracts the necessary information.

[0511] Step 9: The server organizes the search results.

[0512] The server that receives the search results organizes them by price or popularity and formats them into a user-friendly format.

[0513] Input: Analyzed search result data

[0514] Output: Formatted search result data

[0515] Specific operation: The server's data organization algorithm sorts the data and arranges it into an easily understandable format.

[0516] Step 10: Send organized search results to your device.

[0517] The server sends the organized search results back to the smart glasses. Encryption is used during transmission to ensure secure communication.

[0518] Input: Formatted search result data

[0519] Output: Search result data sent to the terminal

[0520] Specific operation: The server encrypts the data and securely transmits it to the terminal.

[0521] Step 11: The device displays information as an overlay.

[0522] Smart glasses overlay information onto the user's field of view. The display design is optimized for easy viewing of the information.

[0523] Input: Search result data sent to the device

[0524] Output: Product information displayed as an overlay in the user's field of view.

[0525] Specific operation: The smart glasses overlay the received data onto the screen and provide it to the user.

[0526] (Application Example 1)

[0527] 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."

[0528] Traditional online shopping systems made it difficult for users to quickly obtain information about products they were interested in within a physical store, and they had limited means of efficiently comparing detailed information and multiple search results when selecting products. This limited the user's purchasing experience, making it difficult to make informed purchasing decisions. Furthermore, users had to go through the trouble of searching online for the same product after seeing it in the physical world.

[0529] 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.

[0530] In this invention, the server includes display means for providing images to the user's field of view, shooting means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating search queries based on the extracted features, search means for sending the generated search queries to an online platform to search for products, display means for displaying the searched product information to the user's field of view, and means for displaying product information, price, ratings, inventory status, and information on similar products in real time by focusing on real-world objects. This makes it easy for users to instantly obtain detailed information on products they find in physical stores, compare them, and make purchases. As a result, the user's purchasing experience is improved, and purchasing decisions can be made efficiently using the information.

[0531] "Display means that provide images to the user's field of view" refers to devices such as smart glasses or head-mounted displays worn by the user that visually present information to the user's field of view.

[0532] "A means of capturing images displayed in the user's field of view" refers to devices such as cameras mounted on smart glasses or head-mounted displays that capture the video of the user's field of view and save it as digital image data.

[0533] An "analysis method for analyzing captured images and extracting features" is a system that uses machine learning and image recognition technologies to analyze features such as the shape and texture of objects from captured field-of-view images and extract important information.

[0534] A "generation means for generating search queries based on extracted features" is a device that automatically generates appropriate keywords and phrases for searching for products online based on the features of an object extracted by an analysis means.

[0535] "A search method that sends generated search queries to an online platform to search for products" refers to a method that sends search queries generated via an internet connection to an online shopping platform and searches for related products on that platform.

[0536] A "display means for displaying searched product information within the user's field of view" is a device that organizes product information received from an online platform and presents it to the user by overlaying it on the user's smart glasses or head-mounted display.

[0537] "A means of displaying product information, price, ratings, stock status, and information on similar products in real time by focusing on real-world objects" refers to a device that has the function of instantly displaying detailed product information related to an object in the user's field of view simply by pointing smart glasses at an object that the user is interested in in the real world.

[0538] This invention relates to a system that provides real-time product information when a user wears smart glasses in a physical store and focuses on a product. The system captures images within the user's field of view, analyzes those images, and displays search results from an online shopping platform.

[0539] First, the user puts on smart glasses. These smart glasses have built-in display and camera functions, allowing the user to capture images of products by looking at them. The captured images are then sent from the device to a server via the internet.

[0540] The server analyzes received images using a deep learning framework such as TensorFlow. Through image analysis, the server extracts features such as the shape and texture of objects. Based on these extracted features, it generates a search query. This generated search query is sent to an online shopping platform to search for relevant products.

[0541] Product information obtained as search results (price, ratings, stock status, information on similar products, etc.) is organized on the server. The organized information is then sent back to the terminal and presented to the user through the smart glasses display. At this time, the searched product information is displayed as an overlay in the user's field of view, allowing for extremely intuitive information acquisition.

[0542] A concrete example would be when a user finds a summer dress in a shopping mall. By pointing smart glasses at the dress, the price, customer reviews, and stock availability of the product would instantly appear in their field of view. This would allow users to compare products in detail within the physical store and make it easier to choose the most suitable item on the spot.

[0543] An example of a prompt message for a generative AI model is as follows:

[0544] "Extract the product features from this image and generate search queries for an online shopping platform. Based on the results, write code to display the appropriate product information."

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The user puts on smart glasses and looks at a specific product.

[0548] Input: The actual product as seen by the user.

[0549] Operation: The smart glasses' camera captures images of products that come into the user's field of view.

[0550] Output: Captured image data.

[0551] Step 2:

[0552] The captured image data is sent from the smart glasses to the server.

[0553] Input: Captured image data.

[0554] Operation: The smart glasses upload image data to a server via the internet.

[0555] Output: Image data stored on the server.

[0556] Step 3:

[0557] The server analyzes the received image data and extracts the characteristics of the target object.

[0558] Input: Image data stored on the server.

[0559] Operation: The server analyzes images using deep learning frameworks such as TensorFlow to identify the shape and texture of objects.

[0560] Output: Feature data of the object.

[0561] Step 4:

[0562] The server generates search queries based on the feature data.

[0563] Input: Feature data of an object.

[0564] Operation: The server automatically generates appropriate search queries (e.g., "summer dress blue women's") based on the extracted features.

[0565] Output: The generated search query.

[0566] Step 5:

[0567] The generated search query is sent to the online shopping platform.

[0568] Input: The generated search query.

[0569] Operation: The server sends queries to the online shopping platform's API via the internet to search for relevant products.

[0570] Output: Search results (product information) returned from the online shopping platform.

[0571] Step 6:

[0572] The search results are organized on the server.

[0573] Input: Search results from an online shopping platform.

[0574] Operation: The server reorganizes information such as price, ratings, stock status, and related products, and presents it in a user-friendly format.

[0575] Output: Organized product information.

[0576] Step 7:

[0577] The organized product information is sent to the smart glasses and displayed as an overlay in the user's field of view.

[0578] Input: Organized product information.

[0579] Operation: Information is retransmitted from the server to the smart glasses and overlaid onto the field of view through the smart glasses' display mechanism.

[0580] Output: Product information displayed within the user's field of view.

[0581] In this way, the system for implementing the present invention provides users with immediate detailed information about products they see in physical stores, and supports them in making efficient purchasing decisions.

[0582] 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.

[0583] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. By combining this system with an emotion engine that recognizes the user's emotions, more appropriate search results can be provided. This system is implemented by the following means.

[0584] The user wears a display device such as smart glasses and focuses on a specific object in an everyday scene. The smart glasses, acting as the terminal, use a camera to capture images of what is in the user's field of view. These images are then transmitted to a server via the internet.

[0585] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[0586] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a query such as "black sneakers men" is generated. This search query is then sent to the online shopping platform.

[0587] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[0588] A distinctive feature of this invention is the introduction of an emotion engine, which enables the optimization of search results based on the user's emotions. The emotion engine recognizes the user's emotional state from their facial expressions and voice. The server analyzes the emotional data obtained from the emotion engine and adjusts the search query based on the user's current emotional state. For example, if the user is enjoying themselves, products that match that emotion can be displayed preferentially. Furthermore, a learning engine that learns the user's preferences using emotional data can also be incorporated. This learning engine analyzes the user's past emotional data and purchase history to provide individually optimized product suggestions.

[0589] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[0590] Furthermore, when a user looks at gloves, the emotion engine recognizes the user's emotions, and if the user is excited, it prioritizes displaying particularly popular gloves. In this way, users can instantly obtain detailed information about products that visually interest them and make efficient purchasing decisions. In addition, the accumulation of emotion data will allow future search results to be more tailored to the user's preferences.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The user wears smart glasses and focuses on specific objects in everyday situations.

[0594] Step 2:

[0595] The device (smart glasses) captures images of objects within the user's field of vision. It also collects emotional data from the user's facial expressions and voice.

[0596] Step 3:

[0597] The device transmits captured image data and emotion data to a server via the internet. A secure protocol (e.g., HTTPS) is used for transmission.

[0598] Step 4:

[0599] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[0600] Step 5:

[0601] The server uses an emotion engine to analyze emotion data sent from the terminal and recognize the user's emotional state. For example, it identifies emotions such as surprise, enjoyment, or interest.

[0602] Step 6:

[0603] The server generates appropriate search queries based on extracted feature information and sentiment data. For example, if a user is interested in and enjoying sneakers, a query such as "recommended black sneakers for men" might be generated.

[0604] Step 7:

[0605] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[0606] Step 8:

[0607] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[0608] Step 9:

[0609] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity based on emotional state.

[0610] Step 10:

[0611] The server then sends the organized and formatted search results back to the terminal.

[0612] Step 11:

[0613] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[0614] Step 12:

[0615] The device captures additional user reactions and sends this sentiment data to the server. This data is analyzed by a learning engine and used to improve future search accuracy.

[0616] (Example 2)

[0617] 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".

[0618] Traditional online shopping systems lacked efficient methods for acquiring information about items users were interested in and their characteristics. Furthermore, they couldn't present search results that considered the user's emotional state, making it difficult to recommend the most suitable products. This resulted in users spending a significant amount of time and effort searching for products and making purchase decisions.

[0619] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means that recognize the user's emotions and correct the search query based on those emotions, a learning device that analyzes past emotion data and purchase history to optimize search results, and display means that overlays the searched product information onto the user's field of view. This enables product recommendations that correspond to the user's emotions, resulting in efficient searching and optimal product suggestions.

[0620] A "display device" is a device that provides images to the user's field of vision.

[0621] A "shooting device" is a device used to capture images that are displayed in the user's field of view.

[0622] An "analysis device" is a device used to analyze captured images and extract features.

[0623] A "generation device" is a device that generates product search queries based on extracted features.

[0624] A "search device" is a device that sends generated product search queries to an online shopping platform to search for products.

[0625] "Display means" refers to the means of displaying searched product information within the user's field of view.

[0626] "Emotion recognition means" refers to a means of recognizing a user's emotions and correcting search queries based on those emotions.

[0627] A "learning device" is a device that analyzes past sentiment data and purchase history to optimize search results.

[0628] "Identification means" refers to means for identifying objects that the user is interested in from a captured image.

[0629] This invention relates to a system that efficiently acquires information displayed within a user's field of vision and presents search results on an online shopping platform. The system aims to efficiently understand the user's interests and provide appropriate search results by having the user wear a display device such as smart glasses and focus on specific objects in everyday scenes.

[0630] Hardware and software to use

[0631] The smart glasses (display devices) used by the user are equipped with a built-in camera (image capture device) and a display. The camera captures images within the user's field of view in real time, and this data is transmitted to a server via the internet. For analysis, the server uses deep learning frameworks such as TensorFlow and PyTorch to analyze the captured image data.

[0632] Emotion recognition and query generation

[0633] The server analyzes the received image data to extract features of the object the user is focusing on. Specifically, the server identifies objects within the image and extracts features such as their color, shape, and brand logo. Once feature extraction is complete, the server generates a product search query based on this information. For example, if the user is looking at black gloves, a search query such as "black gloves women's brand" will be generated.

[0634] Search and results provision

[0635] The generated search query is sent from the server to the online shopping platform. For example, it searches for products using APIs from Amazon or Rakuten. The information on multiple products obtained as search results is returned to the server. The server organizes these search results and formats them in a user-friendly format (such as by price or popularity). This information is then sent back to the smart glasses and displayed as an overlay in the user's field of view.

[0636] Emotional engine and learning engine

[0637] Furthermore, this invention incorporates an emotion engine to optimize search results by taking user emotions into consideration. The smart glasses monitor the user's facial expressions and voice, collecting emotion data in real time. This data is transmitted to a server and analyzed. For example, if the user is excited, the most suitable products based on that emotion (such as popular products) are displayed preferentially. In addition, the learning engine analyzes past emotion data and purchase history to optimize future search results to match the user's preferences.

[0638] Specific example

[0639] Let's say a user is in a shopping mall and becomes interested in a brand of gloves, and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server analyzes the image and generates a search query such as "black gloves women brand" and sends it to online shopping platforms. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses. In addition, an emotion engine recognizes the user's emotions, and if the user is excited, particularly popular gloves are prioritized in the display.

[0640] Example of a prompt

[0641] "Please describe in detail each step of the system's processing: when a user focuses on a specific object, smart glasses capture the image, a server performs image analysis, and then the system presents optimized shopping search results based on the user's emotion recognition by an emotion engine."

[0642] The above describes the embodiments for carrying out the present invention. This system efficiently acquires information about objects visible to the user and provides optimal product search results that take emotions into consideration, thereby improving the user's online shopping experience.

[0643] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0644] Step 1:

[0645] Capturing user attention information

[0646] The user wears smart glasses and focuses on a specific object in an everyday scene.

[0647] The device (smart glasses) uses its built-in camera to capture images in the user's field of view in real time.

[0648] Input: An image of an object the user sees.

[0649] Output: Captured image data.

[0650] Specific operation: For example, when a user looks at a branded pair of gloves in a shopping mall, the smart glasses' camera captures an image of those gloves.

[0651] Step 2:

[0652] Sending image data

[0653] The device (smart glasses) captures image data and sends it to the server via the internet.

[0654] Input: Captured image data.

[0655] Output: Image data sent to the server.

[0656] Specific operation: The smart glasses use Wi-Fi or a mobile network to upload images to a server in real time.

[0657] Step 3:

[0658] Image data analysis

[0659] The server analyzes the received image data and extracts features of the object the user is focusing on.

[0660] The analysis will use deep learning frameworks such as TensorFlow and PyTorch.

[0661] Input: Image data sent to the server.

[0662] Output: Feature data of the extracted objects.

[0663] Specific operation: Identify gloves in an image and analyze their features, such as color, brand logo, and shape, using a deep learning model.

[0664] Step 4:

[0665] Generating search queries

[0666] The server generates a product search query based on the extracted feature data.

[0667] Input: Feature data of the extracted object.

[0668] Output: The generated product search query.

[0669] Specific operation: For example, it automatically generates queries such as "black gloves women's brand".

[0670] Step 5:

[0671] Submit a search query

[0672] The server generates search queries and sends them to the online shopping platform.

[0673] Input: The generated product search query.

[0674] Output: Results of query submission to the online shopping platform.

[0675] Specific operation: Perform product searches using APIs from Amazon, Rakuten Market, etc.

[0676] Step 6:

[0677] Retrieving search results

[0678] The server receives search results obtained from the online shopping platform.

[0679] Input: Search results returned from an online shopping platform.

[0680] Output: Received search result data.

[0681] Specific operation: Retrieve data such as product information, prices, and reviews for multiple products.

[0682] Step 7:

[0683] Organizing and formatting search results

[0684] The server organizes the search results it retrieves and formats them into a user-friendly format.

[0685] Input: Received search result data.

[0686] Output: Organized and formatted search results data.

[0687] Specific actions: Sort by price or popularity, and organize product images and price information to display in a unified format.

[0688] Step 8:

[0689] Displaying search results

[0690] The server resends the organized and formatted search results to the device (smart glasses).

[0691] The device (smart glasses) overlays the search results onto the user's field of view.

[0692] Input: Organized and formatted search results data.

[0693] Output: Search results displayed on smart glasses.

[0694] Specific operation: Search results are overlaid on the smart glasses' display, aligned with the user's field of view.

[0695] Step 9:

[0696] Acquisition and analysis of emotional data

[0697] The device (smart glasses) uses its built-in camera and microphone to acquire emotional data from the user's facial expressions and voice.

[0698] The server analyzes the received emotional data to identify the user's emotional state.

[0699] Input: User facial expression data, voice data.

[0700] Output: Analyzed user emotional state data.

[0701] Specific operation: Use a facial recognition algorithm to determine whether the user is excited or relaxed.

[0702] Step 10:

[0703] Optimizing search results

[0704] The search results are optimized based on sentiment data acquired by the server.

[0705] The learning device analyzes past sentiment data and purchase history to further optimize future search results to match the user's preferences.

[0706] Input: User's emotional state data, past emotional data, purchase history.

[0707] Output: Optimized search results.

[0708] Specific operation: When the user is excited, popular products are displayed first, and the most suitable products are recommended individually based on past search history.

[0709] (Application Example 2)

[0710] 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."

[0711] Traditional online shopping systems lacked a way for users to instantly obtain detailed information about products they viewed in physical stores. Furthermore, they failed to provide optimal product information tailored to the user's emotional state, resulting in users spending considerable time selecting products. Additionally, personalized product recommendations based on user preferences and past purchase history were insufficient. This system aims to address these challenges and improve the user's purchasing experience.

[0712] 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.

[0713] In this invention, the server includes display means for providing images to the user's field of view, imaging means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating product search queries based on the extracted features, search means for sending the generated search queries to an online shopping platform to search for products, display means for displaying the searched product information in the user's field of view, emotion analysis means for recognizing the user's emotions, and optimization means for optimizing the searched product information based on the emotion information obtained from the emotion analysis means. This makes it possible to obtain detailed information about products viewed by the user in a physical store in real time, and further enables the provision of optimal product information according to the user's emotional state, thereby enabling personalized product suggestions.

[0714] A "display means" is a device that provides images to the user's field of vision.

[0715] An "imaging means" is a device for capturing images displayed in the user's field of view.

[0716] An "analysis means" is a device used to analyze captured images and extract features.

[0717] A "generation means" is a device for generating product search queries based on extracted features.

[0718] A "search device" is a device that sends a generated search query to an online shopping platform to search for products.

[0719] An "emotion analysis device" is a device used to recognize a user's emotions.

[0720] An "optimization means" is a device for optimizing product information retrieved based on emotional information obtained from an emotional analysis means.

[0721] This invention relates to a system that provides detailed information about a product in real time when a user views the product in a physical store. This system includes display means, imaging means, analysis means, generation means, search means, sentiment analysis means, and optimization means.

[0722] When a user wears a device such as a smartphone or smart glasses and is looking at products in a physical store, an imaging device captures images of the products that come into their field of view. The imaging device can be the built-in camera of the smart glasses or the camera of the smartphone. The captured images are transmitted to a server via the internet.

[0723] The server analyzes the received images using analysis tools and employs machine learning and image recognition technologies to extract product features. For example, deep learning frameworks such as TensorFlow can be used. This extracts features such as product type, brand, and color.

[0724] Next, the generation method generates a search query based on the extracted features. For example, a query such as "black sneakers men" is generated. The search query is sent to the online shopping platform by the search method. In this case, the search is performed using an API.

[0725] The search results returned from the online shopping platform are sent back to the server. Here, the emotion analysis system analyzes emotional information obtained from the user's facial expressions and voice to determine the user's current emotional state. OpenCV's facial recognition function and emotion analysis models can be used.

[0726] The optimization mechanism optimizes search results based on analyzed sentiment information. For example, if the user is enjoying themselves, products that match that sentiment, i.e., popular products, are displayed preferentially. The optimized search results are then sent back to the device and overlaid on the user's field of view through the display mechanism.

[0727] This allows users to instantly obtain detailed information about products that visually interest them, enabling them to make efficient purchase decisions. Furthermore, the accumulation of emotional data will allow future search results to better match the user's preferences.

[0728] Specific example

[0729] Let's say a user is looking at a specific pair of sneakers in a shopping mall. When the user views the sneakers through smart glasses, the camera captures an image and sends it to a server. The server analyzes the image and generates a search query, "black sneakers men," which is then sent to an online shopping platform. The search results are returned to the server and sorted by popularity based on the user's sentiment. The final search results are displayed as an overlay on the smart glasses.

[0730] Example of a prompt

[0731] "Create a feature that uses the image capture function of smart glasses to identify products currently in the user's field of view and then searches for them on an online shopping platform. Also, incorporate an algorithm that uses an emotion engine to analyze the user's emotions and optimize search results accordingly."

[0732] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0733] Step 1:

[0734] When a user views a product in a physical store using a smartphone or smart glasses, the device's camera captures an image of that product. The input is a real-time captured image, and the output is image data. The device then sends this image data to a server via the internet.

[0735] Step 2:

[0736] The server sends the received image data to the analysis tool. The analysis tool uses a deep learning model such as TensorFlow to extract product features from the image. The input is the captured image data, and the output is product feature data (e.g., color, shape, brand, etc.).

[0737] Step 3:

[0738] The server generates appropriate product search queries based on extracted feature data. For example, if the feature data is "black sneakers," the generated search query will be "black sneakers men's." The input is feature data, and the output is a search query.

[0739] Step 4:

[0740] The generated search query is sent to the online shopping platform via the search mechanism. The input is the search query, and the output is the search results returned by the online shopping platform.

[0741] Step 5:

[0742] The server receives the returned search results. In parallel, it uses the terminal's camera and microphone to capture the user's facial expressions and voice and sends them to the emotion analysis system. The emotion analysis system uses OpenCV's face recognition function and emotion analysis model to analyze the user's emotions. The input is the user's real-time video and audio data, and the output is emotion information (e.g., joy, surprise, excitement).

[0743] Step 6:

[0744] The server's optimization method optimizes search results based on acquired sentiment information. For example, if a user is excited, popular products are displayed preferentially. The input is sentiment information and search results, and the output is the optimized search results.

[0745] Step 7:

[0746] Optimized search results are transmitted to the device via the internet. They are then overlaid on the user's screen through the device's display. The input is the optimized search results, and the output is the product information displayed on the user's screen. This allows the user to check product information in real time and make efficient purchase decisions.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] [Third Embodiment]

[0751] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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).

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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".

[0763] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is implemented by the following means:

[0764] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is then transmitted to a server via the internet.

[0765] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[0766] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a search query such as "men's black sneakers" is generated. This search query is then sent to the online shopping platform.

[0767] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[0768] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[0769] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, compare them, and make purchases. In particular, the ability to obtain information in a natural way through a visual interface significantly improves the user experience.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] The user wears smart glasses and focuses on specific objects in everyday situations.

[0773] Step 2:

[0774] The device (smart glasses) captures images of objects within the user's field of view. Capture is triggered at regular intervals or by specific actions (for example, pressing a button).

[0775] Step 3:

[0776] The device sends the captured image data to the server via the internet. A secure protocol (e.g., HTTPS) is used for this transmission.

[0777] Step 4:

[0778] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[0779] Step 5:

[0780] The server generates appropriate search queries based on the extracted feature information. For example, if the recognized object is sneakers, it will generate queries such as "black sneakers men's".

[0781] Step 6:

[0782] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[0783] Step 7:

[0784] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[0785] Step 8:

[0786] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity.

[0787] Step 9:

[0788] The server then sends the organized and formatted search results back to the terminal.

[0789] Step 10:

[0790] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[0791] (Example 1)

[0792] 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."

[0793] Traditional online shopping systems have made it difficult for users to instantly obtain detailed information about products they are interested in in real time. Furthermore, methods for efficiently identifying specific objects and displaying related product information have been limited. To solve these problems, a system is needed that automatically recognizes objects within the user's field of view and quickly searches for and displays relevant information.

[0794] 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.

[0795] In this invention, the server includes a display means for capturing the user's visual information, an image capture means for photographing the user's visual information, a data analysis means for analyzing the captured image and extracting features, a query generation means for generating product search queries based on the extracted features, a data transmission means for sending the generated search queries to an online shopping platform, and an information display means for receiving search results and presenting them to the user. This makes it possible for the user to easily obtain detailed information about products that they are visually interested in in real time.

[0796] A "display means for capturing user's visual information" is a device for displaying information that is visible to the user.

[0797] "Image capture means for capturing user's visual information" refers to a device for capturing video or images that come into the user's field of vision.

[0798] "Data analysis means for analyzing captured images and extracting features" refers to a device or program for analyzing captured image data and extracting important features and attributes from it.

[0799] "Query generation means for generating product search queries based on extracted features" refers to a device or program for automatically generating appropriate product search queries based on analyzed features.

[0800] "Data transmission means for sending generated search queries to an online shopping platform" refers to a device or program for sending queries to an online shopping platform via the internet.

[0801] "Information display means for receiving search results and presenting them to the user" refers to a device or program for receiving search results from an online shopping platform and visually presenting them to the user.

[0802] "Object identification means for identifying objects of user interest from captured images" refers to a device or program for identifying specific objects of user interest from captured images.

[0803] "Visualization means for overlaying searched product information onto the user's visual field" refers to a device or program for displaying product information obtained as search results superimposed on the user's field of view.

[0804] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is specifically implemented by the following means.

[0805] System Overview

[0806] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is sent to a server via the internet. The server analyzes the received image and extracts the features of the object the user is focusing on. Machine learning and image recognition technologies are used for this feature extraction. For example, deep learning frameworks (such as TensorFlow or PyTorch) could be used. Based on the extracted features, the server generates an appropriate search query. The generated search query is sent to an online shopping platform, and the search results are returned to the server. The server organizes these results, formats them in a user-friendly format, and sends them to the terminal, where they are overlaid on the user's field of view through the smart glasses' display device.

[0807] Hardware and software used

[0808] Hardware:

[0809] Smart glasses (with built-in display and imaging devices for capturing visual information)

[0810] High-speed communication lines (5G and fiber optics)

[0811] software:

[0812] Image analysis module (using technologies such as TensorFlow and PyTorch)

[0813] Query generation module

[0814] Data transmission and reception module

[0815] API for online shopping platforms

[0816] Database management systems (MySQL, PostgreSQL)

[0817] Interface software for overlay display

[0818] Specific examples of how the system works

[0819] The following is a specific example of how this system works.

[0820] 1. The user is looking at branded gloves in a shopping mall.

[0821] 2. The smart glasses capture an image of the glove and send it to the server.

[0822] 3. The server extracts the features of the gloves through image analysis and generates a search query: "women's winter gloves brand".

[0823] 4. The generated search query is sent to the online shopping platform.

[0824] 5. Glove price and review information obtained from each platform is integrated and organized on the server.

[0825] 6. Organized information is overlaid onto the user's field of view through smart glasses.

[0826] Example of a prompt

[0827] Examples of prompt messages are shown below.

[0828] "I'm looking at a pair of gloves from a certain brand. Please search for more information about these gloves and display related product prices and reviews."

[0829] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, making comparisons and purchases easy. Because information is obtained naturally through a visual interface, the user experience is significantly improved.

[0830] Based on the above description, the present invention is now presented in its specific embodiments. The scope of the present invention is limited by the claims, and the above specific embodiments are merely examples to illustrate its technical scope.

[0831] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0832] Step 1: The user focuses on the object.

[0833] The user wears smart glasses and focuses on a specific object. Focusing on a specific object within their field of view for a certain period of time triggers image capture.

[0834] Input: User's visual information (object of focus)

[0835] Specific action: The user stares at a specific object within their field of view.

[0836] Step 2: The device captures the image.

[0837] The smart glasses, acting as the device, use their built-in camera to capture images within the user's field of view. The captured images are temporarily stored on the device.

[0838] Input: Images seen through the user's field of view

[0839] Output: Captured image file

[0840] Specific operation: The smart glasses' camera takes an image and saves the data.

[0841] Step 3: The device sends the image to the server.

[0842] The captured images are sent from the smart glasses to a server via the internet. Encryption is used during transmission to ensure secure communication.

[0843] Input: Captured image file

[0844] Output: Image data sent to the server

[0845] Specific operation: The device uses 5G or Wi-Fi to send image data to the server.

[0846] Step 4: The server receives the image.

[0847] The server receives image data sent from the terminal. The received images are stored in the database.

[0848] Input: Image data sent from the device

[0849] Output: Image data stored in the database

[0850] Specific operation: The server's receiving module acquires image data and saves it to the database.

[0851] Step 5: The server analyzes the image.

[0852] The server analyzes the received image data and extracts features of the object the user is interested in. Feature extraction is performed using deep learning frameworks (such as TensorFlow or PyTorch).

[0853] Input: Received image data

[0854] Output: Extracted feature data

[0855] Specific operation: The server's image analysis module analyzes the data and extracts features.

[0856] Step 6: The server generates the search query.

[0857] The server generates search queries based on the extracted features. Queries appropriate to the object are generated, such as "men's black sneakers."

[0858] Input: Extracted feature data

[0859] Output: Generated search query

[0860] Specific operation: The server's query generation algorithm creates a query based on the feature data.

[0861] Step 7: The server sends a query to the online shopping platform.

[0862] The generated search query is sent to the API of an online shopping platform. For example, the APIs of Amazon or Rakuten are used.

[0863] Input: Generated search query

[0864] Output: Query sent to the online shopping platform

[0865] Specific operation: The server formats the API request and sends the query.

[0866] Step 8: The server receives the search results.

[0867] The server receives search results from various online shopping platforms. The received data is then parsed in formats such as JSON.

[0868] Input: Search results data from an online shopping platform

[0869] Output: Analyzed search result data

[0870] Specific operation: The server parses the received data using a JSON parser and extracts the necessary information.

[0871] Step 9: The server organizes the search results.

[0872] The server that receives the search results organizes them by price or popularity and formats them into a user-friendly format.

[0873] Input: Analyzed search result data

[0874] Output: Formatted search result data

[0875] Specific operation: The server's data organization algorithm sorts the data and arranges it into an easily understandable format.

[0876] Step 10: Send organized search results to your device.

[0877] The server sends the organized search results back to the smart glasses. Encryption is used during transmission to ensure secure communication.

[0878] Input: Formatted search result data

[0879] Output: Search result data sent to the terminal

[0880] Specific operation: The server encrypts the data and securely transmits it to the terminal.

[0881] Step 11: The device displays information as an overlay.

[0882] Smart glasses overlay information onto the user's field of view. The display design is optimized for easy viewing of the information.

[0883] Input: Search result data sent to the device

[0884] Output: Product information displayed as an overlay in the user's field of view.

[0885] Specific operation: The smart glasses overlay the received data onto the screen and provide it to the user.

[0886] (Application Example 1)

[0887] 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."

[0888] Traditional online shopping systems made it difficult for users to quickly obtain information about products they were interested in within a physical store, and they had limited means of efficiently comparing detailed information and multiple search results when selecting products. This limited the user's purchasing experience, making it difficult to make informed purchasing decisions. Furthermore, users had to go through the trouble of searching online for the same product after seeing it in the physical world.

[0889] 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.

[0890] In this invention, the server includes display means for providing images to the user's field of view, shooting means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating search queries based on the extracted features, search means for sending the generated search queries to an online platform to search for products, display means for displaying the searched product information to the user's field of view, and means for displaying product information, price, ratings, inventory status, and information on similar products in real time by focusing on real-world objects. This makes it easy for users to instantly obtain detailed information on products they find in physical stores, compare them, and make purchases. As a result, the user's purchasing experience is improved, and purchasing decisions can be made efficiently using the information.

[0891] "Display means that provide images to the user's field of view" refers to devices such as smart glasses or head-mounted displays worn by the user that visually present information to the user's field of view.

[0892] "A means of capturing images displayed in the user's field of view" refers to devices such as cameras mounted on smart glasses or head-mounted displays that capture the video of the user's field of view and save it as digital image data.

[0893] An "analysis method for analyzing captured images and extracting features" is a system that uses machine learning and image recognition technologies to analyze features such as the shape and texture of objects from captured field-of-view images and extract important information.

[0894] A "generation means for generating search queries based on extracted features" is a device that automatically generates appropriate keywords and phrases for searching for products online based on the features of an object extracted by an analysis means.

[0895] "A search method that sends generated search queries to an online platform to search for products" refers to a method that sends search queries generated via an internet connection to an online shopping platform and searches for related products on that platform.

[0896] A "display means for displaying searched product information within the user's field of view" is a device that organizes product information received from an online platform and presents it to the user by overlaying it on the user's smart glasses or head-mounted display.

[0897] "A means of displaying product information, price, ratings, stock status, and information on similar products in real time by focusing on real-world objects" refers to a device that has the function of instantly displaying detailed product information related to an object in the user's field of view simply by pointing smart glasses at an object that the user is interested in in the real world.

[0898] This invention relates to a system that provides real-time product information when a user wears smart glasses in a physical store and focuses on a product. The system captures images within the user's field of view, analyzes those images, and displays search results from an online shopping platform.

[0899] First, the user puts on smart glasses. These smart glasses have built-in display and camera functions, allowing the user to capture images of products by looking at them. The captured images are then sent from the device to a server via the internet.

[0900] The server analyzes received images using a deep learning framework such as TensorFlow. Through image analysis, the server extracts features such as the shape and texture of objects. Based on these extracted features, it generates a search query. This generated search query is sent to an online shopping platform to search for relevant products.

[0901] Product information obtained as search results (price, ratings, stock status, information on similar products, etc.) is organized on the server. The organized information is then sent back to the terminal and presented to the user through the smart glasses display. At this time, the searched product information is displayed as an overlay in the user's field of view, allowing for extremely intuitive information acquisition.

[0902] A concrete example would be when a user finds a summer dress in a shopping mall. By pointing smart glasses at the dress, the price, customer reviews, and stock availability of the product would instantly appear in their field of view. This would allow users to compare products in detail within the physical store and make it easier to choose the most suitable item on the spot.

[0903] An example of a prompt message for a generative AI model is as follows:

[0904] "Extract the product features from this image and generate search queries for an online shopping platform. Based on the results, write code to display the appropriate product information."

[0905] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0906] Step 1:

[0907] The user puts on smart glasses and looks at a specific product.

[0908] Input: The actual product as seen by the user.

[0909] Operation: The smart glasses' camera captures images of products that come into the user's field of view.

[0910] Output: Captured image data.

[0911] Step 2:

[0912] The captured image data is sent from the smart glasses to the server.

[0913] Input: Captured image data.

[0914] Operation: The smart glasses upload image data to a server via the internet.

[0915] Output: Image data stored on the server.

[0916] Step 3:

[0917] The server analyzes the received image data and extracts the characteristics of the target object.

[0918] Input: Image data stored on the server.

[0919] Operation: The server analyzes images using deep learning frameworks such as TensorFlow to identify the shape and texture of objects.

[0920] Output: Feature data of the object.

[0921] Step 4:

[0922] The server generates search queries based on the feature data.

[0923] Input: Feature data of an object.

[0924] Operation: The server automatically generates appropriate search queries (e.g., "summer dress blue women's") based on the extracted features.

[0925] Output: The generated search query.

[0926] Step 5:

[0927] The generated search query is sent to the online shopping platform.

[0928] Input: The generated search query.

[0929] Operation: The server sends queries to the online shopping platform's API via the internet to search for relevant products.

[0930] Output: Search results (product information) returned from the online shopping platform.

[0931] Step 6:

[0932] The search results are organized on the server.

[0933] Input: Search results from an online shopping platform.

[0934] Operation: The server reorganizes information such as price, ratings, stock status, and related products, and presents it in a user-friendly format.

[0935] Output: Organized product information.

[0936] Step 7:

[0937] The organized product information is sent to the smart glasses and displayed as an overlay in the user's field of view.

[0938] Input: Organized product information.

[0939] Operation: Information is retransmitted from the server to the smart glasses and overlaid onto the field of view through the smart glasses' display mechanism.

[0940] Output: Product information displayed within the user's field of view.

[0941] In this way, the system for implementing the present invention provides users with immediate detailed information about products they see in physical stores, and supports them in making efficient purchasing decisions.

[0942] 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.

[0943] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. By combining this system with an emotion engine that recognizes the user's emotions, more appropriate search results can be provided. This system is implemented by the following means.

[0944] The user wears a display device such as smart glasses and focuses on a specific object in an everyday scene. The smart glasses, acting as the terminal, use a camera to capture images of what is in the user's field of view. These images are then transmitted to a server via the internet.

[0945] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[0946] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a query such as "black sneakers men" is generated. This search query is then sent to the online shopping platform.

[0947] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[0948] A distinctive feature of this invention is the introduction of an emotion engine, which enables the optimization of search results based on the user's emotions. The emotion engine recognizes the user's emotional state from their facial expressions and voice. The server analyzes the emotional data obtained from the emotion engine and adjusts the search query based on the user's current emotional state. For example, if the user is enjoying themselves, products that match that emotion can be displayed preferentially. Furthermore, a learning engine that learns the user's preferences using emotional data can also be incorporated. This learning engine analyzes the user's past emotional data and purchase history to provide individually optimized product suggestions.

[0949] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[0950] Furthermore, when a user looks at gloves, the emotion engine recognizes the user's emotions, and if the user is excited, it prioritizes displaying particularly popular gloves. In this way, users can instantly obtain detailed information about products that visually interest them and make efficient purchasing decisions. In addition, the accumulation of emotion data will allow future search results to be more tailored to the user's preferences.

[0951] The following describes the processing flow.

[0952] Step 1:

[0953] The user wears smart glasses and focuses on specific objects in everyday situations.

[0954] Step 2:

[0955] The device (smart glasses) captures images of objects within the user's field of vision. It also collects emotional data from the user's facial expressions and voice.

[0956] Step 3:

[0957] The device transmits captured image data and emotion data to a server via the internet. A secure protocol (e.g., HTTPS) is used for transmission.

[0958] Step 4:

[0959] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[0960] Step 5:

[0961] The server uses an emotion engine to analyze emotion data sent from the terminal and recognize the user's emotional state. For example, it identifies emotions such as surprise, enjoyment, or interest.

[0962] Step 6:

[0963] The server generates appropriate search queries based on extracted feature information and sentiment data. For example, if a user is interested in and enjoying sneakers, a query such as "recommended black sneakers for men" might be generated.

[0964] Step 7:

[0965] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[0966] Step 8:

[0967] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[0968] Step 9:

[0969] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity based on emotional state.

[0970] Step 10:

[0971] The server then sends the organized and formatted search results back to the terminal.

[0972] Step 11:

[0973] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[0974] Step 12:

[0975] The device captures additional user reactions and sends this sentiment data to the server. This data is analyzed by a learning engine and used to improve future search accuracy.

[0976] (Example 2)

[0977] 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."

[0978] Traditional online shopping systems lacked efficient methods for acquiring information about items users were interested in and their characteristics. Furthermore, they couldn't present search results that considered the user's emotional state, making it difficult to recommend the most suitable products. This resulted in users spending a significant amount of time and effort searching for products and making purchase decisions.

[0979] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means that recognize the user's emotions and correct the search query based on those emotions, a learning device that analyzes past emotion data and purchase history to optimize search results, and display means that overlays the searched product information onto the user's field of view. This enables product recommendations that correspond to the user's emotions, resulting in efficient searching and optimal product suggestions.

[0980] A "display device" is a device that provides images to the user's field of vision.

[0981] A "shooting device" is a device used to capture images that are displayed in the user's field of view.

[0982] An "analysis device" is a device used to analyze captured images and extract features.

[0983] A "generation device" is a device that generates product search queries based on extracted features.

[0984] A "search device" is a device that sends generated product search queries to an online shopping platform to search for products.

[0985] "Display means" refers to the means of displaying searched product information within the user's field of view.

[0986] "Emotion recognition means" refers to a means of recognizing a user's emotions and correcting search queries based on those emotions.

[0987] A "learning device" is a device that analyzes past sentiment data and purchase history to optimize search results.

[0988] "Identification means" refers to means for identifying objects that the user is interested in from a captured image.

[0989] This invention relates to a system that efficiently acquires information displayed within a user's field of vision and presents search results on an online shopping platform. The system aims to efficiently understand the user's interests and provide appropriate search results by having the user wear a display device such as smart glasses and focus on specific objects in everyday scenes.

[0990] Hardware and software to use

[0991] The smart glasses (display devices) used by the user are equipped with a built-in camera (image capture device) and a display. The camera captures images within the user's field of view in real time, and this data is transmitted to a server via the internet. For analysis, the server uses deep learning frameworks such as TensorFlow and PyTorch to analyze the captured image data.

[0992] Emotion recognition and query generation

[0993] The server analyzes the received image data to extract features of the object the user is focusing on. Specifically, the server identifies objects within the image and extracts features such as their color, shape, and brand logo. Once feature extraction is complete, the server generates a product search query based on this information. For example, if the user is looking at black gloves, a search query such as "black gloves women's brand" will be generated.

[0994] Search and results provision

[0995] The generated search query is sent from the server to the online shopping platform. For example, it searches for products using APIs from Amazon or Rakuten. The information on multiple products obtained as search results is returned to the server. The server organizes these search results and formats them in a user-friendly format (such as by price or popularity). This information is then sent back to the smart glasses and displayed as an overlay in the user's field of view.

[0996] Emotional engine and learning engine

[0997] Furthermore, this invention incorporates an emotion engine to optimize search results by taking user emotions into consideration. The smart glasses monitor the user's facial expressions and voice, collecting emotion data in real time. This data is transmitted to a server and analyzed. For example, if the user is excited, the most suitable products based on that emotion (such as popular products) are displayed preferentially. In addition, the learning engine analyzes past emotion data and purchase history to optimize future search results to match the user's preferences.

[0998] Specific example

[0999] Let's say a user is in a shopping mall and becomes interested in a brand of gloves, and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server analyzes the image and generates a search query such as "black gloves women brand" and sends it to online shopping platforms. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses. In addition, an emotion engine recognizes the user's emotions, and if the user is excited, particularly popular gloves are prioritized in the display.

[1000] Example of a prompt

[1001] "Please describe in detail each step of the system's processing: when a user focuses on a specific object, smart glasses capture the image, a server performs image analysis, and then the system presents optimized shopping search results based on the user's emotion recognition by an emotion engine."

[1002] The above describes the embodiments for carrying out the present invention. This system efficiently acquires information about objects visible to the user and provides optimal product search results that take emotions into consideration, thereby improving the user's online shopping experience.

[1003] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1004] Step 1:

[1005] Capturing user attention information

[1006] The user wears smart glasses and focuses on a specific object in an everyday scene.

[1007] The device (smart glasses) uses its built-in camera to capture images in the user's field of view in real time.

[1008] Input: An image of an object the user sees.

[1009] Output: Captured image data.

[1010] Specific operation: For example, when a user looks at a branded pair of gloves in a shopping mall, the smart glasses' camera captures an image of those gloves.

[1011] Step 2:

[1012] Sending image data

[1013] The device (smart glasses) captures image data and sends it to the server via the internet.

[1014] Input: Captured image data.

[1015] Output: Image data sent to the server.

[1016] Specific operation: The smart glasses use Wi-Fi or a mobile network to upload images to a server in real time.

[1017] Step 3:

[1018] Image data analysis

[1019] The server analyzes the received image data and extracts features of the object the user is focusing on.

[1020] The analysis will use deep learning frameworks such as TensorFlow and PyTorch.

[1021] Input: Image data sent to the server.

[1022] Output: Feature data of the extracted objects.

[1023] Specific operation: Identify gloves in an image and analyze their features, such as color, brand logo, and shape, using a deep learning model.

[1024] Step 4:

[1025] Generating search queries

[1026] The server generates a product search query based on the extracted feature data.

[1027] Input: Feature data of the extracted object.

[1028] Output: The generated product search query.

[1029] Specific operation: For example, it automatically generates queries such as "black gloves women's brand".

[1030] Step 5:

[1031] Submit a search query

[1032] The server generates search queries and sends them to the online shopping platform.

[1033] Input: The generated product search query.

[1034] Output: Results of query submission to the online shopping platform.

[1035] Specific operation: Perform product searches using APIs from Amazon, Rakuten Market, etc.

[1036] Step 6:

[1037] Retrieving search results

[1038] The server receives search results obtained from the online shopping platform.

[1039] Input: Search results returned from an online shopping platform.

[1040] Output: Received search result data.

[1041] Specific operation: Retrieve data such as product information, prices, and reviews for multiple products.

[1042] Step 7:

[1043] Organizing and formatting search results

[1044] The server organizes the search results it retrieves and formats them into a user-friendly format.

[1045] Input: Received search result data.

[1046] Output: Organized and formatted search results data.

[1047] Specific actions: Sort by price or popularity, and organize product images and price information to display in a unified format.

[1048] Step 8:

[1049] Displaying search results

[1050] The server resends the organized and formatted search results to the device (smart glasses).

[1051] The device (smart glasses) overlays the search results onto the user's field of view.

[1052] Input: Organized and formatted search results data.

[1053] Output: Search results displayed on smart glasses.

[1054] Specific operation: Search results are overlaid on the smart glasses' display, aligned with the user's field of view.

[1055] Step 9:

[1056] Acquisition and analysis of emotional data

[1057] The device (smart glasses) uses its built-in camera and microphone to acquire emotional data from the user's facial expressions and voice.

[1058] The server analyzes the received emotional data to identify the user's emotional state.

[1059] Input: User facial expression data, voice data.

[1060] Output: Analyzed user emotional state data.

[1061] Specific operation: Use a facial recognition algorithm to determine whether the user is excited or relaxed.

[1062] Step 10:

[1063] Optimizing search results

[1064] The search results are optimized based on sentiment data acquired by the server.

[1065] The learning device analyzes past sentiment data and purchase history to further optimize future search results to match the user's preferences.

[1066] Input: User's emotional state data, past emotional data, purchase history.

[1067] Output: Optimized search results.

[1068] Specific operation: When the user is excited, popular products are displayed first, and the most suitable products are recommended individually based on past search history.

[1069] (Application Example 2)

[1070] 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."

[1071] Traditional online shopping systems lacked a way for users to instantly obtain detailed information about products they viewed in physical stores. Furthermore, they failed to provide optimal product information tailored to the user's emotional state, resulting in users spending considerable time selecting products. Additionally, personalized product recommendations based on user preferences and past purchase history were insufficient. This system aims to address these challenges and improve the user's purchasing experience.

[1072] 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.

[1073] In this invention, the server includes display means for providing images to the user's field of view, imaging means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating product search queries based on the extracted features, search means for sending the generated search queries to an online shopping platform to search for products, display means for displaying the searched product information in the user's field of view, emotion analysis means for recognizing the user's emotions, and optimization means for optimizing the searched product information based on the emotion information obtained from the emotion analysis means. This makes it possible to obtain detailed information about products viewed by the user in a physical store in real time, and further enables the provision of optimal product information according to the user's emotional state, thereby enabling personalized product suggestions.

[1074] A "display means" is a device that provides images to the user's field of vision.

[1075] An "imaging means" is a device for capturing images displayed in the user's field of view.

[1076] An "analysis means" is a device used to analyze captured images and extract features.

[1077] A "generation means" is a device for generating product search queries based on extracted features.

[1078] A "search device" is a device that sends a generated search query to an online shopping platform to search for products.

[1079] An "emotion analysis device" is a device used to recognize a user's emotions.

[1080] An "optimization means" is a device for optimizing product information retrieved based on emotional information obtained from an emotional analysis means.

[1081] This invention relates to a system that provides detailed information about a product in real time when a user views the product in a physical store. This system includes display means, imaging means, analysis means, generation means, search means, sentiment analysis means, and optimization means.

[1082] When a user wears a device such as a smartphone or smart glasses and is looking at products in a physical store, an imaging device captures images of the products that come into their field of view. The imaging device can be the built-in camera of the smart glasses or the camera of the smartphone. The captured images are transmitted to a server via the internet.

[1083] The server analyzes the received images using analysis tools and employs machine learning and image recognition technologies to extract product features. For example, deep learning frameworks such as TensorFlow can be used. This extracts features such as product type, brand, and color.

[1084] Next, the generation method generates a search query based on the extracted features. For example, a query such as "black sneakers men" is generated. The search query is sent to the online shopping platform by the search method. In this case, the search is performed using an API.

[1085] The search results returned from the online shopping platform are sent back to the server. Here, the emotion analysis system analyzes emotional information obtained from the user's facial expressions and voice to determine the user's current emotional state. OpenCV's facial recognition function and emotion analysis models can be used.

[1086] The optimization mechanism optimizes search results based on analyzed sentiment information. For example, if the user is enjoying themselves, products that match that sentiment, i.e., popular products, are displayed preferentially. The optimized search results are then sent back to the device and overlaid on the user's field of view through the display mechanism.

[1087] This allows users to instantly obtain detailed information about products that visually interest them, enabling them to make efficient purchase decisions. Furthermore, the accumulation of emotional data will allow future search results to better match the user's preferences.

[1088] Specific example

[1089] Let's say a user is looking at a specific pair of sneakers in a shopping mall. When the user views the sneakers through smart glasses, the camera captures an image and sends it to a server. The server analyzes the image and generates a search query, "black sneakers men," which is then sent to an online shopping platform. The search results are returned to the server and sorted by popularity based on the user's sentiment. The final search results are displayed as an overlay on the smart glasses.

[1090] Example of a prompt

[1091] "Create a feature that uses the image capture function of smart glasses to identify products currently in the user's field of view and then searches for them on an online shopping platform. Also, incorporate an algorithm that uses an emotion engine to analyze the user's emotions and optimize search results accordingly."

[1092] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1093] Step 1:

[1094] When a user views a product in a physical store using a smartphone or smart glasses, the device's camera captures an image of that product. The input is a real-time captured image, and the output is image data. The device then sends this image data to a server via the internet.

[1095] Step 2:

[1096] The server sends the received image data to the analysis tool. The analysis tool uses a deep learning model such as TensorFlow to extract product features from the image. The input is the captured image data, and the output is product feature data (e.g., color, shape, brand, etc.).

[1097] Step 3:

[1098] The server generates appropriate product search queries based on extracted feature data. For example, if the feature data is "black sneakers," the generated search query will be "black sneakers men's." The input is feature data, and the output is a search query.

[1099] Step 4:

[1100] The generated search query is sent to the online shopping platform via the search mechanism. The input is the search query, and the output is the search results returned by the online shopping platform.

[1101] Step 5:

[1102] The server receives the returned search results. In parallel, it uses the terminal's camera and microphone to capture the user's facial expressions and voice and sends them to the emotion analysis system. The emotion analysis system uses OpenCV's face recognition function and emotion analysis model to analyze the user's emotions. The input is the user's real-time video and audio data, and the output is emotion information (e.g., joy, surprise, excitement).

[1103] Step 6:

[1104] The server's optimization method optimizes search results based on acquired sentiment information. For example, if a user is excited, popular products are displayed preferentially. The input is sentiment information and search results, and the output is the optimized search results.

[1105] Step 7:

[1106] Optimized search results are transmitted to the device via the internet. They are then overlaid on the user's screen through the device's display. The input is the optimized search results, and the output is the product information displayed on the user's screen. This allows the user to check product information in real time and make efficient purchase decisions.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] [Fourth Embodiment]

[1111] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1112] 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.

[1113] 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).

[1114] 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.

[1115] 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.

[1116] 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).

[1117] 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.

[1118] 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.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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.

[1123] 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".

[1124] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is implemented by the following means:

[1125] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is then transmitted to a server via the internet.

[1126] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[1127] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a search query such as "men's black sneakers" is generated. This search query is then sent to the online shopping platform.

[1128] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[1129] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[1130] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, compare them, and make purchases. In particular, the ability to obtain information in a natural way through a visual interface significantly improves the user experience.

[1131] The following describes the processing flow.

[1132] Step 1:

[1133] The user wears smart glasses and focuses on specific objects in everyday situations.

[1134] Step 2:

[1135] The device (smart glasses) captures images of objects within the user's field of view. Capture is triggered at regular intervals or by specific actions (for example, pressing a button).

[1136] Step 3:

[1137] The device sends the captured image data to the server via the internet. A secure protocol (e.g., HTTPS) is used for this transmission.

[1138] Step 4:

[1139] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[1140] Step 5:

[1141] The server generates appropriate search queries based on the extracted feature information. For example, if the recognized object is sneakers, it will generate queries such as "black sneakers men's".

[1142] Step 6:

[1143] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[1144] Step 7:

[1145] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[1146] Step 8:

[1147] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity.

[1148] Step 9:

[1149] The server then sends the organized and formatted search results back to the terminal.

[1150] Step 10:

[1151] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[1152] (Example 1)

[1153] 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".

[1154] Traditional online shopping systems have made it difficult for users to instantly obtain detailed information about products they are interested in in real time. Furthermore, methods for efficiently identifying specific objects and displaying related product information have been limited. To solve these problems, a system is needed that automatically recognizes objects within the user's field of view and quickly searches for and displays relevant information.

[1155] 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.

[1156] In this invention, the server includes a display means for capturing the user's visual information, an image capture means for photographing the user's visual information, a data analysis means for analyzing the captured image and extracting features, a query generation means for generating product search queries based on the extracted features, a data transmission means for sending the generated search queries to an online shopping platform, and an information display means for receiving search results and presenting them to the user. This makes it possible for the user to easily obtain detailed information about products that they are visually interested in in real time.

[1157] A "display means for capturing user's visual information" is a device for displaying information that is visible to the user.

[1158] "Image capture means for capturing user's visual information" refers to a device for capturing video or images that come into the user's field of vision.

[1159] "Data analysis means for analyzing captured images and extracting features" refers to a device or program for analyzing captured image data and extracting important features and attributes from it.

[1160] "Query generation means for generating product search queries based on extracted features" refers to a device or program for automatically generating appropriate product search queries based on analyzed features.

[1161] "Data transmission means for sending generated search queries to an online shopping platform" refers to a device or program for sending queries to an online shopping platform via the internet.

[1162] "Information display means for receiving search results and presenting them to the user" refers to a device or program for receiving search results from an online shopping platform and visually presenting them to the user.

[1163] "Object identification means for identifying objects of user interest from captured images" refers to a device or program for identifying specific objects of user interest from captured images.

[1164] "Visualization means for overlaying searched product information onto the user's visual field" refers to a device or program for displaying product information obtained as search results superimposed on the user's field of view.

[1165] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. This system is specifically implemented by the following means.

[1166] System Overview

[1167] The user wears a display device such as smart glasses and focuses on a specific object. The smart glasses, acting as the terminal, use a camera to capture an image of what is in the user's field of view. This image is sent to a server via the internet. The server analyzes the received image and extracts the features of the object the user is focusing on. Machine learning and image recognition technologies are used for this feature extraction. For example, deep learning frameworks (such as TensorFlow or PyTorch) could be used. Based on the extracted features, the server generates an appropriate search query. The generated search query is sent to an online shopping platform, and the search results are returned to the server. The server organizes these results, formats them in a user-friendly format, and sends them to the terminal, where they are overlaid on the user's field of view through the smart glasses' display device.

[1168] Hardware and software used

[1169] Hardware:

[1170] Smart glasses (with built-in display and imaging devices for capturing visual information)

[1171] High-speed communication lines (5G and fiber optics)

[1172] software:

[1173] Image analysis module (using technologies such as TensorFlow and PyTorch)

[1174] Query generation module

[1175] Data transmission and reception module

[1176] API for online shopping platforms

[1177] Database management systems (MySQL, PostgreSQL)

[1178] Interface software for overlay display

[1179] Specific examples of how the system works

[1180] The following is a specific example of how this system works.

[1181] 1. The user is looking at branded gloves in a shopping mall.

[1182] 2. The smart glasses capture an image of the glove and send it to the server.

[1183] 3. The server extracts the features of the gloves through image analysis and generates a search query: "women's winter gloves brand".

[1184] 4. The generated search query is sent to the online shopping platform.

[1185] 5. Glove price and review information obtained from each platform is integrated and organized on the server.

[1186] 6. Organized information is overlaid onto the user's field of view through smart glasses.

[1187] Example of a prompt

[1188] Examples of prompt messages are shown below.

[1189] "I'm looking at a pair of gloves from a certain brand. Please search for more information about these gloves and display related product prices and reviews."

[1190] This system allows users to instantly obtain detailed information about products they see in physical stores or on the street, making comparisons and purchases easy. Because information is obtained naturally through a visual interface, the user experience is significantly improved.

[1191] Based on the above description, the present invention is now presented in its specific embodiments. The scope of the present invention is limited by the claims, and the above specific embodiments are merely examples to illustrate its technical scope.

[1192] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1193] Step 1: The user focuses on the object.

[1194] The user wears smart glasses and focuses on a specific object. Focusing on a specific object within their field of view for a certain period of time triggers image capture.

[1195] Input: User's visual information (object of focus)

[1196] Specific action: The user stares at a specific object within their field of view.

[1197] Step 2: The device captures the image.

[1198] The smart glasses, acting as the device, use their built-in camera to capture images within the user's field of view. The captured images are temporarily stored on the device.

[1199] Input: Images seen through the user's field of view

[1200] Output: Captured image file

[1201] Specific operation: The smart glasses' camera takes an image and saves the data.

[1202] Step 3: The device sends the image to the server.

[1203] The captured images are sent from the smart glasses to a server via the internet. Encryption is used during transmission to ensure secure communication.

[1204] Input: Captured image file

[1205] Output: Image data sent to the server

[1206] Specific operation: The device uses 5G or Wi-Fi to send image data to the server.

[1207] Step 4: The server receives the image.

[1208] The server receives image data sent from the terminal. The received images are stored in the database.

[1209] Input: Image data sent from the device

[1210] Output: Image data stored in the database

[1211] Specific operation: The server's receiving module acquires image data and saves it to the database.

[1212] Step 5: The server analyzes the image.

[1213] The server analyzes the received image data and extracts features of the object the user is interested in. Feature extraction is performed using deep learning frameworks (such as TensorFlow or PyTorch).

[1214] Input: Received image data

[1215] Output: Extracted feature data

[1216] Specific operation: The server's image analysis module analyzes the data and extracts features.

[1217] Step 6: The server generates the search query.

[1218] The server generates search queries based on the extracted features. Queries appropriate to the object are generated, such as "men's black sneakers."

[1219] Input: Extracted feature data

[1220] Output: Generated search query

[1221] Specific operation: The server's query generation algorithm creates a query based on the feature data.

[1222] Step 7: The server sends a query to the online shopping platform.

[1223] The generated search query is sent to the API of an online shopping platform. For example, the APIs of Amazon or Rakuten are used.

[1224] Input: Generated search query

[1225] Output: Query sent to the online shopping platform

[1226] Specific operation: The server formats the API request and sends the query.

[1227] Step 8: The server receives the search results.

[1228] The server receives search results from various online shopping platforms. The received data is then parsed in formats such as JSON.

[1229] Input: Search results data from an online shopping platform

[1230] Output: Analyzed search result data

[1231] Specific operation: The server parses the received data using a JSON parser and extracts the necessary information.

[1232] Step 9: The server organizes the search results.

[1233] The server that receives the search results organizes them by price or popularity and formats them into a user-friendly format.

[1234] Input: Analyzed search result data

[1235] Output: Formatted search result data

[1236] Specific operation: The server's data organization algorithm sorts the data and arranges it into an easily understandable format.

[1237] Step 10: Send organized search results to your device.

[1238] The server sends the organized search results back to the smart glasses. Encryption is used during transmission to ensure secure communication.

[1239] Input: Formatted search result data

[1240] Output: Search result data sent to the terminal

[1241] Specific operation: The server encrypts the data and securely transmits it to the terminal.

[1242] Step 11: The device displays information as an overlay.

[1243] Smart glasses overlay information onto the user's field of view. The display design is optimized for easy viewing of the information.

[1244] Input: Search result data sent to the device

[1245] Output: Product information displayed as an overlay in the user's field of view.

[1246] Specific operation: The smart glasses overlay the received data onto the screen and provide it to the user.

[1247] (Application Example 1)

[1248] 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".

[1249] Traditional online shopping systems made it difficult for users to quickly obtain information about products they were interested in within a physical store, and they had limited means of efficiently comparing detailed information and multiple search results when selecting products. This limited the user's purchasing experience, making it difficult to make informed purchasing decisions. Furthermore, users had to go through the trouble of searching online for the same product after seeing it in the physical world.

[1250] 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.

[1251] In this invention, the server includes display means for providing images to the user's field of view, shooting means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating search queries based on the extracted features, search means for sending the generated search queries to an online platform to search for products, display means for displaying the searched product information to the user's field of view, and means for displaying product information, price, ratings, inventory status, and information on similar products in real time by focusing on real-world objects. This makes it easy for users to instantly obtain detailed information on products they find in physical stores, compare them, and make purchases. As a result, the user's purchasing experience is improved, and purchasing decisions can be made efficiently using the information.

[1252] "Display means that provide images to the user's field of view" refers to devices such as smart glasses or head-mounted displays worn by the user that visually present information to the user's field of view.

[1253] "A means of capturing images displayed in the user's field of view" refers to devices such as cameras mounted on smart glasses or head-mounted displays that capture the video of the user's field of view and save it as digital image data.

[1254] An "analysis method for analyzing captured images and extracting features" is a system that uses machine learning and image recognition technologies to analyze features such as the shape and texture of objects from captured field-of-view images and extract important information.

[1255] A "generation means for generating search queries based on extracted features" is a device that automatically generates appropriate keywords and phrases for searching for products online based on the features of an object extracted by an analysis means.

[1256] "A search method that sends generated search queries to an online platform to search for products" refers to a method that sends search queries generated via an internet connection to an online shopping platform and searches for related products on that platform.

[1257] A "display means for displaying searched product information within the user's field of view" is a device that organizes product information received from an online platform and presents it to the user by overlaying it on the user's smart glasses or head-mounted display.

[1258] "A means of displaying product information, price, ratings, stock status, and information on similar products in real time by focusing on real-world objects" refers to a device that has the function of instantly displaying detailed product information related to an object in the user's field of view simply by pointing smart glasses at an object that the user is interested in in the real world.

[1259] This invention relates to a system that provides real-time product information when a user wears smart glasses in a physical store and focuses on a product. The system captures images within the user's field of view, analyzes those images, and displays search results from an online shopping platform.

[1260] First, the user puts on smart glasses. These smart glasses have built-in display and camera functions, allowing the user to capture images of products by looking at them. The captured images are then sent from the device to a server via the internet.

[1261] The server analyzes received images using a deep learning framework such as TensorFlow. Through image analysis, the server extracts features such as the shape and texture of objects. Based on these extracted features, it generates a search query. This generated search query is sent to an online shopping platform to search for relevant products.

[1262] Product information obtained as search results (price, ratings, stock status, information on similar products, etc.) is organized on the server. The organized information is then sent back to the terminal and presented to the user through the smart glasses display. At this time, the searched product information is displayed as an overlay in the user's field of view, allowing for extremely intuitive information acquisition.

[1263] A concrete example would be when a user finds a summer dress in a shopping mall. By pointing smart glasses at the dress, the price, customer reviews, and stock availability of the product would instantly appear in their field of view. This would allow users to compare products in detail within the physical store and make it easier to choose the most suitable item on the spot.

[1264] An example of a prompt message for a generative AI model is as follows:

[1265] "Extract the product features from this image and generate search queries for an online shopping platform. Based on the results, write code to display the appropriate product information."

[1266] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1267] Step 1:

[1268] The user puts on smart glasses and looks at a specific product.

[1269] Input: The actual product as seen by the user.

[1270] Operation: The smart glasses' camera captures images of products that come into the user's field of view.

[1271] Output: Captured image data.

[1272] Step 2:

[1273] The captured image data is sent from the smart glasses to the server.

[1274] Input: Captured image data.

[1275] Operation: The smart glasses upload image data to a server via the internet.

[1276] Output: Image data stored on the server.

[1277] Step 3:

[1278] The server analyzes the received image data and extracts the characteristics of the target object.

[1279] Input: Image data stored on the server.

[1280] Operation: The server analyzes images using deep learning frameworks such as TensorFlow to identify the shape and texture of objects.

[1281] Output: Feature data of the object.

[1282] Step 4:

[1283] The server generates search queries based on the feature data.

[1284] Input: Feature data of an object.

[1285] Operation: The server automatically generates appropriate search queries (e.g., "summer dress blue women's") based on the extracted features.

[1286] Output: The generated search query.

[1287] Step 5:

[1288] The generated search query is sent to the online shopping platform.

[1289] Input: The generated search query.

[1290] Operation: The server sends queries to the online shopping platform's API via the internet to search for relevant products.

[1291] Output: Search results (product information) returned from the online shopping platform.

[1292] Step 6:

[1293] The search results are organized on the server.

[1294] Input: Search results from an online shopping platform.

[1295] Operation: The server reorganizes information such as price, ratings, stock status, and related products, and presents it in a user-friendly format.

[1296] Output: Organized product information.

[1297] Step 7:

[1298] The organized product information is sent to the smart glasses and displayed as an overlay in the user's field of view.

[1299] Input: Organized product information.

[1300] Operation: Information is retransmitted from the server to the smart glasses and overlaid onto the field of view through the smart glasses' display mechanism.

[1301] Output: Product information displayed within the user's field of view.

[1302] In this way, the system for implementing the present invention provides users with immediate detailed information about products they see in physical stores, and supports them in making efficient purchasing decisions.

[1303] 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.

[1304] This invention relates to a system for efficiently acquiring information displayed within a user's field of vision and presenting search results on an online shopping platform. By combining this system with an emotion engine that recognizes the user's emotions, more appropriate search results can be provided. This system is implemented by the following means.

[1305] The user wears a display device such as smart glasses and focuses on a specific object in an everyday scene. The smart glasses, acting as the terminal, use a camera to capture images of what is in the user's field of view. These images are then transmitted to a server via the internet.

[1306] The server analyzes the received image to extract features of the object the user is interested in. Machine learning and image recognition technologies can be used for this feature extraction. For example, deep learning frameworks could be employed.

[1307] Next, the server generates an appropriate search query based on the extracted features. For example, if the user is interested in sneakers, a query such as "black sneakers men" is generated. This search query is then sent to the online shopping platform.

[1308] The search results obtained from the search platform are returned to the server. The server organizes these results and formats them in a user-friendly format. For example, this may include sorting by price or popularity. These organized search results are then sent back to the device and overlaid on the user's field of view through the smart glasses' display.

[1309] A distinctive feature of this invention is the introduction of an emotion engine, which enables the optimization of search results based on the user's emotions. The emotion engine recognizes the user's emotional state from their facial expressions and voice. The server analyzes the emotional data obtained from the emotion engine and adjusts the search query based on the user's current emotional state. For example, if the user is enjoying themselves, products that match that emotion can be displayed preferentially. Furthermore, a learning engine that learns the user's preferences using emotional data can also be incorporated. This learning engine analyzes the user's past emotional data and purchase history to provide individually optimized product suggestions.

[1310] To give a concrete example, a user becomes interested in a particular brand of gloves at a shopping mall and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server extracts the features of the gloves through image analysis and generates a search query such as "women's winter gloves brand," which is then sent to an online shopping platform. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses.

[1311] Furthermore, when a user looks at gloves, the emotion engine recognizes the user's emotions, and if the user is excited, it prioritizes displaying particularly popular gloves. In this way, users can instantly obtain detailed information about products that visually interest them and make efficient purchasing decisions. In addition, the accumulation of emotion data will allow future search results to be more tailored to the user's preferences.

[1312] The following describes the processing flow.

[1313] Step 1:

[1314] The user wears smart glasses and focuses on specific objects in everyday situations.

[1315] Step 2:

[1316] The device (smart glasses) captures images of objects within the user's field of vision. It also collects emotional data from the user's facial expressions and voice.

[1317] Step 3:

[1318] The device transmits captured image data and emotion data to a server via the internet. A secure protocol (e.g., HTTPS) is used for transmission.

[1319] Step 4:

[1320] The server analyzes the received image data to extract features of the object the user is interested in. Image recognition technology and deep learning algorithms are used for this analysis.

[1321] Step 5:

[1322] The server uses an emotion engine to analyze emotion data sent from the terminal and recognize the user's emotional state. For example, it identifies emotions such as surprise, enjoyment, or interest.

[1323] Step 6:

[1324] The server generates appropriate search queries based on extracted feature information and sentiment data. For example, if a user is interested in and enjoying sneakers, a query such as "recommended black sneakers for men" might be generated.

[1325] Step 7:

[1326] The server sends the search query to the online shopping platform. In doing so, it uses the platform's API to make the search request.

[1327] Step 8:

[1328] The server receives search results from online shopping platforms. This includes information on multiple products, pricing, and customer reviews.

[1329] Step 9:

[1330] The server organizes and integrates the received search results and formats them into a user-friendly format. For example, it sorts product information by price or popularity based on emotional state.

[1331] Step 10:

[1332] The server then sends the organized and formatted search results back to the terminal.

[1333] Step 11:

[1334] The device overlays the received search results onto the user's field of view. This allows the user to instantly view details of products they are interested in based on the displayed information.

[1335] Step 12:

[1336] The device captures additional user reactions and sends this sentiment data to the server. This data is analyzed by a learning engine and used to improve future search accuracy.

[1337] (Example 2)

[1338] 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".

[1339] Traditional online shopping systems lacked efficient methods for acquiring information about items users were interested in and their characteristics. Furthermore, they couldn't present search results that considered the user's emotional state, making it difficult to recommend the most suitable products. This resulted in users spending a significant amount of time and effort searching for products and making purchase decisions.

[1340] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes emotion recognition means that recognize the user's emotions and correct the search query based on those emotions, a learning device that analyzes past emotion data and purchase history to optimize search results, and display means that overlays the searched product information onto the user's field of view. This enables product recommendations that correspond to the user's emotions, resulting in efficient searching and optimal product suggestions.

[1341] A "display device" is a device that provides images to the user's field of vision.

[1342] A "shooting device" is a device used to capture images that are displayed in the user's field of view.

[1343] An "analysis device" is a device used to analyze captured images and extract features.

[1344] A "generation device" is a device that generates product search queries based on extracted features.

[1345] A "search device" is a device that sends generated product search queries to an online shopping platform to search for products.

[1346] "Display means" refers to the means of displaying searched product information within the user's field of view.

[1347] "Emotion recognition means" refers to a means of recognizing a user's emotions and correcting search queries based on those emotions.

[1348] A "learning device" is a device that analyzes past sentiment data and purchase history to optimize search results.

[1349] "Identification means" refers to means for identifying objects that the user is interested in from a captured image.

[1350] This invention relates to a system that efficiently acquires information displayed within a user's field of vision and presents search results on an online shopping platform. The system aims to efficiently understand the user's interests and provide appropriate search results by having the user wear a display device such as smart glasses and focus on specific objects in everyday scenes.

[1351] Hardware and software to use

[1352] The smart glasses (display devices) used by the user are equipped with a built-in camera (image capture device) and a display. The camera captures images within the user's field of view in real time, and this data is transmitted to a server via the internet. For analysis, the server uses deep learning frameworks such as TensorFlow and PyTorch to analyze the captured image data.

[1353] Emotion recognition and query generation

[1354] The server analyzes the received image data to extract features of the object the user is focusing on. Specifically, the server identifies objects within the image and extracts features such as their color, shape, and brand logo. Once feature extraction is complete, the server generates a product search query based on this information. For example, if the user is looking at black gloves, a search query such as "black gloves women's brand" will be generated.

[1355] Search and results provision

[1356] The generated search query is sent from the server to the online shopping platform. For example, it searches for products using APIs from Amazon or Rakuten. The information on multiple products obtained as search results is returned to the server. The server organizes these search results and formats them in a user-friendly format (such as by price or popularity). This information is then sent back to the smart glasses and displayed as an overlay in the user's field of view.

[1357] Emotional engine and learning engine

[1358] Furthermore, this invention incorporates an emotion engine to optimize search results by taking user emotions into consideration. The smart glasses monitor the user's facial expressions and voice, collecting emotion data in real time. This data is transmitted to a server and analyzed. For example, if the user is excited, the most suitable products based on that emotion (such as popular products) are displayed preferentially. In addition, the learning engine analyzes past emotion data and purchase history to optimize future search results to match the user's preferences.

[1359] Specific example

[1360] Let's say a user is in a shopping mall and becomes interested in a brand of gloves, and stares at them. Smart glasses capture an image of the gloves and send it to a server. The server analyzes the image and generates a search query such as "black gloves women brand" and sends it to online shopping platforms. The price and review information for the gloves obtained from each platform is integrated and organized on the server and displayed as an overlay in the user's field of view through the smart glasses. In addition, an emotion engine recognizes the user's emotions, and if the user is excited, particularly popular gloves are prioritized in the display.

[1361] Example of a prompt

[1362] "Please describe in detail each step of the system's processing: when a user focuses on a specific object, smart glasses capture the image, a server performs image analysis, and then the system presents optimized shopping search results based on the user's emotion recognition by an emotion engine."

[1363] The above describes the embodiments for carrying out the present invention. This system efficiently acquires information about objects visible to the user and provides optimal product search results that take emotions into consideration, thereby improving the user's online shopping experience.

[1364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1365] Step 1:

[1366] Capturing user attention information

[1367] The user wears smart glasses and focuses on a specific object in an everyday scene.

[1368] The device (smart glasses) uses its built-in camera to capture images in the user's field of view in real time.

[1369] Input: An image of an object the user sees.

[1370] Output: Captured image data.

[1371] Specific operation: For example, when a user looks at a branded pair of gloves in a shopping mall, the smart glasses' camera captures an image of those gloves.

[1372] Step 2:

[1373] Sending image data

[1374] The device (smart glasses) captures image data and sends it to the server via the internet.

[1375] Input: Captured image data.

[1376] Output: Image data sent to the server.

[1377] Specific operation: The smart glasses use Wi-Fi or a mobile network to upload images to a server in real time.

[1378] Step 3:

[1379] Image data analysis

[1380] The server analyzes the received image data and extracts features of the object the user is focusing on.

[1381] The analysis will use deep learning frameworks such as TensorFlow and PyTorch.

[1382] Input: Image data sent to the server.

[1383] Output: Feature data of the extracted objects.

[1384] Specific operation: Identify gloves in an image and analyze their features, such as color, brand logo, and shape, using a deep learning model.

[1385] Step 4:

[1386] Generating search queries

[1387] The server generates a product search query based on the extracted feature data.

[1388] Input: Feature data of the extracted object.

[1389] Output: The generated product search query.

[1390] Specific operation: For example, it automatically generates queries such as "black gloves women's brand".

[1391] Step 5:

[1392] Submit a search query

[1393] The server generates search queries and sends them to the online shopping platform.

[1394] Input: The generated product search query.

[1395] Output: Results of query submission to the online shopping platform.

[1396] Specific operation: Perform product searches using APIs from Amazon, Rakuten Market, etc.

[1397] Step 6:

[1398] Retrieving search results

[1399] The server receives search results obtained from the online shopping platform.

[1400] Input: Search results returned from an online shopping platform.

[1401] Output: Received search result data.

[1402] Specific operation: Retrieve data such as product information, prices, and reviews for multiple products.

[1403] Step 7:

[1404] Organizing and formatting search results

[1405] The server organizes the search results it retrieves and formats them into a user-friendly format.

[1406] Input: Received search result data.

[1407] Output: Organized and formatted search results data.

[1408] Specific actions: Sort by price or popularity, and organize product images and price information to display in a unified format.

[1409] Step 8:

[1410] Displaying search results

[1411] The server resends the organized and formatted search results to the device (smart glasses).

[1412] The device (smart glasses) overlays the search results onto the user's field of view.

[1413] Input: Organized and formatted search results data.

[1414] Output: Search results displayed on smart glasses.

[1415] Specific operation: Search results are overlaid on the smart glasses' display, aligned with the user's field of view.

[1416] Step 9:

[1417] Acquisition and analysis of emotional data

[1418] The device (smart glasses) uses its built-in camera and microphone to acquire emotional data from the user's facial expressions and voice.

[1419] The server analyzes the received emotional data to identify the user's emotional state.

[1420] Input: User facial expression data, voice data.

[1421] Output: Analyzed user emotional state data.

[1422] Specific operation: Use a facial recognition algorithm to determine whether the user is excited or relaxed.

[1423] Step 10:

[1424] Optimizing search results

[1425] The search results are optimized based on sentiment data acquired by the server.

[1426] The learning device analyzes past sentiment data and purchase history to further optimize future search results to match the user's preferences.

[1427] Input: User's emotional state data, past emotional data, purchase history.

[1428] Output: Optimized search results.

[1429] Specific operation: When the user is excited, popular products are displayed first, and the most suitable products are recommended individually based on past search history.

[1430] (Application Example 2)

[1431] 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".

[1432] Traditional online shopping systems lacked a way for users to instantly obtain detailed information about products they viewed in physical stores. Furthermore, they failed to provide optimal product information tailored to the user's emotional state, resulting in users spending considerable time selecting products. Additionally, personalized product recommendations based on user preferences and past purchase history were insufficient. This system aims to address these challenges and improve the user's purchasing experience.

[1433] 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.

[1434] In this invention, the server includes display means for providing images to the user's field of view, imaging means for capturing images displayed in the user's field of view, analysis means for analyzing the captured images and extracting features, generation means for generating product search queries based on the extracted features, search means for sending the generated search queries to an online shopping platform to search for products, display means for displaying the searched product information in the user's field of view, emotion analysis means for recognizing the user's emotions, and optimization means for optimizing the searched product information based on the emotion information obtained from the emotion analysis means. This makes it possible to obtain detailed information about products viewed by the user in a physical store in real time, and further enables the provision of optimal product information according to the user's emotional state, thereby enabling personalized product suggestions.

[1435] A "display means" is a device that provides images to the user's field of vision.

[1436] An "imaging means" is a device for capturing images displayed in the user's field of view.

[1437] An "analysis means" is a device used to analyze captured images and extract features.

[1438] A "generation means" is a device for generating product search queries based on extracted features.

[1439] A "search device" is a device that sends a generated search query to an online shopping platform to search for products.

[1440] An "emotion analysis device" is a device used to recognize a user's emotions.

[1441] An "optimization means" is a device for optimizing product information retrieved based on emotional information obtained from an emotional analysis means.

[1442] This invention relates to a system that provides detailed information about a product in real time when a user views the product in a physical store. This system includes display means, imaging means, analysis means, generation means, search means, sentiment analysis means, and optimization means.

[1443] When a user wears a device such as a smartphone or smart glasses and is looking at products in a physical store, an imaging device captures images of the products that come into their field of view. The imaging device can be the built-in camera of the smart glasses or the camera of the smartphone. The captured images are transmitted to a server via the internet.

[1444] The server analyzes the received images using analysis tools and employs machine learning and image recognition technologies to extract product features. For example, deep learning frameworks such as TensorFlow can be used. This extracts features such as product type, brand, and color.

[1445] Next, the generation method generates a search query based on the extracted features. For example, a query such as "black sneakers men" is generated. The search query is sent to the online shopping platform by the search method. In this case, the search is performed using an API.

[1446] The search results returned from the online shopping platform are sent back to the server. Here, the emotion analysis system analyzes emotional information obtained from the user's facial expressions and voice to determine the user's current emotional state. OpenCV's facial recognition function and emotion analysis models can be used.

[1447] The optimization mechanism optimizes search results based on analyzed sentiment information. For example, if the user is enjoying themselves, products that match that sentiment, i.e., popular products, are displayed preferentially. The optimized search results are then sent back to the device and overlaid on the user's field of view through the display mechanism.

[1448] This allows users to instantly obtain detailed information about products that visually interest them, enabling them to make efficient purchase decisions. Furthermore, the accumulation of emotional data will allow future search results to better match the user's preferences.

[1449] Specific example

[1450] Let's say a user is looking at a specific pair of sneakers in a shopping mall. When the user views the sneakers through smart glasses, the camera captures an image and sends it to a server. The server analyzes the image and generates a search query, "black sneakers men," which is then sent to an online shopping platform. The search results are returned to the server and sorted by popularity based on the user's sentiment. The final search results are displayed as an overlay on the smart glasses.

[1451] Example of a prompt

[1452] "Create a feature that uses the image capture function of smart glasses to identify products currently in the user's field of view and then searches for them on an online shopping platform. Also, incorporate an algorithm that uses an emotion engine to analyze the user's emotions and optimize search results accordingly."

[1453] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1454] Step 1:

[1455] When a user views a product in a physical store using a smartphone or smart glasses, the device's camera captures an image of that product. The input is a real-time captured image, and the output is image data. The device then sends this image data to a server via the internet.

[1456] Step 2:

[1457] The server sends the received image data to the analysis tool. The analysis tool uses a deep learning model such as TensorFlow to extract product features from the image. The input is the captured image data, and the output is product feature data (e.g., color, shape, brand, etc.).

[1458] Step 3:

[1459] The server generates appropriate product search queries based on extracted feature data. For example, if the feature data is "black sneakers," the generated search query will be "black sneakers men's." The input is feature data, and the output is a search query.

[1460] Step 4:

[1461] The generated search query is sent to the online shopping platform via the search mechanism. The input is the search query, and the output is the search results returned by the online shopping platform.

[1462] Step 5:

[1463] The server receives the returned search results. In parallel, it uses the terminal's camera and microphone to capture the user's facial expressions and voice and sends them to the emotion analysis system. The emotion analysis system uses OpenCV's face recognition function and emotion analysis model to analyze the user's emotions. The input is the user's real-time video and audio data, and the output is emotion information (e.g., joy, surprise, excitement).

[1464] Step 6:

[1465] The server's optimization method optimizes search results based on acquired sentiment information. For example, if a user is excited, popular products are displayed preferentially. The input is sentiment information and search results, and the output is the optimized search results.

[1466] Step 7:

[1467] Optimized search results are transmitted to the device via the internet. They are then overlaid on the user's screen through the device's display. The input is the optimized search results, and the output is the product information displayed on the user's screen. This allows the user to check product information in real time and make efficient purchase decisions.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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."

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] The following is further disclosed regarding the embodiments described above.

[1490] (Claim 1)

[1491] A display device that provides images to the user's field of view,

[1492] A camera that captures images displayed in the user's field of view,

[1493] An analysis device that analyzes captured images and extracts features,

[1494] A generator that generates product search queries based on extracted features,

[1495] A search device that sends the generated search query to an online shopping platform to search for products,

[1496] A display means for displaying searched product information within the user's field of view,

[1497] A system that includes this.

[1498] (Claim 2)

[1499] The system according to claim 1, further comprising identification means for identifying an object of user interest from a captured image.

[1500] (Claim 3)

[1501] The system according to claim 1, further comprising display means for overlaying searched product information onto the user's field of view.

[1502]

[1503] "Example 1"

[1504] (Claim 1)

[1505] A display means for capturing the user's visual information,

[1506] An image capture means for capturing the user's visual information,

[1507] A data analysis means for analyzing captured images and extracting features,

[1508] A query generation means that generates product search queries based on extracted features,

[1509] A data transmission means for sending generated search queries to an online shopping platform,

[1510] A means of displaying information that receives search results and presents them to the user,

[1511] A system that includes this.

[1512] (Claim 2)

[1513] The system according to claim 1, further comprising object identification means for identifying an object of user interest from a captured image.

[1514] (Claim 3)

[1515] The system according to claim 1, further comprising visualization means for displaying searched product information as an overlay in the user's visual field.

[1516] "Application Example 1"

[1517] (Claim 1)

[1518] A display means that provides images to the user's field of view,

[1519] A means of capturing images that appear in the user's field of view,

[1520] An analysis means for analyzing captured images and extracting features,

[1521] A generation means for generating search queries based on extracted features,

[1522] A search method that sends the generated search query to an online platform to search for products,

[1523] A display means for displaying searched product information within the user's field of view,

[1524] By focusing on real-world objects, this provides a means to display product information, prices, ratings, inventory status, and information on similar products in real time.

[1525] A system that includes this.

[1526] (Claim 2)

[1527] The system according to claim 1, further comprising identification means for identifying an object of user interest from a captured image.

[1528] (Claim 3)

[1529] The system according to claim 1, further comprising display means for overlaying searched product information onto the user's field of view.

[1530] "Example 2 of combining an emotion engine"

[1531] (Claim 1)

[1532] A display device that provides images to the user's field of view,

[1533] A camera that captures images displayed in the user's field of view,

[1534] An analysis device that analyzes captured images and extracts features,

[1535] A generator that generates product search queries based on extracted features,

[1536] A search device that sends the generated product search query to an online shopping platform to search for products,

[1537] A display means for displaying searched product information within the user's field of view,

[1538] A sentiment recognition means that recognizes the user's emotions and corrects the search query based on those emotions,

[1539] A learning device that analyzes past emotional data and purchase history to optimize search results,

[1540] A system that includes this.

[1541] (Claim 2)

[1542] The system according to claim 1, further comprising identification means for identifying an object of user interest from a captured image.

[1543] (Claim 3)

[1544] The system according to claim 1, further comprising display means for overlaying searched product information onto the user's field of view.

[1545] "Application example 2 when combining with an emotional engine"

[1546] (Claim 1)

[1547] A display means that provides images to the user's field of view,

[1548] An imaging means for capturing images displayed in the user's field of view,

[1549] An analysis means for analyzing captured images and extracting features,

[1550] A generation means for generating product search queries based on extracted features,

[1551] A search method that sends the generated search query to an online shopping platform to search for products,

[1552] A display means for displaying searched product information within the user's field of view,

[1553] A means of analyzing user emotions,

[1554] An optimization means for optimizing product information retrieved based on emotional information obtained from an emotional analysis means,

[1555] A system that includes this.

[1556] (Claim 2)

[1557] The system according to claim 1, further comprising identification means for identifying an object of user interest from a captured image.

[1558] (Claim 3)

[1559] The system according to claim 1, further comprising display means for overlaying searched product information onto the user's field of view. [Explanation of symbols]

[1560] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A display device that provides images to the user's field of view, A camera that captures images displayed in the user's field of view, An analysis device that analyzes captured images and extracts features, A generator that generates product search queries based on extracted features, A search device that sends the generated search query to an online shopping platform to search for products, A display means for displaying searched product information within the user's field of view, A system that includes this.

2. The system according to claim 1, further comprising identification means for identifying an object of user interest from a captured image.

3. The system according to claim 1, further comprising display means for displaying searched product information as an overlay in the user's field of view.

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