System

Abstract

A system is provided.SOLUTION: A system, comprising: means for capturing video from devices worn by users; means for transmitting the video to a server; means for analyzing the transmitted video and recognizing objects; means for retrieving relevant information from a database based on the analysis; and means for transmitting and displaying the retrieved information to the users' devices; wherein the generated AI models are used to analyze the video in real-time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, it is becoming increasingly important for users to obtain information quickly and accurately. For example, while users desire detailed descriptions of tourist attractions, conventional methods make it difficult to obtain information easily because the information is scattered. There is also a demand for reliable information on store and product reviews to be obtained in real time. Furthermore, travelers continue to experience inconveniences due to the inability to instantly understand signs and menus in foreign languages. A comprehensive solution to these issues is needed. [Means for solving the problem]

[0005] This invention provides a system that uses a generative AI model to analyze video data in real time. Video data is captured from a device worn by the user and sent to a server. The server analyzes the video data and recognizes objects. Based on the analysis results, related information is retrieved from a database and sent to the user's device for display. This allows users to obtain detailed descriptions of tourist attractions, product reviews and ratings, foreign language translations, and more in real time. This system aims to provide users with information they can intuitively understand, making everyday life more convenient and efficient.

[0006] A "generative AI model" refers to an artificial intelligence model that has the ability to analyze and generate various data formats, particularly for analyzing images and text in real time.

[0007] "Video Data" refers to real-time visual information captured through the camera of a device worn by a User.

[0008] "Devices worn by users" refers to wearable devices such as glasses with augmented reality (AR) capabilities, which are used by users to obtain information about their surroundings in real time.

[0009] "Server" refers to a central computer system that receives and analyzes data and provides appropriate information based on the results of the analysis.

[0010] "Objects" refer to the objects and text information that the user sees through the device they wear, and analyzing these objects is the system's main purpose.

[0011] "Database" refers to a digital storage system that stores information to be provided based on the analysis results (such as details of tourist attractions, product reviews, and foreign language translations).

[0012] "Related information" refers to useful data such as detailed encyclopedia-like descriptions, reviews, and translations obtained from a database based on the analysis of video data. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a 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.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0035] Terminal side processing

[0036] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This device captures video data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0037] Server-side processing

[0038] The server receives the video data sent from the device. The received video data is input into a generative AI model, which analyzes the objects and text information. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of foreign language signs and menus.

[0039] Providing information

[0040] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0041] Specific examples

[0042] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0043] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0044] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0045] With these functions, the system of the present invention allows users to obtain detailed information in real time, making daily life, travel, shopping, etc. more convenient and efficient.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The device captures the user's view in real time, with the device's camera constantly capturing the latest video data.

[0049] Step 2:

[0050] The device sends the captured video data to the server using the device's network functions, ensuring low latency.

[0051] Step 3:

[0052] The server receives the video data sent from the device, temporarily stores it, and prepares it for analysis.

[0053] Step 4:

[0054] The server inputs the received video data into the generative AI model and begins analysis, where objects and text information are extracted.

[0055] Step 5:

[0056] The server accesses a database based on the analysis results to retrieve relevant information, such as tourist attraction information, product reviews, and foreign language translation results.

[0057] Step 6:

[0058] The server then sends the acquired information to the device, again using the device's network capabilities to quickly transmit the data.

[0059] Step 7:

[0060] The terminal displays the received information to the user: text information is displayed on a display, and audio information is played through a speaker.

[0061] Step 8:

[0062] The user can review the information provided and, if desired, take further action to obtain additional information, such as capturing a new object with the camera.

[0063] This allows users to easily obtain the information they need in real time, making their daily lives and travels more convenient and fulfilling.

[0064] Example 1

[0065] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0066] In modern society, it is extremely important for users to quickly and accurately obtain the information they need in real time in their daily lives, travel, shopping, etc. However, conventional technology does not adequately provide a system that can instantly analyze the information that users visually obtain and provide the results immediately. This poses the problem of users having to spend time and effort searching for the information they are looking for.

[0067] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0068] In this invention, the server includes means for acquiring visual information from a device worn by a user, means for transmitting the acquired visual information to a central processing unit, means for receiving the visual information in the central processing unit, means for analyzing the received visual information using a generative AI model, means for acquiring related data from an information storage device based on the analysis results, means for transmitting the acquired data to the user's device, and means for displaying the received data on the user's device, thereby enabling the user to acquire rapid and accurate information in real time based on their own visual information.

[0069] A "user-worn device" is a device worn by a user on the body, and includes a camera for acquiring visual information and a display for displaying information.

[0070] "Visual information" refers to video data captured by a camera, such as objects and text within the user's field of view.

[0071] A "central processing unit" is a device that receives visual information sent from a terminal and analyzes it using a generative AI model.

[0072] A "generative AI model" is a model that uses machine learning and deep learning techniques to analyze input data and recognize objects and text information.

[0073] An "information storage device" is a storage medium, such as a database or file system, accessed by a central processing unit, that holds related data.

[0074] "Related data" refers to information such as detailed descriptions, evaluations, and translations of the object, which is obtained based on the analyzed visual information.

[0075] The "display means" refers to a means for presenting information to a user, including a display built into a device worn by the user and an audio output device.

[0076] The "means for displaying received data" refers to a means for the device worn by the user to display relevant data received from the central processing unit.

[0077] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0078] Terminal side processing

[0079] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This video data is stored in temporary memory within the device. Once the device captures the video data, it transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0080] Server-side processing

[0081] The server receives the video data sent from the device. The received video data is input into a generative AI model (e.g., TensorFlow or PyTorch) to analyze the objects and text information. Based on the analysis results, the server accesses a database (information storage device) to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of signs and menus in foreign languages.

[0082] Providing information

[0083] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0084] Specific examples

[0085] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0086] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0087] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0088] Prompt Sentence Examples

[0089] The following prompt sentences can be used as input to a generative AI model:

[0090] 1. "I want the AR glasses to analyze the images being viewed by users in real time, and retrieve and display relevant information."

[0091] 2. "The names and background information of historical buildings installed at tourist sites will be used in real-time commentary."

[0092] 3. "Get and display reviews and ratings of products in stores in real time."

[0093] 4. "Translate foreign language signs and menus and display the translation results on the device display."

[0094] The system allows users to obtain detailed information in real time, making daily life, travel, shopping, and more convenient and efficient.

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1: Visual Capture

[0097] Device behavior

[0098] The camera in the device (AR glasses) worn by the user captures the user's field of view in real time. The camera image is the input, and the captured image data is generated as the output. This image data is temporarily stored in the device's memory.

[0099] Step 2: Sending video data

[0100] Device behavior

[0101] The device sends the captured video data to the server. The input is the captured video data, and the output is the data sent to the server. The data is encrypted and sent over a network connection.

[0102] Step 3: Receiving video data

[0103] Server Operation

[0104] The server receives video data sent from the terminal. The received video data is input, and the data stored in the buffer is generated as output. The received data is temporarily saved in the buffer.

[0105] Step 4: Data analysis

[0106] Server Operation

[0107] The server inputs the received video data into a generative AI model. The received video data is the input, and the analysis results are generated as the output. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to extract and recognize objects and text information.

[0108] Step 5: Obtain related information

[0109] Server Operation

[0110] The server retrieves related data from an information storage device (database) based on the analysis results. The analysis results are input, and the retrieved related information is generated as output. SQL queries are executed against the database to retrieve related details, reviews, and translation data.

[0111] Step 6: Sending acquired data

[0112] Server Operation

[0113] The server sends the acquired relevant information to the terminal. The acquired relevant information is the input, and data to be sent to the terminal is generated as the output. The data is packaged in a pre-specified format (for example, JSON format), encrypted, and sent.

[0114] Step 7: Display information

[0115] Device behavior

[0116] The device analyzes the information received from the server and displays it in the user's field of view. The received data is the input, and the information displayed on the display is generated as the output. Text information is overlaid on the display, and audio information is provided through the speaker.

[0117] Step 8: Verify the user

[0118] User behavior

[0119] The user checks the information displayed on the device's display and listens to audio information as needed. The displayed information is the input, and the user's understanding and judgment are the output. This allows the user to take action and make choices based on visual information in real time.

[0120] (Application example 1)

[0121] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] Traditional shopping experiences in brick-and-mortar stores require a lot of effort to obtain product information, making efficient shopping difficult. It also takes time to obtain detailed information, reviews, and sales information about specific products, and in some cases users may not be able to find the information they are looking for. Furthermore, there are also issues with long wait times when paying at the register, which degrades the user experience.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0124] In this invention, the server includes: means for analyzing video data in real time using a generative AI model; means for capturing video data from a display device worn by a user; means for transmitting the video data to the server via a network; means for analyzing the transmitted video data and recognizing objects; means for retrieving related information from a database based on the analysis results; means for transmitting and displaying the retrieved information on the user's display device; means for retrieving product names, prices, ratings, and reviews based on the captured product information; and means for providing the retrieved product information to the user via a virtual cart function and making payments. This allows users to obtain all information about products in physical stores in real time and efficiently shop. Furthermore, payment through the virtual cart function reduces waiting times at the cash register and provides a more comfortable shopping experience.

[0125] A "generative AI model" is an artificial intelligence algorithm used to analyze a user's visual data and recognize objects.

[0126] A "display device" is a screen or display device worn by a user to visually view information.

[0127] A "network" is a communications infrastructure used to send and receive information.

[0128] A "server" is a computer system that has a central processing unit for storing, processing, and analyzing data.

[0129] A "database" is an information system that systematically stores related information and makes it available.

[0130] "Virtual Cart Function" refers to a software function that allows users to virtually add and pay for selected items.

[0131] This invention is a system that enhances the shopping experience in brick-and-mortar stores by linking a user-worn display device with a server. The system uses a generative AI model to analyze video data in real time and provide the user with the information they need.

[0132] Terminal side processing

[0133] The display device worn by the user (e.g., AR glasses) is equipped with a camera and captures the user's field of view in real time. The captured video data is sent to a server via a network. The display device also has a display function to provide visual information to the user and an audio output device.

[0134] Server-side processing

[0135] The server receives the video data sent from the device and inputs it into the generative AI model. The generative AI model analyzes the video data and recognizes the object (e.g., product). Based on the recognition result, the server retrieves related information (price, reviews, ratings, sale information, etc.) from the database. The retrieved information is then sent back to the device and displayed on the user's display device.

[0136] Hardware and software used

[0137] Hardware:

[0138] User device: AR glasses (camera, display, speaker)

[0139] Server: High-performance AI server

[0140] Network: High-speed internet connection

[0141] software:

[0142] Generative AI models: image recognition models, natural language processing models

[0143] Frameworks: TensorFlow, PyTorch

[0144] Data Management: SQL Database

[0145] Specific examples

[0146] Consider a user browsing a store shelf. The AR glasses worn by the user capture the product and send the image data to a server. The server uses an image recognition model to identify the product and retrieves related details (e.g., product name, price, reviews) from a database. This information is then sent back to the device and displayed on the AR glasses' display. The user can then continue shopping while checking the information.

[0147] Furthermore, the virtual cart function allows users to virtually add their favorite products to their cart and make smart payments, reducing waiting times at the cash register and enabling efficient shopping.

[0148] Prompt Sentence Examples

[0149] The following prompts could be fed to a generative AI model:

[0150] "Recognize products from camera footage and instantly retrieve and display detailed product information, including product name, price, user reviews, and sales information. It also suggests related products and allows real-time payment using a virtual cart function."

[0151] This system allows users to enjoy a more comfortable and efficient shopping experience in physical stores.

[0152] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0153] Step 1:

[0154] The camera on the device (the display device worn by the user) captures the user's field of view in real time. The input is the video data from the camera, and the output is real-time video data.

[0155] Step 2:

[0156] The video data captured by the terminal is sent to the server via the network. The input is the video data captured by the terminal, and the output is the video data sent to the server.

[0157] Step 3:

[0158] The video data received by the server is input into a generative AI model, which analyzes and recognizes objects in the video. The input is the video data received by the server, and the output is the recognition result of the object. This analysis uses an image recognition model to specifically identify the product.

[0159] Step 4:

[0160] Based on the object recognition results, the server retrieves related information (product name, price, reviews, ratings, sale information, etc.) from the database. The input is the object recognition results, and the output is the product information retrieved from the database.

[0161] Step 5:

[0162] The server transmits the acquired product information to the terminal via the network. The input is the information acquired from the database, and the output is the product information transmitted to the terminal.

[0163] Step 6:

[0164] The terminal displays the product information received from the server in the user's field of view. The input is the product information sent from the server, and the output is the product information that the user can see through the display device.

[0165] Step 7:

[0166] When a user checks product information and adds it to the virtual cart, the terminal sends the information to the server, which then processes the addition of the product to the virtual cart. The input is the user's selection, and the output is the product information added to the virtual cart.

[0167] Step 8:

[0168] The server aggregates the product information in the virtual cart and processes the payment. The input is the product information added to the virtual cart, and the output is the payment completion status. At this time, a payment gateway is used if necessary.

[0169] At each step, the following specific actions are performed:

[0170] Step 1: The camera captures the user's field of view in high resolution.

[0171] Step 2: Send the video data to the server using Wi-Fi or mobile data.

[0172] Step 3: The generative AI model analyzes the data and recognizes the product.

[0173] Step 4: Retrieve product information from the database using an SQL query.

[0174] Step 5: Transfer information securely using the HTTPS protocol.

[0175] Step 6: Overlay information on the display.

[0176] Step 7: Add items to your virtual cart with touch or voice commands.

[0177] Step 8: Process the payment in real time via the payment gateway.

[0178] This allows users to shop smoothly and efficiently in-store.

[0179] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0180] This invention provides a system that combines an emotion engine that recognizes the user's emotions. This system uses a device worn by the user, a server, and a generative AI model to analyze video data in real time and provide the user with the information they need immediately. Furthermore, the emotion engine makes it possible to provide information according to the user's emotional state.

[0181] Terminal side processing

[0182] The device worn by the user (AR glasses) has features such as a camera and built-in sensors that capture the user's field of view and emotional state in real time. The device captures video data and emotional data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0183] Server-side processing

[0184] The server receives the video and emotion data sent from the device. The received video data is input into a generative AI model, which analyzes the object and text information. The emotion engine also analyzes the received emotion data to recognize the user's emotional state. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews for stores, and translations of signs and menus in foreign languages.

[0185] Providing information

[0186] The server adaptively customizes the information obtained through the analysis and sends it to the device. For example, if the user is excited, it provides more detailed explanations, and if the user is tired, it provides simple information. The device displays the received information in the user's field of view. Text information is displayed on the screen, and audio information is provided through the speaker.

[0187] Specific examples

[0188] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, which analyzes the image data and recognizes the name of the building and its historical background. At the same time, the emotion engine analyzes the user's emotional state and determines that the user is excited. Based on the analysis results, the server retrieves detailed explanatory information from the database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0189] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's satisfaction. This data is sent to a server, which analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and adjusts the detail of the reviews to reflect the user's satisfaction. The device displays this information on its screen, allowing the user to check detailed reviews and ratings for the product.

[0190] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's confusion. This data is sent to the server, which analyzes the image and extracts the foreign text. At the same time, the emotion engine analyzes the user's confusion and generates a detailed translation result that reflects this. Based on the analysis results, the server translates the text and sends it to the device. The device then displays the translation result on the screen, allowing the user to view the translated text in real time.

[0191] With these functions, the system of the present invention not only allows users to obtain detailed information in real time, but also provides customized information tailored to the user's emotional state, making daily life, travel, shopping, and more convenient and efficient.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] The device activates a camera to capture the user's field of view in real time, while simultaneously measuring the user's emotional state with an emotion sensor and capturing that data.

[0195] Step 2:

[0196] The user looks at an object (such as a tourist attraction or a product) they want to see through the AR glasses. At this time, the camera in the AR glasses captures the image of the object, and the built-in emotion sensor measures the user's emotion.

[0197] Step 3:

[0198] The device transmits the captured video data and emotion data to a server, where the data is transmitted over a network with low latency.

[0199] Step 4:

[0200] The server receives the video data and emotion data sent from the device and prepares the received data for analysis.

[0201] Step 5:

[0202] The server inputs the video data into a generative AI model that analyzes objects and text information, such as the names of tourist attractions and product details.

[0203] Step 6:

[0204] The server uses an emotion engine to analyze the received emotion data and recognize the user's emotional state, for example, determining whether the user is excited or confused.

[0205] Step 7:

[0206] Based on the analysis results, the server accesses a database to retrieve relevant information, such as detailed descriptions of tourist attractions, product reviews, and foreign language translations.

[0207] Step 8:

[0208] The server takes into account the user's emotional state and customizes the information it provides, for example providing detailed information if the user is excited and brief information if the user is tired.

[0209] Step 9:

[0210] The server sends the customized information to the device in the appropriate format (text, voice).

[0211] Step 10:

[0212] The terminal displays the received information to the user: text information on the display and audio information through the speaker.

[0213] Step 11:

[0214] The user reviews the displayed information and, if necessary, can request more detailed information or information about a different object, in which case the process begins again from step 1.

[0215] Through the above processing steps, users can not only obtain detailed information in real time, but also enjoy customized information tailored to their emotional state, making daily life, travel, shopping, etc. more convenient and efficient.

[0216] Example 2

[0217] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0218] Conventional information provision systems have difficulty in providing customized information based on the user's real-time emotional state. Furthermore, while they have advanced analytical capabilities such as recognizing specific objects and text information, they have problems in that they are unable to provide flexibility in the accuracy and delivery of information according to the user's emotions.

[0219] The identification process 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 means for capturing video data from a device worn by the user, means for detecting the user's emotional state using a built-in sensor, means for transmitting the captured video data and emotional data to the server, means for analyzing the transmitted video data using a generative AI model and recognizing objects and text information, means including an emotion engine for analyzing the emotional data and recognizing the user's emotional state, means for retrieving related information from a database based on the analysis result and the emotional state, and means for generating customized information and transmitting it to the user's device for display. This makes it possible to provide information customized according to the user's emotional state in real time.

[0220] A "device worn by the user" is a device such as AR glasses that can be worn by the user to take in images into their field of vision.

[0221] "Built-in sensors" are sensors built into devices to measure biometric information such as the user's heart rate and facial expressions.

[0222] "Video data" is digital data that captures video of objects or scenes that come into the user's field of view.

[0223] "Emotional data" refers to data that represents the user's emotional state as inferred from factors such as their heart rate and facial expression measured by the built-in sensor.

[0224] A "generative AI model" is an artificial intelligence model trained using large datasets, and is a technology for analyzing video data and recognizing objects and text information.

[0225] A "server" is a computer system that processes data received from a terminal over a network and returns the analysis results.

[0226] An "emotion engine" is an algorithm or software that analyzes emotional data to recognize a user's emotional state.

[0227] A "database" is a data storage system that stores related information and retrieves required information based on the results of analysis.

[0228] "Customized information" refers to detailed commentary, reviews, translations, and other information tailored to the user's emotional state.

[0229] This invention is a system that utilizes an emotion engine that recognizes the user's emotions and a generative AI model, and uses a device worn by the user and a server to analyze video data in real time and provide the user with the information they need instantly.

[0230] Hardware and Software Configuration

[0231] The system consists of the following major hardware and software components:

[0232] 1. Devices worn by users

[0233] The device worn by the user, such as AR glasses, is equipped with a camera and built-in sensors (such as a heart rate monitor and facial recognition sensor). This device captures images in the user's field of vision and simultaneously detects their emotional state. The device also includes a display and speaker, providing the acquired information visually and audibly to the user.

[0234] 2. Server

[0235] The server is a computer system that receives and processes data sent from the device worn by the user. The server is equipped with a generative AI model (e.g., implemented in TensorFlow or PyTorch) and an emotion engine (e.g., Emotion API or Affectiva).

[0236] Data processing and calculation

[0237] This system processes and calculates data through the following processes.

[0238] 1. Acquiring video data and emotion data

[0239] The user wears the device and captures the image of their field of vision. The built-in sensors detect the user's emotional state (e.g., excitement, satisfaction, confusion, etc.) in real time. This data is then sent from the device to a server.

[0240] 2. Analysis of video data and emotion data

[0241] On the server, the video data is first input into a generative AI model to recognize objects and text information, allowing it to identify, for example, specific tourist attractions or product names. At the same time, an emotion engine analyzes the emotion data and identifies the user's emotional state.

[0242] 3. Information Collection and Customization

[0243] Based on the analysis results of the generative AI model and emotion engine, the server retrieves relevant information from a database, such as the history and detailed descriptions of tourist attractions, product reviews and ratings, and translations of foreign language signs and menus. It then customizes the level of detail and format of the information depending on the user's emotional state.

[0244] 4. Provision of Information

[0245] The server then sends the customized information to the user's device, which then displays it visually or provides it as audio. For example, if the user is excited, detailed information is displayed, and if the user is tired, simple information is provided.

[0246] Specific use cases

[0247] As specific examples, we will explain visiting tourist spots, shopping, and translating signs and menus in foreign countries.

[0248] When visiting tourist spots

[0249] When a user visits a tourist attraction, they put on the AR glasses and look at the attraction. The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, where it is analyzed to identify the name and historical background. The emotion engine recognizes the user's excitement, and the server sends detailed explanatory information to the device. The user can view this information in real time on the display.

[0250] For shopping

[0251] When a user looks at a product in a store, the device captures the image as they look at it through the AR glasses, and the emotion sensor detects their level of satisfaction. The server analyzes this data to identify the product name and brand. Depending on their level of satisfaction, detailed reviews and ratings are provided.

[0252] Translation of foreign signs and menus

[0253] When a user sees a foreign sign or menu, the device captures the video and an emotion sensor detects confusion. The server analyzes the video and extracts the foreign language text. A detailed translation based on the user's emotional state is generated and sent to the user's device.

[0254] Prompt Sentence Examples

[0255] "Taking video data of a tourist spot and the user's emotional state as input, generate a detailed commentary and output customized information by providing additional information if the user is excited."

[0256] With the above configuration, the present invention can provide users with appropriate and customized information in real time, improving their experience in various situations.

[0257] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0258] Step 1:

[0259] The user puts on the AR glasses and captures the image that comes into their field of vision. Specifically, when the user directs their gaze at a specific tourist attraction or product, the device's camera captures the image as digital data. The input is the image that appears in their field of vision, and the output is video data. In addition, built-in sensors measure biometric information such as heart rate and facial expressions, and record this as emotional data.

[0260] Step 2:

[0261] The device transmits the captured video data and emotion data to the server. Specifically, the data is encrypted and transmitted over the network. The input is the video data and emotion data captured by the device, and the output is the encrypted data transferred to the server.

[0262] Step 3:

[0263] The server receives and decrypts the data sent from the terminal. Specifically, it decrypts the received data into an appropriate format and separates the video data and emotion data. The input at this time is encrypted video data and emotion data, and the output is decrypted video data and emotion data.

[0264] Step 4:

[0265] The server analyzes the decoded video data using a generative AI model to recognize objects and text information. For example, TensorFlow or PyTorch can be used to analyze the video data and identify tourist attractions or product names. The input is the decoded video data, and the output is the object information and text information resulting from the analysis.

[0266] Step 5:

[0267] The server analyzes the decoded emotional data with an emotion engine to recognize the user's emotional state. Specifically, it processes the emotional data using Emotion API and Affectiva to determine whether the user is excited, tired, or confused. The input is the decoded emotional data, and the output is the user's emotional state.

[0268] Step 6:

[0269] The server retrieves related information from a database based on the analysis results. For example, it searches the database for detailed descriptions of tourist attractions, product reviews, translations of foreign texts, etc. The input is the object information and the user's emotional state, and the output is related information.

[0270] Step 7:

[0271] The server customizes the acquired information according to the user's emotional state. Specifically, it generates detailed information when the user is excited and concise information when the user is tired. The input is the acquired related information and the user's emotional state, and the output is the customized information.

[0272] Step 8:

[0273] The server encrypts and transmits the customized information to the user's device. The device decrypts the received information and displays it on a display or provides it as audio information from a speaker. The input in this case is the customized information, and the output is information provided to the user visually or audibly.

[0274] (Application example 2)

[0275] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0276] Conventional systems acquire and analyze video data through a wearable device worn by the user, but because they do not provide information that takes the user's emotional state into consideration, the information provided does not necessarily match the user's needs or situation.The present invention aims to provide a system that combines an emotion engine and provides adaptive information according to the user's emotional state, thereby improving the user experience.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0278] In this invention, the server includes means for analyzing video data in real time using a generative AI model, means for capturing video data from a wearable device worn by a user, means for transmitting video data and emotion data to the server, means for analyzing the transmitted video data and recognizing objects, means for analyzing the user's emotion data using an emotion engine, means for retrieving related information from a database based on the analysis results, and means for transmitting the retrieved information to the user's wearable device and displaying it. This enables the provision of adaptive and detailed information according to the user's emotional state.

[0279] A "generative AI model" is a machine learning model that uses artificial intelligence to extract patterns and features from data and perform specific tasks.

[0280] "Real-time" refers to a processing method in which data is acquired and analyzed instantaneously, providing results immediately.

[0281] "Video Data" means visual information captured by a camera or other video capture device.

[0282] A "wearable device worn by a user" is an electronic device that can be worn by a user and has functions such as a camera, sensor, and display.

[0283] "Emotion data" is data that expresses the user's emotional state as numerical values ​​or categories.

[0284] "Transmitting means" refers to the functionality or technology for transferring data from one device to another.

[0285] A "server" is a computer system that processes, stores, and distributes data over a network.

[0286] "Target" refers to an object, place, person, etc. identified within video data.

[0287] An "emotion engine" is software or hardware that has the function of analyzing and recognizing the user's emotional state and outputting the results as data.

[0288] A "database" is a system for storing and managing data efficiently and systematically.

[0289] "Means for transmitting and displaying on a wearable device" refers to a function or technology for sending data to a wearable device and presenting information to the user using its display or audio output device.

[0290] The present invention relates to a system consisting of a wearable device (e.g., smart glasses) worn by a user and a server. This system can analyze video data in real time and provide information according to the user's emotional state.

[0291] The user wears a wearable device (smart glasses). The wearable device is equipped with a camera, an emotion sensor, a display, and an audio output device. The device captures the user's field of view and simultaneously monitors the user's emotional state in real time. The captured video data and emotion data are sent to a server.

[0292] When the server receives the video data, it uses the generative AI model to analyze the video data and recognize the object. For example, if a user looks at a product on a shelf, it recognizes the product and obtains information about it. It also uses an emotion engine to analyze the user's emotional data and understand their current emotional state. This allows the server to select appropriate information according to their emotional state.

[0293] Based on the analysis results, the server retrieves relevant information from the database. For example, if the user is excited, it displays detailed product reviews and ratings. If the user is confused, it provides a brief explanation and price comparison information. This information is sent to the wearable device and provided to the user through a display or audio output device.

[0294] As a concrete example, consider a user in a brick-and-mortar bookstore. The user puts on smart glasses and picks up a book. At this time, the camera in the smart glasses captures the book, and the emotion sensor detects the user's state of excitement. This data is sent to the server, which retrieves the book's title and detailed review and adaptively displays them to the user. The generation AI prompt sentence used is, "Please generate a book title and review. The user is excited, so please be detailed."

[0295] Furthermore, consider the case where a user is in an electronics store. The user puts on smart glasses and looks at a home appliance. The camera captures the product, and the emotion sensor detects a confused state. This allows the server to provide a brief description of the product's features and price comparison information. The generated AI prompt sentence is, "The user is confused. Please provide a brief description of the product's features and price comparison information."

[0296] In this way, the system of the present invention can provide appropriate information in real time according to the user's emotional state, thereby significantly improving the user experience.

[0297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0298] Step 1:

[0299] A user wears a wearable device (smart glasses) and visually recognizes products in a store. At this time, a camera captures the product image, and an emotion sensor acquires the user's emotion data in real time.

[0300] Input: Product video, emotion data

[0301] Output: Captured video and emotion data

[0302] Step 2:

[0303] The device transmits the captured video data and emotion data to a server using a communication module that bundles the video data and emotion data and transmits them to the server via a network.

[0304] Input: Captured video and emotion data

[0305] Output: Video and emotion data sent to the server

[0306] Step 3:

[0307] The server inputs the received video data into the generative AI model and performs video analysis. This is the process by which the generative AI model recognizes objects (e.g., products) from the video data.

[0308] Input: Video data sent to the server

[0309] Output: Data of recognized objects (products)

[0310] Step 4:

[0311] The server simultaneously inputs the received emotional data into the emotion engine, which analyzes the user's emotional state and quantifies it as happiness, excitement, confusion, or other emotional states.

[0312] Input: Emotion data sent to the server

[0313] Output: Parsed user's emotional state

[0314] Step 5:

[0315] The server retrieves relevant information from the database based on the object recognition results of the generative AI model and the emotional state analysis results of the emotion engine, such as product reviews and ratings, detailed product descriptions, and price information.

[0316] Input: Recognized object data, analyzed user emotional state

[0317] Output: Relevant information obtained

[0318] Step 6:

[0319] The server adaptively customizes the information it retrieves, tailoring it to the user's emotional state: for example, providing detailed reviews if the user is excited, or concise explanations if the user is confused.

[0320] Input: retrieved relevant information, analyzed user emotional state

[0321] Output: Customized information

[0322] Step 7:

[0323] The server sends the customized information to the terminal, which is then formatted as text data and sent to the wearable device via a network.

[0324] Input:Customized Information

[0325] Output: Information sent to the wearable device

[0326] Step 8:

[0327] The terminal (wearable device) displays the received information in the user's field of vision, showing detailed product reviews, ratings, and pricing information on the display, and providing audio information via an audio output device if necessary.

[0328] Input: Information sent to the wearable device

[0329] Output: Information displayed to the user's eyes or output as audio

[0330] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0331] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0332] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0333] [Second embodiment]

[0334] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0335] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0336] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0337] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0338] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0339] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0340] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0341] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0342] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0343] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0344] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0345] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0346] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0347] Terminal side processing

[0348] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This device captures video data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0349] Server-side processing

[0350] The server receives the video data sent from the device. The received video data is input into a generative AI model, which analyzes the objects and text information. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of foreign language signs and menus.

[0351] Providing information

[0352] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0353] Specific examples

[0354] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0355] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0356] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0357] With these functions, the system of the present invention allows users to obtain detailed information in real time, making daily life, travel, shopping, etc. more convenient and efficient.

[0358] The processing flow will be explained below.

[0359] Step 1:

[0360] The device captures the user's view in real time, with the device's camera constantly capturing the latest video data.

[0361] Step 2:

[0362] The device sends the captured video data to the server using the device's network functions, ensuring low latency.

[0363] Step 3:

[0364] The server receives the video data sent from the device, temporarily stores it, and prepares it for analysis.

[0365] Step 4:

[0366] The server inputs the received video data into the generative AI model and begins analysis, where objects and text information are extracted.

[0367] Step 5:

[0368] The server accesses a database based on the analysis results to retrieve relevant information, such as tourist attraction information, product reviews, and foreign language translation results.

[0369] Step 6:

[0370] The server then sends the acquired information to the device, again using the device's network capabilities to quickly transmit the data.

[0371] Step 7:

[0372] The terminal displays the received information to the user: text information is displayed on a display, and audio information is played through a speaker.

[0373] Step 8:

[0374] The user can review the information provided and, if desired, take further action to obtain additional information, such as capturing a new object with the camera.

[0375] This allows users to easily obtain the information they need in real time, making their daily lives and travels more convenient and fulfilling.

[0376] Example 1

[0377] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0378] In modern society, it is extremely important for users to quickly and accurately obtain the information they need in real time in their daily lives, travel, shopping, etc. However, conventional technology does not adequately provide a system that can instantly analyze the information that users visually obtain and provide the results immediately. This poses the problem of users having to spend time and effort searching for the information they are looking for.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0380] In this invention, the server includes means for acquiring visual information from a device worn by a user, means for transmitting the acquired visual information to a central processing unit, means for receiving the visual information in the central processing unit, means for analyzing the received visual information using a generative AI model, means for acquiring related data from an information storage device based on the analysis results, means for transmitting the acquired data to the user's device, and means for displaying the received data on the user's device, thereby enabling the user to acquire rapid and accurate information in real time based on their own visual information.

[0381] A "user-worn device" is a device worn by a user on the body, and includes a camera for acquiring visual information and a display for displaying information.

[0382] "Visual information" refers to video data captured by a camera, such as objects and text within the user's field of view.

[0383] A "central processing unit" is a device that receives visual information sent from a terminal and analyzes it using a generative AI model.

[0384] A "generative AI model" is a model that uses machine learning and deep learning techniques to analyze input data and recognize objects and text information.

[0385] An "information storage device" is a storage medium, such as a database or file system, accessed by a central processing unit, that holds related data.

[0386] "Related data" refers to information such as detailed descriptions, evaluations, and translations of the object, which is obtained based on the analyzed visual information.

[0387] The "display means" refers to a means for presenting information to a user, including a display built into a device worn by the user and an audio output device.

[0388] The "means for displaying received data" refers to a means for the device worn by the user to display relevant data received from the central processing unit.

[0389] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0390] Terminal side processing

[0391] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This video data is stored in temporary memory within the device. Once the device captures the video data, it transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0392] Server-side processing

[0393] The server receives the video data sent from the device. The received video data is input into a generative AI model (e.g., TensorFlow or PyTorch) to analyze the objects and text information. Based on the analysis results, the server accesses a database (information storage device) to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of signs and menus in foreign languages.

[0394] Providing information

[0395] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0396] Specific examples

[0397] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0398] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0399] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0400] Prompt Sentence Examples

[0401] The following prompt sentences can be used as input to a generative AI model:

[0402] 1. "I want the AR glasses to analyze the images being viewed by users in real time, and retrieve and display relevant information."

[0403] 2. "The names and background information of historical buildings installed at tourist sites will be used in real-time commentary."

[0404] 3. "Get and display reviews and ratings of products in stores in real time."

[0405] 4. "Translate foreign language signs and menus and display the translation results on the device display."

[0406] The system allows users to obtain detailed information in real time, making daily life, travel, shopping, and more convenient and efficient.

[0407] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0408] Step 1: Visual Capture

[0409] Device behavior

[0410] The camera in the device (AR glasses) worn by the user captures the user's field of view in real time. The camera image is the input, and the captured image data is generated as the output. This image data is temporarily stored in the device's memory.

[0411] Step 2: Sending video data

[0412] Device behavior

[0413] The device sends the captured video data to the server. The input is the captured video data, and the output is the data sent to the server. The data is encrypted and sent over a network connection.

[0414] Step 3: Receiving video data

[0415] Server Operation

[0416] The server receives video data sent from the terminal. The received video data is input, and the data stored in the buffer is generated as output. The received data is temporarily saved in the buffer.

[0417] Step 4: Data analysis

[0418] Server Operation

[0419] The server inputs the received video data into a generative AI model. The received video data is the input, and the analysis results are generated as the output. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to extract and recognize objects and text information.

[0420] Step 5: Obtain related information

[0421] Server Operation

[0422] The server retrieves related data from an information storage device (database) based on the analysis results. The analysis results are input, and the retrieved related information is generated as output. SQL queries are executed against the database to retrieve related details, reviews, and translation data.

[0423] Step 6: Sending acquired data

[0424] Server Operation

[0425] The server sends the acquired relevant information to the terminal. The acquired relevant information is the input, and data to be sent to the terminal is generated as the output. The data is packaged in a pre-specified format (for example, JSON format), encrypted, and sent.

[0426] Step 7: Display information

[0427] Device behavior

[0428] The device analyzes the information received from the server and displays it in the user's field of view. The received data is the input, and the information displayed on the display is generated as the output. Text information is overlaid on the display, and audio information is provided through the speaker.

[0429] Step 8: Verify the user

[0430] User behavior

[0431] The user checks the information displayed on the device's display and listens to audio information as needed. The displayed information is the input, and the user's understanding and judgment are the output. This allows the user to take action and make choices based on visual information in real time.

[0432] (Application example 1)

[0433] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0434] Traditional shopping experiences in brick-and-mortar stores require a lot of effort to obtain product information, making efficient shopping difficult. It also takes time to obtain detailed information, reviews, and sales information about specific products, and in some cases users may not be able to find the information they are looking for. Furthermore, there are also issues with long wait times when paying at the register, which degrades the user experience.

[0435] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0436] In this invention, the server includes: means for analyzing video data in real time using a generative AI model; means for capturing video data from a display device worn by a user; means for transmitting the video data to the server via a network; means for analyzing the transmitted video data and recognizing objects; means for retrieving related information from a database based on the analysis results; means for transmitting and displaying the retrieved information on the user's display device; means for retrieving product names, prices, ratings, and reviews based on the captured product information; and means for providing the retrieved product information to the user via a virtual cart function and making payments. This allows users to obtain all information about products in physical stores in real time and efficiently shop. Furthermore, payment through the virtual cart function reduces waiting times at the cash register and provides a more comfortable shopping experience.

[0437] A "generative AI model" is an artificial intelligence algorithm used to analyze a user's visual data and recognize objects.

[0438] A "display device" is a screen or display device worn by a user to visually view information.

[0439] A "network" is a communications infrastructure used to send and receive information.

[0440] A "server" is a computer system that has a central processing unit for storing, processing, and analyzing data.

[0441] A "database" is an information system that systematically stores related information and makes it available.

[0442] "Virtual Cart Function" refers to a software function that allows users to virtually add and pay for selected items.

[0443] This invention is a system that enhances the shopping experience in brick-and-mortar stores by linking a user-worn display device with a server. The system uses a generative AI model to analyze video data in real time and provide the user with the information they need.

[0444] Terminal side processing

[0445] The display device worn by the user (e.g., AR glasses) is equipped with a camera and captures the user's field of view in real time. The captured video data is sent to a server via a network. The display device also has a display function to provide visual information to the user and an audio output device.

[0446] Server-side processing

[0447] The server receives the video data sent from the device and inputs it into the generative AI model. The generative AI model analyzes the video data and recognizes the object (e.g., product). Based on the recognition result, the server retrieves related information (price, reviews, ratings, sale information, etc.) from the database. The retrieved information is then sent back to the device and displayed on the user's display device.

[0448] Hardware and software used

[0449] Hardware:

[0450] User device: AR glasses (camera, display, speaker)

[0451] Server: High-performance AI server

[0452] Network: High-speed internet connection

[0453] software:

[0454] Generative AI models: image recognition models, natural language processing models

[0455] Frameworks: TensorFlow, PyTorch

[0456] Data Management: SQL Database

[0457] Specific examples

[0458] Consider a user browsing a store shelf. The AR glasses worn by the user capture the product and send the image data to a server. The server uses an image recognition model to identify the product and retrieves related details (e.g., product name, price, reviews) from a database. This information is then sent back to the device and displayed on the AR glasses' display. The user can then continue shopping while checking the information.

[0459] Furthermore, the virtual cart function allows users to virtually add their favorite products to their cart and make smart payments, reducing waiting times at the cash register and enabling efficient shopping.

[0460] Prompt Sentence Examples

[0461] The following prompts could be fed to a generative AI model:

[0462] "Recognize products from camera footage and instantly retrieve and display detailed product information, including product name, price, user reviews, and sales information. It also suggests related products and allows real-time payment using a virtual cart function."

[0463] This system allows users to enjoy a more comfortable and efficient shopping experience in physical stores.

[0464] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0465] Step 1:

[0466] The camera on the device (the display device worn by the user) captures the user's field of view in real time. The input is the video data from the camera, and the output is real-time video data.

[0467] Step 2:

[0468] The video data captured by the terminal is sent to the server via the network. The input is the video data captured by the terminal, and the output is the video data sent to the server.

[0469] Step 3:

[0470] The video data received by the server is input into a generative AI model, which analyzes and recognizes objects in the video. The input is the video data received by the server, and the output is the recognition result of the object. This analysis uses an image recognition model to specifically identify the product.

[0471] Step 4:

[0472] Based on the object recognition results, the server retrieves related information (product name, price, reviews, ratings, sale information, etc.) from the database. The input is the object recognition results, and the output is the product information retrieved from the database.

[0473] Step 5:

[0474] The server transmits the acquired product information to the terminal via the network. The input is the information acquired from the database, and the output is the product information transmitted to the terminal.

[0475] Step 6:

[0476] The terminal displays the product information received from the server in the user's field of view. The input is the product information sent from the server, and the output is the product information that the user can see through the display device.

[0477] Step 7:

[0478] When a user checks product information and adds it to the virtual cart, the terminal sends the information to the server, which then processes the addition of the product to the virtual cart. The input is the user's selection, and the output is the product information added to the virtual cart.

[0479] Step 8:

[0480] The server aggregates the product information in the virtual cart and processes the payment. The input is the product information added to the virtual cart, and the output is the payment completion status. At this time, a payment gateway is used if necessary.

[0481] At each step, the following specific actions are performed:

[0482] Step 1: The camera captures the user's field of view in high resolution.

[0483] Step 2: Send the video data to the server using Wi-Fi or mobile data.

[0484] Step 3: The generative AI model analyzes the data and recognizes the product.

[0485] Step 4: Retrieve product information from the database using an SQL query.

[0486] Step 5: Transfer information securely using the HTTPS protocol.

[0487] Step 6: Overlay information on the display.

[0488] Step 7: Add items to your virtual cart with touch or voice commands.

[0489] Step 8: Process the payment in real time via the payment gateway.

[0490] This allows users to shop smoothly and efficiently in-store.

[0491] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0492] This invention provides a system that combines an emotion engine that recognizes the user's emotions. This system uses a device worn by the user, a server, and a generative AI model to analyze video data in real time and provide the user with the information they need immediately. Furthermore, the emotion engine makes it possible to provide information according to the user's emotional state.

[0493] Terminal side processing

[0494] The device worn by the user (AR glasses) has features such as a camera and built-in sensors that capture the user's field of view and emotional state in real time. The device captures video data and emotional data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0495] Server-side processing

[0496] The server receives the video and emotion data sent from the device. The received video data is input into a generative AI model, which analyzes the object and text information. The emotion engine also analyzes the received emotion data to recognize the user's emotional state. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews for stores, and translations of signs and menus in foreign languages.

[0497] Providing information

[0498] The server adaptively customizes the information obtained through the analysis and sends it to the device. For example, if the user is excited, it provides more detailed explanations, and if the user is tired, it provides simple information. The device displays the received information in the user's field of view. Text information is displayed on the screen, and audio information is provided through the speaker.

[0499] Specific examples

[0500] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, which analyzes the image data and recognizes the name of the building and its historical background. At the same time, the emotion engine analyzes the user's emotional state and determines that the user is excited. Based on the analysis results, the server retrieves detailed explanatory information from the database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0501] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's satisfaction. This data is sent to a server, which analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and adjusts the detail of the reviews to reflect the user's satisfaction. The device displays this information on its screen, allowing the user to check detailed reviews and ratings for the product.

[0502] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's confusion. This data is sent to the server, which analyzes the image and extracts the foreign text. At the same time, the emotion engine analyzes the user's confusion and generates a detailed translation result that reflects this. Based on the analysis results, the server translates the text and sends it to the device. The device then displays the translation result on the screen, allowing the user to view the translated text in real time.

[0503] With these functions, the system of the present invention not only allows users to obtain detailed information in real time, but also provides customized information tailored to the user's emotional state, making daily life, travel, shopping, and more convenient and efficient.

[0504] The processing flow will be explained below.

[0505] Step 1:

[0506] The device activates a camera to capture the user's field of view in real time, while simultaneously measuring the user's emotional state with an emotion sensor and capturing that data.

[0507] Step 2:

[0508] The user looks at an object (such as a tourist attraction or a product) they want to see through the AR glasses. At this time, the camera in the AR glasses captures the image of the object, and the built-in emotion sensor measures the user's emotion.

[0509] Step 3:

[0510] The device transmits the captured video data and emotion data to a server, where the data is transmitted over a network with low latency.

[0511] Step 4:

[0512] The server receives the video data and emotion data sent from the device and prepares the received data for analysis.

[0513] Step 5:

[0514] The server inputs the video data into a generative AI model that analyzes objects and text information, such as the names of tourist attractions and product details.

[0515] Step 6:

[0516] The server uses an emotion engine to analyze the received emotion data and recognize the user's emotional state, for example, determining whether the user is excited or confused.

[0517] Step 7:

[0518] Based on the analysis results, the server accesses a database to retrieve relevant information, such as detailed descriptions of tourist attractions, product reviews, and foreign language translations.

[0519] Step 8:

[0520] The server takes into account the user's emotional state and customizes the information it provides, for example providing detailed information if the user is excited and brief information if the user is tired.

[0521] Step 9:

[0522] The server sends the customized information to the device in the appropriate format (text, voice).

[0523] Step 10:

[0524] The terminal displays the received information to the user: text information on the display and audio information through the speaker.

[0525] Step 11:

[0526] The user reviews the displayed information and, if necessary, can request more detailed information or information about a different object, in which case the process begins again from step 1.

[0527] Through the above processing steps, users can not only obtain detailed information in real time, but also enjoy customized information tailored to their emotional state, making daily life, travel, shopping, etc. more convenient and efficient.

[0528] Example 2

[0529] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0530] Conventional information provision systems have difficulty in providing customized information based on the user's real-time emotional state. Furthermore, while they have advanced analytical capabilities such as recognizing specific objects and text information, they have problems in that they are unable to provide flexibility in the accuracy and delivery of information according to the user's emotions.

[0531] The identification process 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 means for capturing video data from a device worn by the user, means for detecting the user's emotional state using a built-in sensor, means for transmitting the captured video data and emotional data to the server, means for analyzing the transmitted video data using a generative AI model and recognizing objects and text information, means including an emotion engine for analyzing the emotional data and recognizing the user's emotional state, means for retrieving related information from a database based on the analysis result and the emotional state, and means for generating customized information and transmitting it to the user's device for display. This makes it possible to provide information customized according to the user's emotional state in real time.

[0532] A "device worn by the user" is a device such as AR glasses that can be worn by the user to take in images into their field of vision.

[0533] "Built-in sensors" are sensors built into devices to measure biometric information such as the user's heart rate and facial expressions.

[0534] "Video data" is digital data that captures video of objects or scenes that come into the user's field of view.

[0535] "Emotional data" refers to data that represents the user's emotional state as inferred from factors such as their heart rate and facial expression measured by the built-in sensor.

[0536] A "generative AI model" is an artificial intelligence model trained using large datasets, and is a technology for analyzing video data and recognizing objects and text information.

[0537] A "server" is a computer system that processes data received from a terminal over a network and returns the analysis results.

[0538] An "emotion engine" is an algorithm or software that analyzes emotional data to recognize a user's emotional state.

[0539] A "database" is a data storage system that stores related information and retrieves required information based on the results of analysis.

[0540] "Customized information" refers to detailed commentary, reviews, translations, and other information tailored to the user's emotional state.

[0541] This invention is a system that utilizes an emotion engine that recognizes the user's emotions and a generative AI model, and uses a device worn by the user and a server to analyze video data in real time and provide the user with the information they need instantly.

[0542] Hardware and Software Configuration

[0543] The system consists of the following major hardware and software components:

[0544] 1. Devices worn by users

[0545] The device worn by the user, such as AR glasses, is equipped with a camera and built-in sensors (such as a heart rate monitor and facial recognition sensor). This device captures images in the user's field of vision and simultaneously detects their emotional state. The device also includes a display and speaker, providing the acquired information visually and audibly to the user.

[0546] 2. Server

[0547] The server is a computer system that receives and processes data sent from the device worn by the user. The server is equipped with a generative AI model (e.g., implemented in TensorFlow or PyTorch) and an emotion engine (e.g., Emotion API or Affectiva).

[0548] Data processing and calculation

[0549] This system processes and calculates data through the following processes.

[0550] 1. Acquiring video data and emotion data

[0551] The user wears the device and captures the image of their field of vision. The built-in sensors detect the user's emotional state (e.g., excitement, satisfaction, confusion, etc.) in real time. This data is then sent from the device to a server.

[0552] 2. Analysis of video data and emotion data

[0553] On the server, the video data is first input into a generative AI model to recognize objects and text information, allowing it to identify, for example, specific tourist attractions or product names. At the same time, an emotion engine analyzes the emotion data and identifies the user's emotional state.

[0554] 3. Information Collection and Customization

[0555] Based on the analysis results of the generative AI model and emotion engine, the server retrieves relevant information from a database, such as the history and detailed descriptions of tourist attractions, product reviews and ratings, and translations of foreign language signs and menus. It then customizes the level of detail and format of the information depending on the user's emotional state.

[0556] 4. Provision of Information

[0557] The server then sends the customized information to the user's device, which then displays it visually or provides it as audio. For example, if the user is excited, detailed information is displayed, and if the user is tired, simple information is provided.

[0558] Specific use cases

[0559] As specific examples, we will explain visiting tourist spots, shopping, and translating signs and menus in foreign countries.

[0560] When visiting tourist spots

[0561] When a user visits a tourist attraction, they put on the AR glasses and look at the attraction. The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, where it is analyzed to identify the name and historical background. The emotion engine recognizes the user's excitement, and the server sends detailed explanatory information to the device. The user can view this information in real time on the display.

[0562] For shopping

[0563] When a user looks at a product in a store, the device captures the image as they look at it through the AR glasses, and the emotion sensor detects their level of satisfaction. The server analyzes this data to identify the product name and brand. Depending on their level of satisfaction, detailed reviews and ratings are provided.

[0564] Translation of foreign signs and menus

[0565] When a user sees a foreign sign or menu, the device captures the video and an emotion sensor detects confusion. The server analyzes the video and extracts the foreign language text. A detailed translation based on the user's emotional state is generated and sent to the user's device.

[0566] Prompt Sentence Examples

[0567] "Taking video data of a tourist spot and the user's emotional state as input, generate a detailed commentary and output customized information by providing additional information if the user is excited."

[0568] With the above configuration, the present invention can provide users with appropriate and customized information in real time, improving their experience in various situations.

[0569] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] The user puts on the AR glasses and captures the image that comes into their field of vision. Specifically, when the user directs their gaze at a specific tourist attraction or product, the device's camera captures the image as digital data. The input is the image that appears in their field of vision, and the output is video data. In addition, built-in sensors measure biometric information such as heart rate and facial expressions, and record this as emotional data.

[0572] Step 2:

[0573] The device transmits the captured video data and emotion data to the server. Specifically, the data is encrypted and transmitted over the network. The input is the video data and emotion data captured by the device, and the output is the encrypted data transferred to the server.

[0574] Step 3:

[0575] The server receives and decrypts the data sent from the terminal. Specifically, it decrypts the received data into an appropriate format and separates the video data and emotion data. The input at this time is encrypted video data and emotion data, and the output is decrypted video data and emotion data.

[0576] Step 4:

[0577] The server analyzes the decoded video data using a generative AI model to recognize objects and text information. For example, TensorFlow or PyTorch can be used to analyze the video data and identify tourist attractions or product names. The input is the decoded video data, and the output is the object information and text information resulting from the analysis.

[0578] Step 5:

[0579] The server analyzes the decoded emotional data with an emotion engine to recognize the user's emotional state. Specifically, it processes the emotional data using Emotion API and Affectiva to determine whether the user is excited, tired, or confused. The input is the decoded emotional data, and the output is the user's emotional state.

[0580] Step 6:

[0581] The server retrieves related information from a database based on the analysis results. For example, it searches the database for detailed descriptions of tourist attractions, product reviews, translations of foreign texts, etc. The input is the object information and the user's emotional state, and the output is related information.

[0582] Step 7:

[0583] The server customizes the acquired information according to the user's emotional state. Specifically, it generates detailed information when the user is excited and concise information when the user is tired. The input is the acquired related information and the user's emotional state, and the output is the customized information.

[0584] Step 8:

[0585] The server encrypts and transmits the customized information to the user's device. The device decrypts the received information and displays it on a display or provides it as audio information from a speaker. The input in this case is the customized information, and the output is information provided to the user visually or audibly.

[0586] (Application example 2)

[0587] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0588] Conventional systems acquire and analyze video data through a wearable device worn by the user, but because they do not provide information that takes the user's emotional state into consideration, the information provided does not necessarily match the user's needs or situation.The present invention aims to provide a system that combines an emotion engine and provides adaptive information according to the user's emotional state, thereby improving the user experience.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0590] In this invention, the server includes means for analyzing video data in real time using a generative AI model, means for capturing video data from a wearable device worn by a user, means for transmitting video data and emotion data to the server, means for analyzing the transmitted video data and recognizing objects, means for analyzing the user's emotion data using an emotion engine, means for retrieving related information from a database based on the analysis results, and means for transmitting the retrieved information to the user's wearable device and displaying it. This enables the provision of adaptive and detailed information according to the user's emotional state.

[0591] A "generative AI model" is a machine learning model that uses artificial intelligence to extract patterns and features from data and perform specific tasks.

[0592] "Real-time" refers to a processing method in which data is acquired and analyzed instantaneously, providing results immediately.

[0593] "Video Data" means visual information captured by a camera or other video capture device.

[0594] A "wearable device worn by a user" is an electronic device that can be worn by a user and has functions such as a camera, sensor, and display.

[0595] "Emotion data" is data that expresses the user's emotional state as numerical values ​​or categories.

[0596] "Transmitting means" refers to the functionality or technology for transferring data from one device to another.

[0597] A "server" is a computer system that processes, stores, and distributes data over a network.

[0598] "Target" refers to an object, place, person, etc. identified within video data.

[0599] An "emotion engine" is software or hardware that has the function of analyzing and recognizing the user's emotional state and outputting the results as data.

[0600] A "database" is a system for storing and managing data efficiently and systematically.

[0601] "Means for transmitting and displaying on a wearable device" refers to a function or technology for sending data to a wearable device and presenting information to the user using its display or audio output device.

[0602] The present invention relates to a system consisting of a wearable device (e.g., smart glasses) worn by a user and a server. This system can analyze video data in real time and provide information according to the user's emotional state.

[0603] The user wears a wearable device (smart glasses). The wearable device is equipped with a camera, an emotion sensor, a display, and an audio output device. The device captures the user's field of view and simultaneously monitors the user's emotional state in real time. The captured video data and emotion data are sent to a server.

[0604] When the server receives the video data, it uses the generative AI model to analyze the video data and recognize the object. For example, if a user looks at a product on a shelf, it recognizes the product and obtains information about it. It also uses an emotion engine to analyze the user's emotional data and understand their current emotional state. This allows the server to select appropriate information according to their emotional state.

[0605] Based on the analysis results, the server retrieves relevant information from the database. For example, if the user is excited, it displays detailed product reviews and ratings. If the user is confused, it provides a brief explanation and price comparison information. This information is sent to the wearable device and provided to the user through a display or audio output device.

[0606] As a concrete example, consider a user in a brick-and-mortar bookstore. The user puts on smart glasses and picks up a book. At this time, the camera in the smart glasses captures the book, and the emotion sensor detects the user's state of excitement. This data is sent to the server, which retrieves the book's title and detailed review and adaptively displays them to the user. The generation AI prompt sentence used is, "Please generate a book title and review. The user is excited, so please be detailed."

[0607] Furthermore, consider the case where a user is in an electronics store. The user puts on smart glasses and looks at a home appliance. The camera captures the product, and the emotion sensor detects a confused state. This allows the server to provide a brief description of the product's features and price comparison information. The generated AI prompt sentence is, "The user is confused. Please provide a brief description of the product's features and price comparison information."

[0608] In this way, the system of the present invention can provide appropriate information in real time according to the user's emotional state, thereby significantly improving the user experience.

[0609] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0610] Step 1:

[0611] A user wears a wearable device (smart glasses) and visually recognizes products in a store. At this time, a camera captures the product image, and an emotion sensor acquires the user's emotion data in real time.

[0612] Input: Product video, emotion data

[0613] Output: Captured video and emotion data

[0614] Step 2:

[0615] The device transmits the captured video data and emotion data to a server using a communication module that bundles the video data and emotion data and transmits them to the server via a network.

[0616] Input: Captured video and emotion data

[0617] Output: Video and emotion data sent to the server

[0618] Step 3:

[0619] The server inputs the received video data into the generative AI model and performs video analysis. This is the process by which the generative AI model recognizes objects (e.g., products) from the video data.

[0620] Input: Video data sent to the server

[0621] Output: Data of recognized objects (products)

[0622] Step 4:

[0623] The server simultaneously inputs the received emotional data into the emotion engine, which analyzes the user's emotional state and quantifies it as happiness, excitement, confusion, or other emotional states.

[0624] Input: Emotion data sent to the server

[0625] Output: Parsed user's emotional state

[0626] Step 5:

[0627] The server retrieves relevant information from the database based on the object recognition results of the generative AI model and the emotional state analysis results of the emotion engine, such as product reviews and ratings, detailed product descriptions, and price information.

[0628] Input: Recognized object data, analyzed user emotional state

[0629] Output: Relevant information obtained

[0630] Step 6:

[0631] The server adaptively customizes the information it retrieves, tailoring it to the user's emotional state: for example, providing detailed reviews if the user is excited, or concise explanations if the user is confused.

[0632] Input: retrieved relevant information, analyzed user emotional state

[0633] Output: Customized information

[0634] Step 7:

[0635] The server sends the customized information to the terminal, which is then formatted as text data and sent to the wearable device via a network.

[0636] Input:Customized Information

[0637] Output: Information sent to the wearable device

[0638] Step 8:

[0639] The terminal (wearable device) displays the received information in the user's field of vision, showing detailed product reviews, ratings, and pricing information on the display, and providing audio information via an audio output device if necessary.

[0640] Input: Information sent to the wearable device

[0641] Output: Information displayed to the user's eyes or output as audio

[0642] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0643] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0644] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0645] [Third embodiment]

[0646] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0647] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0648] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0649] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0650] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0651] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0652] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0653] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0654] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0655] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0656] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0657] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0658] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0659] Terminal side processing

[0660] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This device captures video data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0661] Server-side processing

[0662] The server receives the video data sent from the device. The received video data is input into a generative AI model, which analyzes the objects and text information. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of foreign language signs and menus.

[0663] Providing information

[0664] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0665] Specific examples

[0666] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0667] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0668] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0669] With these functions, the system of the present invention allows users to obtain detailed information in real time, making daily life, travel, shopping, etc. more convenient and efficient.

[0670] The processing flow will be explained below.

[0671] Step 1:

[0672] The device captures the user's view in real time, with the device's camera constantly capturing the latest video data.

[0673] Step 2:

[0674] The device sends the captured video data to the server using the device's network functions, ensuring low latency.

[0675] Step 3:

[0676] The server receives the video data sent from the device, temporarily stores it, and prepares it for analysis.

[0677] Step 4:

[0678] The server inputs the received video data into the generative AI model and begins analysis, where objects and text information are extracted.

[0679] Step 5:

[0680] The server accesses a database based on the analysis results to retrieve relevant information, such as tourist attraction information, product reviews, and foreign language translation results.

[0681] Step 6:

[0682] The server then sends the acquired information to the device, again using the device's network capabilities to quickly transmit the data.

[0683] Step 7:

[0684] The terminal displays the received information to the user: text information is displayed on a display, and audio information is played through a speaker.

[0685] Step 8:

[0686] The user can review the information provided and, if desired, take further action to obtain additional information, such as capturing a new object with the camera.

[0687] This allows users to easily obtain the information they need in real time, making their daily lives and travels more convenient and fulfilling.

[0688] Example 1

[0689] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0690] In modern society, it is extremely important for users to quickly and accurately obtain the information they need in real time in their daily lives, travel, shopping, etc. However, conventional technology does not adequately provide a system that can instantly analyze the information that users visually obtain and provide the results immediately. This poses the problem of users having to spend time and effort searching for the information they are looking for.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0692] In this invention, the server includes means for acquiring visual information from a device worn by a user, means for transmitting the acquired visual information to a central processing unit, means for receiving the visual information in the central processing unit, means for analyzing the received visual information using a generative AI model, means for acquiring related data from an information storage device based on the analysis results, means for transmitting the acquired data to the user's device, and means for displaying the received data on the user's device, thereby enabling the user to acquire rapid and accurate information in real time based on their own visual information.

[0693] A "user-worn device" is a device worn by a user on the body, and includes a camera for acquiring visual information and a display for displaying information.

[0694] "Visual information" refers to video data captured by a camera, such as objects and text within the user's field of view.

[0695] A "central processing unit" is a device that receives visual information sent from a terminal and analyzes it using a generative AI model.

[0696] A "generative AI model" is a model that uses machine learning and deep learning techniques to analyze input data and recognize objects and text information.

[0697] An "information storage device" is a storage medium, such as a database or file system, accessed by a central processing unit, that holds related data.

[0698] "Related data" refers to information such as detailed descriptions, evaluations, and translations of the object, which is obtained based on the analyzed visual information.

[0699] The "display means" refers to a means for presenting information to a user, including a display built into a device worn by the user and an audio output device.

[0700] The "means for displaying received data" refers to a means for the device worn by the user to display relevant data received from the central processing unit.

[0701] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0702] Terminal side processing

[0703] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This video data is stored in temporary memory within the device. Once the device captures the video data, it transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0704] Server-side processing

[0705] The server receives the video data sent from the device. The received video data is input into a generative AI model (e.g., TensorFlow or PyTorch) to analyze the objects and text information. Based on the analysis results, the server accesses a database (information storage device) to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of signs and menus in foreign languages.

[0706] Providing information

[0707] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0708] Specific examples

[0709] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0710] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0711] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0712] Prompt Sentence Examples

[0713] The following prompt sentences can be used as input to a generative AI model:

[0714] 1. "I want the AR glasses to analyze the images being viewed by users in real time, and retrieve and display relevant information."

[0715] 2. "The names and background information of historical buildings installed at tourist sites will be used in real-time commentary."

[0716] 3. "Get and display reviews and ratings of products in stores in real time."

[0717] 4. "Translate foreign language signs and menus and display the translation results on the device display."

[0718] The system allows users to obtain detailed information in real time, making daily life, travel, shopping, and more convenient and efficient.

[0719] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0720] Step 1: Visual Capture

[0721] Device behavior

[0722] The camera in the device (AR glasses) worn by the user captures the user's field of view in real time. The camera image is the input, and the captured image data is generated as the output. This image data is temporarily stored in the device's memory.

[0723] Step 2: Sending video data

[0724] Device behavior

[0725] The device sends the captured video data to the server. The input is the captured video data, and the output is the data sent to the server. The data is encrypted and sent over a network connection.

[0726] Step 3: Receiving video data

[0727] Server Operation

[0728] The server receives video data sent from the terminal. The received video data is input, and the data stored in the buffer is generated as output. The received data is temporarily saved in the buffer.

[0729] Step 4: Data analysis

[0730] Server Operation

[0731] The server inputs the received video data into a generative AI model. The received video data is the input, and the analysis results are generated as the output. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to extract and recognize objects and text information.

[0732] Step 5: Obtain related information

[0733] Server Operation

[0734] The server retrieves related data from an information storage device (database) based on the analysis results. The analysis results are input, and the retrieved related information is generated as output. SQL queries are executed against the database to retrieve related details, reviews, and translation data.

[0735] Step 6: Sending acquired data

[0736] Server Operation

[0737] The server sends the acquired relevant information to the terminal. The acquired relevant information is the input, and data to be sent to the terminal is generated as the output. The data is packaged in a pre-specified format (for example, JSON format), encrypted, and sent.

[0738] Step 7: Display information

[0739] Device behavior

[0740] The device analyzes the information received from the server and displays it in the user's field of view. The received data is the input, and the information displayed on the display is generated as the output. Text information is overlaid on the display, and audio information is provided through the speaker.

[0741] Step 8: Verify the user

[0742] User behavior

[0743] The user checks the information displayed on the device's display and listens to audio information as needed. The displayed information is the input, and the user's understanding and judgment are the output. This allows the user to take action and make choices based on visual information in real time.

[0744] (Application example 1)

[0745] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0746] Traditional shopping experiences in brick-and-mortar stores require a lot of effort to obtain product information, making efficient shopping difficult. It also takes time to obtain detailed information, reviews, and sales information about specific products, and in some cases users may not be able to find the information they are looking for. Furthermore, there are also issues with long wait times when paying at the register, which degrades the user experience.

[0747] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0748] In this invention, the server includes: means for analyzing video data in real time using a generative AI model; means for capturing video data from a display device worn by a user; means for transmitting the video data to the server via a network; means for analyzing the transmitted video data and recognizing objects; means for retrieving related information from a database based on the analysis results; means for transmitting and displaying the retrieved information on the user's display device; means for retrieving product names, prices, ratings, and reviews based on the captured product information; and means for providing the retrieved product information to the user via a virtual cart function and making payments. This allows users to obtain all information about products in physical stores in real time and efficiently shop. Furthermore, payment through the virtual cart function reduces waiting times at the cash register and provides a more comfortable shopping experience.

[0749] A "generative AI model" is an artificial intelligence algorithm used to analyze a user's visual data and recognize objects.

[0750] A "display device" is a screen or display device worn by a user to visually view information.

[0751] A "network" is a communications infrastructure used to send and receive information.

[0752] A "server" is a computer system that has a central processing unit for storing, processing, and analyzing data.

[0753] A "database" is an information system that systematically stores related information and makes it available.

[0754] "Virtual Cart Function" refers to a software function that allows users to virtually add and pay for selected items.

[0755] This invention is a system that enhances the shopping experience in brick-and-mortar stores by linking a user-worn display device with a server. The system uses a generative AI model to analyze video data in real time and provide the user with the information they need.

[0756] Terminal side processing

[0757] The display device worn by the user (e.g., AR glasses) is equipped with a camera and captures the user's field of view in real time. The captured video data is sent to a server via a network. The display device also has a display function to provide visual information to the user and an audio output device.

[0758] Server-side processing

[0759] The server receives the video data sent from the device and inputs it into the generative AI model. The generative AI model analyzes the video data and recognizes the object (e.g., product). Based on the recognition result, the server retrieves related information (price, reviews, ratings, sale information, etc.) from the database. The retrieved information is then sent back to the device and displayed on the user's display device.

[0760] Hardware and software used

[0761] Hardware:

[0762] User device: AR glasses (camera, display, speaker)

[0763] Server: High-performance AI server

[0764] Network: High-speed internet connection

[0765] software:

[0766] Generative AI models: image recognition models, natural language processing models

[0767] Frameworks: TensorFlow, PyTorch

[0768] Data Management: SQL Database

[0769] Specific examples

[0770] Consider a user browsing a store shelf. The AR glasses worn by the user capture the product and send the image data to a server. The server uses an image recognition model to identify the product and retrieves related details (e.g., product name, price, reviews) from a database. This information is then sent back to the device and displayed on the AR glasses' display. The user can then continue shopping while checking the information.

[0771] Furthermore, the virtual cart function allows users to virtually add their favorite products to their cart and make smart payments, reducing waiting times at the cash register and enabling efficient shopping.

[0772] Prompt Sentence Examples

[0773] The following prompts could be fed to a generative AI model:

[0774] "Recognize products from camera footage and instantly retrieve and display detailed product information, including product name, price, user reviews, and sales information. It also suggests related products and allows real-time payment using a virtual cart function."

[0775] This system allows users to enjoy a more comfortable and efficient shopping experience in physical stores.

[0776] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0777] Step 1:

[0778] The camera on the device (the display device worn by the user) captures the user's field of view in real time. The input is the video data from the camera, and the output is real-time video data.

[0779] Step 2:

[0780] The video data captured by the terminal is sent to the server via the network. The input is the video data captured by the terminal, and the output is the video data sent to the server.

[0781] Step 3:

[0782] The video data received by the server is input into a generative AI model, which analyzes and recognizes objects in the video. The input is the video data received by the server, and the output is the recognition result of the object. This analysis uses an image recognition model to specifically identify the product.

[0783] Step 4:

[0784] Based on the object recognition results, the server retrieves related information (product name, price, reviews, ratings, sale information, etc.) from the database. The input is the object recognition results, and the output is the product information retrieved from the database.

[0785] Step 5:

[0786] The server transmits the acquired product information to the terminal via the network. The input is the information acquired from the database, and the output is the product information transmitted to the terminal.

[0787] Step 6:

[0788] The terminal displays the product information received from the server in the user's field of view. The input is the product information sent from the server, and the output is the product information that the user can see through the display device.

[0789] Step 7:

[0790] When a user checks product information and adds it to the virtual cart, the terminal sends the information to the server, which then processes the addition of the product to the virtual cart. The input is the user's selection, and the output is the product information added to the virtual cart.

[0791] Step 8:

[0792] The server aggregates the product information in the virtual cart and processes the payment. The input is the product information added to the virtual cart, and the output is the payment completion status. At this time, a payment gateway is used if necessary.

[0793] At each step, the following specific actions are performed:

[0794] Step 1: The camera captures the user's field of view in high resolution.

[0795] Step 2: Send the video data to the server using Wi-Fi or mobile data.

[0796] Step 3: The generative AI model analyzes the data and recognizes the product.

[0797] Step 4: Retrieve product information from the database using an SQL query.

[0798] Step 5: Transfer information securely using the HTTPS protocol.

[0799] Step 6: Overlay information on the display.

[0800] Step 7: Add items to your virtual cart with touch or voice commands.

[0801] Step 8: Process the payment in real time via the payment gateway.

[0802] This allows users to shop smoothly and efficiently in-store.

[0803] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0804] This invention provides a system that combines an emotion engine that recognizes the user's emotions. This system uses a device worn by the user, a server, and a generative AI model to analyze video data in real time and provide the user with the information they need immediately. Furthermore, the emotion engine makes it possible to provide information according to the user's emotional state.

[0805] Terminal side processing

[0806] The device worn by the user (AR glasses) has features such as a camera and built-in sensors that capture the user's field of view and emotional state in real time. The device captures video data and emotional data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0807] Server-side processing

[0808] The server receives the video and emotion data sent from the device. The received video data is input into a generative AI model, which analyzes the object and text information. The emotion engine also analyzes the received emotion data to recognize the user's emotional state. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews for stores, and translations of signs and menus in foreign languages.

[0809] Providing information

[0810] The server adaptively customizes the information obtained through the analysis and sends it to the device. For example, if the user is excited, it provides more detailed explanations, and if the user is tired, it provides simple information. The device displays the received information in the user's field of view. Text information is displayed on the screen, and audio information is provided through the speaker.

[0811] Specific examples

[0812] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, which analyzes the image data and recognizes the name of the building and its historical background. At the same time, the emotion engine analyzes the user's emotional state and determines that the user is excited. Based on the analysis results, the server retrieves detailed explanatory information from the database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0813] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's satisfaction. This data is sent to a server, which analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and adjusts the detail of the reviews to reflect the user's satisfaction. The device displays this information on its screen, allowing the user to check detailed reviews and ratings for the product.

[0814] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's confusion. This data is sent to the server, which analyzes the image and extracts the foreign text. At the same time, the emotion engine analyzes the user's confusion and generates a detailed translation result that reflects this. Based on the analysis results, the server translates the text and sends it to the device. The device then displays the translation result on the screen, allowing the user to view the translated text in real time.

[0815] With these functions, the system of the present invention not only allows users to obtain detailed information in real time, but also provides customized information tailored to the user's emotional state, making daily life, travel, shopping, and more convenient and efficient.

[0816] The processing flow will be explained below.

[0817] Step 1:

[0818] The device activates a camera to capture the user's field of view in real time, while simultaneously measuring the user's emotional state with an emotion sensor and capturing that data.

[0819] Step 2:

[0820] The user looks at an object (such as a tourist attraction or a product) they want to see through the AR glasses. At this time, the camera in the AR glasses captures the image of the object, and the built-in emotion sensor measures the user's emotion.

[0821] Step 3:

[0822] The device transmits the captured video data and emotion data to a server, where the data is transmitted over a network with low latency.

[0823] Step 4:

[0824] The server receives the video data and emotion data sent from the device and prepares the received data for analysis.

[0825] Step 5:

[0826] The server inputs the video data into a generative AI model that analyzes objects and text information, such as the names of tourist attractions and product details.

[0827] Step 6:

[0828] The server uses an emotion engine to analyze the received emotion data and recognize the user's emotional state, for example, determining whether the user is excited or confused.

[0829] Step 7:

[0830] Based on the analysis results, the server accesses a database to retrieve relevant information, such as detailed descriptions of tourist attractions, product reviews, and foreign language translations.

[0831] Step 8:

[0832] The server takes into account the user's emotional state and customizes the information it provides, for example providing detailed information if the user is excited and brief information if the user is tired.

[0833] Step 9:

[0834] The server sends the customized information to the device in the appropriate format (text, voice).

[0835] Step 10:

[0836] The terminal displays the received information to the user: text information on the display and audio information through the speaker.

[0837] Step 11:

[0838] The user reviews the displayed information and, if necessary, can request more detailed information or information about a different object, in which case the process begins again from step 1.

[0839] Through the above processing steps, users can not only obtain detailed information in real time, but also enjoy customized information tailored to their emotional state, making daily life, travel, shopping, etc. more convenient and efficient.

[0840] Example 2

[0841] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0842] Conventional information provision systems have difficulty in providing customized information based on the user's real-time emotional state. Furthermore, while they have advanced analytical capabilities such as recognizing specific objects and text information, they have problems in that they are unable to provide flexibility in the accuracy and delivery of information according to the user's emotions.

[0843] The identification process 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 means for capturing video data from a device worn by the user, means for detecting the user's emotional state using a built-in sensor, means for transmitting the captured video data and emotional data to the server, means for analyzing the transmitted video data using a generative AI model and recognizing objects and text information, means including an emotion engine for analyzing the emotional data and recognizing the user's emotional state, means for retrieving related information from a database based on the analysis result and the emotional state, and means for generating customized information and transmitting it to the user's device for display. This makes it possible to provide information customized according to the user's emotional state in real time.

[0844] A "device worn by the user" is a device such as AR glasses that can be worn by the user to take in images into their field of vision.

[0845] "Built-in sensors" are sensors built into devices to measure biometric information such as the user's heart rate and facial expressions.

[0846] "Video data" is digital data that captures video of objects or scenes that come into the user's field of view.

[0847] "Emotional data" refers to data that represents the user's emotional state as inferred from factors such as their heart rate and facial expression measured by the built-in sensor.

[0848] A "generative AI model" is an artificial intelligence model trained using large datasets, and is a technology for analyzing video data and recognizing objects and text information.

[0849] A "server" is a computer system that processes data received from a terminal over a network and returns the analysis results.

[0850] An "emotion engine" is an algorithm or software that analyzes emotional data to recognize a user's emotional state.

[0851] A "database" is a data storage system that stores related information and retrieves required information based on the results of analysis.

[0852] "Customized information" refers to detailed commentary, reviews, translations, and other information tailored to the user's emotional state.

[0853] This invention is a system that utilizes an emotion engine that recognizes the user's emotions and a generative AI model, and uses a device worn by the user and a server to analyze video data in real time and provide the user with the information they need instantly.

[0854] Hardware and Software Configuration

[0855] The system consists of the following major hardware and software components:

[0856] 1. Devices worn by users

[0857] The device worn by the user, such as AR glasses, is equipped with a camera and built-in sensors (such as a heart rate monitor and facial recognition sensor). This device captures images in the user's field of vision and simultaneously detects their emotional state. The device also includes a display and speaker, providing the acquired information visually and audibly to the user.

[0858] 2. Server

[0859] The server is a computer system that receives and processes data sent from the device worn by the user. The server is equipped with a generative AI model (e.g., implemented in TensorFlow or PyTorch) and an emotion engine (e.g., Emotion API or Affectiva).

[0860] Data processing and calculation

[0861] This system processes and calculates data through the following processes.

[0862] 1. Acquiring video data and emotion data

[0863] The user wears the device and captures the image of their field of vision. The built-in sensors detect the user's emotional state (e.g., excitement, satisfaction, confusion, etc.) in real time. This data is then sent from the device to a server.

[0864] 2. Analysis of video data and emotion data

[0865] On the server, the video data is first input into a generative AI model to recognize objects and text information, allowing it to identify, for example, specific tourist attractions or product names. At the same time, an emotion engine analyzes the emotion data and identifies the user's emotional state.

[0866] 3. Information Collection and Customization

[0867] Based on the analysis results of the generative AI model and emotion engine, the server retrieves relevant information from a database, such as the history and detailed descriptions of tourist attractions, product reviews and ratings, and translations of foreign language signs and menus. It then customizes the level of detail and format of the information depending on the user's emotional state.

[0868] 4. Provision of Information

[0869] The server then sends the customized information to the user's device, which then displays it visually or provides it as audio. For example, if the user is excited, detailed information is displayed, and if the user is tired, simple information is provided.

[0870] Specific use cases

[0871] As specific examples, we will explain visiting tourist spots, shopping, and translating signs and menus in foreign countries.

[0872] When visiting tourist spots

[0873] When a user visits a tourist attraction, they put on the AR glasses and look at the attraction. The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, where it is analyzed to identify the name and historical background. The emotion engine recognizes the user's excitement, and the server sends detailed explanatory information to the device. The user can view this information in real time on the display.

[0874] For shopping

[0875] When a user looks at a product in a store, the device captures the image as they look at it through the AR glasses, and the emotion sensor detects their level of satisfaction. The server analyzes this data to identify the product name and brand. Depending on their level of satisfaction, detailed reviews and ratings are provided.

[0876] Translation of foreign signs and menus

[0877] When a user sees a foreign sign or menu, the device captures the video and an emotion sensor detects confusion. The server analyzes the video and extracts the foreign language text. A detailed translation based on the user's emotional state is generated and sent to the user's device.

[0878] Prompt Sentence Examples

[0879] "Taking video data of a tourist spot and the user's emotional state as input, generate a detailed commentary and output customized information by providing additional information if the user is excited."

[0880] With the above configuration, the present invention can provide users with appropriate and customized information in real time, improving their experience in various situations.

[0881] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0882] Step 1:

[0883] The user puts on the AR glasses and captures the image that comes into their field of vision. Specifically, when the user directs their gaze at a specific tourist attraction or product, the device's camera captures the image as digital data. The input is the image that appears in their field of vision, and the output is video data. In addition, built-in sensors measure biometric information such as heart rate and facial expressions, and record this as emotional data.

[0884] Step 2:

[0885] The device transmits the captured video data and emotion data to the server. Specifically, the data is encrypted and transmitted over the network. The input is the video data and emotion data captured by the device, and the output is the encrypted data transferred to the server.

[0886] Step 3:

[0887] The server receives and decrypts the data sent from the terminal. Specifically, it decrypts the received data into an appropriate format and separates the video data and emotion data. The input at this time is encrypted video data and emotion data, and the output is decrypted video data and emotion data.

[0888] Step 4:

[0889] The server analyzes the decoded video data using a generative AI model to recognize objects and text information. For example, TensorFlow or PyTorch can be used to analyze the video data and identify tourist attractions or product names. The input is the decoded video data, and the output is the object information and text information resulting from the analysis.

[0890] Step 5:

[0891] The server analyzes the decoded emotional data with an emotion engine to recognize the user's emotional state. Specifically, it processes the emotional data using Emotion API and Affectiva to determine whether the user is excited, tired, or confused. The input is the decoded emotional data, and the output is the user's emotional state.

[0892] Step 6:

[0893] The server retrieves related information from a database based on the analysis results. For example, it searches the database for detailed descriptions of tourist attractions, product reviews, translations of foreign texts, etc. The input is the object information and the user's emotional state, and the output is related information.

[0894] Step 7:

[0895] The server customizes the acquired information according to the user's emotional state. Specifically, it generates detailed information when the user is excited and concise information when the user is tired. The input is the acquired related information and the user's emotional state, and the output is the customized information.

[0896] Step 8:

[0897] The server encrypts and transmits the customized information to the user's device. The device decrypts the received information and displays it on a display or provides it as audio information from a speaker. The input in this case is the customized information, and the output is information provided to the user visually or audibly.

[0898] (Application example 2)

[0899] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0900] Conventional systems acquire and analyze video data through a wearable device worn by the user, but because they do not provide information that takes the user's emotional state into consideration, the information provided does not necessarily match the user's needs or situation.The present invention aims to provide a system that combines an emotion engine and provides adaptive information according to the user's emotional state, thereby improving the user experience.

[0901] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0902] In this invention, the server includes means for analyzing video data in real time using a generative AI model, means for capturing video data from a wearable device worn by a user, means for transmitting video data and emotion data to the server, means for analyzing the transmitted video data and recognizing objects, means for analyzing the user's emotion data using an emotion engine, means for retrieving related information from a database based on the analysis results, and means for transmitting the retrieved information to the user's wearable device and displaying it. This enables the provision of adaptive and detailed information according to the user's emotional state.

[0903] A "generative AI model" is a machine learning model that uses artificial intelligence to extract patterns and features from data and perform specific tasks.

[0904] "Real-time" refers to a processing method in which data is acquired and analyzed instantaneously, providing results immediately.

[0905] "Video Data" means visual information captured by a camera or other video capture device.

[0906] A "wearable device worn by a user" is an electronic device that can be worn by a user and has functions such as a camera, sensor, and display.

[0907] "Emotion data" is data that expresses the user's emotional state as numerical values ​​or categories.

[0908] "Transmitting means" refers to the functionality or technology for transferring data from one device to another.

[0909] A "server" is a computer system that processes, stores, and distributes data over a network.

[0910] "Target" refers to an object, place, person, etc. identified within video data.

[0911] An "emotion engine" is software or hardware that has the function of analyzing and recognizing the user's emotional state and outputting the results as data.

[0912] A "database" is a system for storing and managing data efficiently and systematically.

[0913] "Means for transmitting and displaying on a wearable device" refers to a function or technology for sending data to a wearable device and presenting information to the user using its display or audio output device.

[0914] The present invention relates to a system consisting of a wearable device (e.g., smart glasses) worn by a user and a server. This system can analyze video data in real time and provide information according to the user's emotional state.

[0915] The user wears a wearable device (smart glasses). The wearable device is equipped with a camera, an emotion sensor, a display, and an audio output device. The device captures the user's field of view and simultaneously monitors the user's emotional state in real time. The captured video data and emotion data are sent to a server.

[0916] When the server receives the video data, it uses the generative AI model to analyze the video data and recognize the object. For example, if a user looks at a product on a shelf, it recognizes the product and obtains information about it. It also uses an emotion engine to analyze the user's emotional data and understand their current emotional state. This allows the server to select appropriate information according to their emotional state.

[0917] Based on the analysis results, the server retrieves relevant information from the database. For example, if the user is excited, it displays detailed product reviews and ratings. If the user is confused, it provides a brief explanation and price comparison information. This information is sent to the wearable device and provided to the user through a display or audio output device.

[0918] As a concrete example, consider a user in a brick-and-mortar bookstore. The user puts on smart glasses and picks up a book. At this time, the camera in the smart glasses captures the book, and the emotion sensor detects the user's state of excitement. This data is sent to the server, which retrieves the book's title and detailed review and adaptively displays them to the user. The generation AI prompt sentence used is, "Please generate a book title and review. The user is excited, so please be detailed."

[0919] Furthermore, consider the case where a user is in an electronics store. The user puts on smart glasses and looks at a home appliance. The camera captures the product, and the emotion sensor detects a confused state. This allows the server to provide a brief description of the product's features and price comparison information. The generated AI prompt sentence is, "The user is confused. Please provide a brief description of the product's features and price comparison information."

[0920] In this way, the system of the present invention can provide appropriate information in real time according to the user's emotional state, thereby significantly improving the user experience.

[0921] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0922] Step 1:

[0923] A user wears a wearable device (smart glasses) and visually recognizes products in a store. At this time, a camera captures the product image, and an emotion sensor acquires the user's emotion data in real time.

[0924] Input: Product video, emotion data

[0925] Output: Captured video and emotion data

[0926] Step 2:

[0927] The device transmits the captured video data and emotion data to a server using a communication module that bundles the video data and emotion data and transmits them to the server via a network.

[0928] Input: Captured video and emotion data

[0929] Output: Video and emotion data sent to the server

[0930] Step 3:

[0931] The server inputs the received video data into the generative AI model and performs video analysis. This is the process by which the generative AI model recognizes objects (e.g., products) from the video data.

[0932] Input: Video data sent to the server

[0933] Output: Data of recognized objects (products)

[0934] Step 4:

[0935] The server simultaneously inputs the received emotional data into the emotion engine, which analyzes the user's emotional state and quantifies it as happiness, excitement, confusion, or other emotional states.

[0936] Input: Emotion data sent to the server

[0937] Output: Parsed user's emotional state

[0938] Step 5:

[0939] The server retrieves relevant information from the database based on the object recognition results of the generative AI model and the emotional state analysis results of the emotion engine, such as product reviews and ratings, detailed product descriptions, and price information.

[0940] Input: Recognized object data, analyzed user emotional state

[0941] Output: Relevant information obtained

[0942] Step 6:

[0943] The server adaptively customizes the information it retrieves, tailoring it to the user's emotional state: for example, providing detailed reviews if the user is excited, or concise explanations if the user is confused.

[0944] Input: retrieved relevant information, analyzed user emotional state

[0945] Output: Customized information

[0946] Step 7:

[0947] The server sends the customized information to the terminal, which is then formatted as text data and sent to the wearable device via a network.

[0948] Input:Customized Information

[0949] Output: Information sent to the wearable device

[0950] Step 8:

[0951] The terminal (wearable device) displays the received information in the user's field of vision, showing detailed product reviews, ratings, and pricing information on the display, and providing audio information via an audio output device if necessary.

[0952] Input: Information sent to the wearable device

[0953] Output: Information displayed to the user's eyes or output as audio

[0954] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0955] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0956] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0957] [Fourth embodiment]

[0958] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0959] 7, a 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.

[0960] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0961] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0962] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0963] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0964] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0965] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0966] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0967] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0968] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0969] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0970] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0971] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[0972] Terminal side processing

[0973] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This device captures video data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[0974] Server-side processing

[0975] The server receives the video data sent from the device. The received video data is input into a generative AI model, which analyzes the objects and text information. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of foreign language signs and menus.

[0976] Providing information

[0977] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[0978] Specific examples

[0979] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[0980] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[0981] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[0982] With these functions, the system of the present invention allows users to obtain detailed information in real time, making daily life, travel, shopping, etc. more convenient and efficient.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] The device captures the user's view in real time, with the device's camera constantly capturing the latest video data.

[0986] Step 2:

[0987] The device sends the captured video data to the server using the device's network functions, ensuring low latency.

[0988] Step 3:

[0989] The server receives the video data sent from the device, temporarily stores it, and prepares it for analysis.

[0990] Step 4:

[0991] The server inputs the received video data into the generative AI model and begins analysis, where objects and text information are extracted.

[0992] Step 5:

[0993] The server accesses a database based on the analysis results to retrieve relevant information, such as tourist attraction information, product reviews, and foreign language translation results.

[0994] Step 6:

[0995] The server then sends the acquired information to the device, again using the device's network capabilities to quickly transmit the data.

[0996] Step 7:

[0997] The terminal displays the received information to the user: text information is displayed on a display, and audio information is played through a speaker.

[0998] Step 8:

[0999] The user can review the information provided and, if desired, take further action to obtain additional information, such as capturing a new object with the camera.

[1000] This allows users to easily obtain the information they need in real time, making their daily lives and travels more convenient and fulfilling.

[1001] Example 1

[1002] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1003] In modern society, it is extremely important for users to quickly and accurately obtain the information they need in real time in their daily lives, travel, shopping, etc. However, conventional technology does not adequately provide a system that can instantly analyze the information that users visually obtain and provide the results immediately. This poses the problem of users having to spend time and effort searching for the information they are looking for.

[1004] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1005] In this invention, the server includes means for acquiring visual information from a device worn by a user, means for transmitting the acquired visual information to a central processing unit, means for receiving the visual information in the central processing unit, means for analyzing the received visual information using a generative AI model, means for acquiring related data from an information storage device based on the analysis results, means for transmitting the acquired data to the user's device, and means for displaying the received data on the user's device, thereby enabling the user to acquire rapid and accurate information in real time based on their own visual information.

[1006] A "user-worn device" is a device worn by a user on the body, and includes a camera for acquiring visual information and a display for displaying information.

[1007] "Visual information" refers to video data captured by a camera, such as objects and text within the user's field of view.

[1008] A "central processing unit" is a device that receives visual information sent from a terminal and analyzes it using a generative AI model.

[1009] A "generative AI model" is a model that uses machine learning and deep learning techniques to analyze input data and recognize objects and text information.

[1010] An "information storage device" is a storage medium, such as a database or file system, accessed by a central processing unit, that holds related data.

[1011] "Related data" refers to information such as detailed descriptions, evaluations, and translations of the object, which is obtained based on the analyzed visual information.

[1012] The "display means" refers to a means for presenting information to a user, including a display built into a device worn by the user and an audio output device.

[1013] The "means for displaying received data" refers to a means for the device worn by the user to display relevant data received from the central processing unit.

[1014] The present invention provides a system that connects a user-worn device with a server. This system utilizes a generative AI model to analyze video data in real time and provide the user with the information they need instantly.

[1015] Terminal side processing

[1016] The device worn by the user (AR glasses) is equipped with a camera and has the ability to capture the user's field of view in real time. This video data is stored in temporary memory within the device. Once the device captures the video data, it transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[1017] Server-side processing

[1018] The server receives the video data sent from the device. The received video data is input into a generative AI model (e.g., TensorFlow or PyTorch) to analyze the objects and text information. Based on the analysis results, the server accesses a database (information storage device) to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews of stores, and translations of signs and menus in foreign languages.

[1019] Providing information

[1020] The information obtained through the analysis is sent back to the device from the server. The device then displays the received information in the user's field of vision. Text information is displayed on the screen, and audio information is provided through the speaker. This allows users to easily obtain the information they need in real time.

[1021] Specific examples

[1022] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image and sends it to the server. The server analyzes the image data and recognizes the name of the building and its historical background. Based on the analysis results, the server retrieves detailed explanatory information from a database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[1023] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and sends them to the device. The device then displays this information on its display, allowing the user to check the ratings and reviews for the product.

[1024] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image and sends it to the server. The server analyzes the image and extracts the foreign text. Based on the analysis, the server translates the text and sends the translation result to the device. The device then displays the translation result on its display, allowing the user to see the translated text in real time.

[1025] Prompt Sentence Examples

[1026] The following prompt sentences can be used as input to a generative AI model:

[1027] 1. "I want the AR glasses to analyze the images being viewed by users in real time, and retrieve and display relevant information."

[1028] 2. "The names and background information of historical buildings installed at tourist sites will be used in real-time commentary."

[1029] 3. "Get and display reviews and ratings of products in stores in real time."

[1030] 4. "Translate foreign language signs and menus and display the translation results on the device display."

[1031] The system allows users to obtain detailed information in real time, making daily life, travel, shopping, and more convenient and efficient.

[1032] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1033] Step 1: Visual Capture

[1034] Device behavior

[1035] The camera in the device (AR glasses) worn by the user captures the user's field of view in real time. The camera image is the input, and the captured image data is generated as the output. This image data is temporarily stored in the device's memory.

[1036] Step 2: Sending video data

[1037] Device behavior

[1038] The device sends the captured video data to the server. The input is the captured video data, and the output is the data sent to the server. The data is encrypted and sent over a network connection.

[1039] Step 3: Receiving video data

[1040] Server Operation

[1041] The server receives video data sent from the terminal. The received video data is input, and the data stored in the buffer is generated as output. The received data is temporarily saved in the buffer.

[1042] Step 4: Data analysis

[1043] Server Operation

[1044] The server inputs the received video data into a generative AI model. The received video data is the input, and the analysis results are generated as the output. The generative AI model uses deep learning frameworks such as TensorFlow and PyTorch to extract and recognize objects and text information.

[1045] Step 5: Obtain related information

[1046] Server Operation

[1047] The server retrieves related data from an information storage device (database) based on the analysis results. The analysis results are input, and the retrieved related information is generated as output. SQL queries are executed against the database to retrieve related details, reviews, and translation data.

[1048] Step 6: Sending acquired data

[1049] Server Operation

[1050] The server sends the acquired relevant information to the terminal. The acquired relevant information is the input, and data to be sent to the terminal is generated as the output. The data is packaged in a pre-specified format (for example, JSON format), encrypted, and sent.

[1051] Step 7: Display information

[1052] Device behavior

[1053] The device analyzes the information received from the server and displays it in the user's field of view. The received data is the input, and the information displayed on the display is generated as the output. Text information is overlaid on the display, and audio information is provided through the speaker.

[1054] Step 8: Verify the user

[1055] User behavior

[1056] The user checks the information displayed on the device's display and listens to audio information as needed. The displayed information is the input, and the user's understanding and judgment are the output. This allows the user to take action and make choices based on visual information in real time.

[1057] (Application example 1)

[1058] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1059] Traditional shopping experiences in brick-and-mortar stores require a lot of effort to obtain product information, making efficient shopping difficult. It also takes time to obtain detailed information, reviews, and sales information about specific products, and in some cases users may not be able to find the information they are looking for. Furthermore, there are also issues with long wait times when paying at the register, which degrades the user experience.

[1060] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1061] In this invention, the server includes: means for analyzing video data in real time using a generative AI model; means for capturing video data from a display device worn by a user; means for transmitting the video data to the server via a network; means for analyzing the transmitted video data and recognizing objects; means for retrieving related information from a database based on the analysis results; means for transmitting and displaying the retrieved information on the user's display device; means for retrieving product names, prices, ratings, and reviews based on the captured product information; and means for providing the retrieved product information to the user via a virtual cart function and making payments. This allows users to obtain all information about products in physical stores in real time and efficiently shop. Furthermore, payment through the virtual cart function reduces waiting times at the cash register and provides a more comfortable shopping experience.

[1062] A "generative AI model" is an artificial intelligence algorithm used to analyze a user's visual data and recognize objects.

[1063] A "display device" is a screen or display device worn by a user to visually view information.

[1064] A "network" is a communications infrastructure used to send and receive information.

[1065] A "server" is a computer system that has a central processing unit for storing, processing, and analyzing data.

[1066] A "database" is an information system that systematically stores related information and makes it available.

[1067] "Virtual Cart Function" refers to a software function that allows users to virtually add and pay for selected items.

[1068] This invention is a system that enhances the shopping experience in brick-and-mortar stores by linking a user-worn display device with a server. The system uses a generative AI model to analyze video data in real time and provide the user with the information they need.

[1069] Terminal side processing

[1070] The display device worn by the user (e.g., AR glasses) is equipped with a camera and captures the user's field of view in real time. The captured video data is sent to a server via a network. The display device also has a display function to provide visual information to the user and an audio output device.

[1071] Server-side processing

[1072] The server receives the video data sent from the device and inputs it into the generative AI model. The generative AI model analyzes the video data and recognizes the object (e.g., product). Based on the recognition result, the server retrieves related information (price, reviews, ratings, sale information, etc.) from the database. The retrieved information is then sent back to the device and displayed on the user's display device.

[1073] Hardware and software used

[1074] Hardware:

[1075] User device: AR glasses (camera, display, speaker)

[1076] Server: High-performance AI server

[1077] Network: High-speed internet connection

[1078] software:

[1079] Generative AI models: image recognition models, natural language processing models

[1080] Frameworks: TensorFlow, PyTorch

[1081] Data Management: SQL Database

[1082] Specific examples

[1083] Consider a user browsing a store shelf. The AR glasses worn by the user capture the product and send the image data to a server. The server uses an image recognition model to identify the product and retrieves related details (e.g., product name, price, reviews) from a database. This information is then sent back to the device and displayed on the AR glasses' display. The user can then continue shopping while checking the information.

[1084] Furthermore, the virtual cart function allows users to virtually add their favorite products to their cart and make smart payments, reducing waiting times at the cash register and enabling efficient shopping.

[1085] Prompt Sentence Examples

[1086] The following prompts could be fed to a generative AI model:

[1087] "Recognize products from camera footage and instantly retrieve and display detailed product information, including product name, price, user reviews, and sales information. It also suggests related products and allows real-time payment using a virtual cart function."

[1088] This system allows users to enjoy a more comfortable and efficient shopping experience in physical stores.

[1089] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1090] Step 1:

[1091] The camera on the device (the display device worn by the user) captures the user's field of view in real time. The input is the video data from the camera, and the output is real-time video data.

[1092] Step 2:

[1093] The video data captured by the terminal is sent to the server via the network. The input is the video data captured by the terminal, and the output is the video data sent to the server.

[1094] Step 3:

[1095] The video data received by the server is input into a generative AI model, which analyzes and recognizes objects in the video. The input is the video data received by the server, and the output is the recognition result of the object. This analysis uses an image recognition model to specifically identify the product.

[1096] Step 4:

[1097] Based on the object recognition results, the server retrieves related information (product name, price, reviews, ratings, sale information, etc.) from the database. The input is the object recognition results, and the output is the product information retrieved from the database.

[1098] Step 5:

[1099] The server transmits the acquired product information to the terminal via the network. The input is the information acquired from the database, and the output is the product information transmitted to the terminal.

[1100] Step 6:

[1101] The terminal displays the product information received from the server in the user's field of view. The input is the product information sent from the server, and the output is the product information that the user can see through the display device.

[1102] Step 7:

[1103] When a user checks product information and adds it to the virtual cart, the terminal sends the information to the server, which then processes the addition of the product to the virtual cart. The input is the user's selection, and the output is the product information added to the virtual cart.

[1104] Step 8:

[1105] The server aggregates the product information in the virtual cart and processes the payment. The input is the product information added to the virtual cart, and the output is the payment completion status. At this time, a payment gateway is used if necessary.

[1106] At each step, the following specific actions are performed:

[1107] Step 1: The camera captures the user's field of view in high resolution.

[1108] Step 2: Send the video data to the server using Wi-Fi or mobile data.

[1109] Step 3: The generative AI model analyzes the data and recognizes the product.

[1110] Step 4: Retrieve product information from the database using an SQL query.

[1111] Step 5: Transfer information securely using the HTTPS protocol.

[1112] Step 6: Overlay information on the display.

[1113] Step 7: Add items to your virtual cart with touch or voice commands.

[1114] Step 8: Process the payment in real time via the payment gateway.

[1115] This allows users to shop smoothly and efficiently in-store.

[1116] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1117] This invention provides a system that combines an emotion engine that recognizes the user's emotions. This system uses a device worn by the user, a server, and a generative AI model to analyze video data in real time and provide the user with the information they need immediately. Furthermore, the emotion engine makes it possible to provide information according to the user's emotional state.

[1118] Terminal side processing

[1119] The device worn by the user (AR glasses) has features such as a camera and built-in sensors that capture the user's field of view and emotional state in real time. The device captures video data and emotional data and transmits it to a server. It also includes a display and audio output device that allows the user to receive information visually or audibly.

[1120] Server-side processing

[1121] The server receives the video and emotion data sent from the device. The received video data is input into a generative AI model, which analyzes the object and text information. The emotion engine also analyzes the received emotion data to recognize the user's emotional state. Based on the analysis results, the server accesses a database to retrieve related information. This information may include detailed descriptions of tourist attractions, product reviews for stores, and translations of signs and menus in foreign languages.

[1122] Providing information

[1123] The server adaptively customizes the information obtained through the analysis and sends it to the device. For example, if the user is excited, it provides more detailed explanations, and if the user is tired, it provides simple information. The device displays the received information in the user's field of view. Text information is displayed on the screen, and audio information is provided through the speaker.

[1124] Specific examples

[1125] As a concrete example, consider the case where a user visits a tourist spot. The user puts on AR glasses and looks at a tourist attraction (e.g., a historical building). The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, which analyzes the image data and recognizes the name of the building and its historical background. At the same time, the emotion engine analyzes the user's emotional state and determines that the user is excited. Based on the analysis results, the server retrieves detailed explanatory information from the database and sends it to the device. The device displays this information on its display, allowing the user to receive detailed explanations about the building in real time.

[1126] Next, we will explain a specific example of when a user looks at a product in a store. When the user looks at a product through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's satisfaction. This data is sent to a server, which analyzes the image and recognizes the product name and brand. Based on the analysis results, the server retrieves reviews and ratings for the product from a database and adjusts the detail of the reviews to reflect the user's satisfaction. The device displays this information on its screen, allowing the user to check detailed reviews and ratings for the product.

[1127] This also explains what happens when a user sees a foreign sign or menu. When a user looks at foreign text through the AR glasses, the device's camera captures the image, and the emotion sensor detects the user's confusion. This data is sent to the server, which analyzes the image and extracts the foreign text. At the same time, the emotion engine analyzes the user's confusion and generates a detailed translation result that reflects this. Based on the analysis results, the server translates the text and sends it to the device. The device then displays the translation result on the screen, allowing the user to view the translated text in real time.

[1128] With these functions, the system of the present invention not only allows users to obtain detailed information in real time, but also provides customized information tailored to the user's emotional state, making daily life, travel, shopping, and more convenient and efficient.

[1129] The processing flow will be explained below.

[1130] Step 1:

[1131] The device activates a camera to capture the user's field of view in real time, while simultaneously measuring the user's emotional state with an emotion sensor and capturing that data.

[1132] Step 2:

[1133] The user looks at an object (such as a tourist attraction or a product) they want to see through the AR glasses. At this time, the camera in the AR glasses captures the image of the object, and the built-in emotion sensor measures the user's emotion.

[1134] Step 3:

[1135] The device transmits the captured video data and emotion data to a server, where the data is transmitted over a network with low latency.

[1136] Step 4:

[1137] The server receives the video data and emotion data sent from the device and prepares the received data for analysis.

[1138] Step 5:

[1139] The server inputs the video data into a generative AI model that analyzes objects and text information, such as the names of tourist attractions and product details.

[1140] Step 6:

[1141] The server uses an emotion engine to analyze the received emotion data and recognize the user's emotional state, for example, determining whether the user is excited or confused.

[1142] Step 7:

[1143] Based on the analysis results, the server accesses a database to retrieve relevant information, such as detailed descriptions of tourist attractions, product reviews, and foreign language translations.

[1144] Step 8:

[1145] The server takes into account the user's emotional state and customizes the information it provides, for example providing detailed information if the user is excited and brief information if the user is tired.

[1146] Step 9:

[1147] The server sends the customized information to the device in the appropriate format (text, voice).

[1148] Step 10:

[1149] The terminal displays the received information to the user: text information on the display and audio information through the speaker.

[1150] Step 11:

[1151] The user reviews the displayed information and, if necessary, can request more detailed information or information about a different object, in which case the process begins again from step 1.

[1152] Through the above processing steps, users can not only obtain detailed information in real time, but also enjoy customized information tailored to their emotional state, making daily life, travel, shopping, etc. more convenient and efficient.

[1153] Example 2

[1154] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1155] Conventional information provision systems have difficulty in providing customized information based on the user's real-time emotional state. Furthermore, while they have advanced analytical capabilities such as recognizing specific objects and text information, they have problems in that they are unable to provide flexibility in the accuracy and delivery of information according to the user's emotions.

[1156] The identification process 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 means for capturing video data from a device worn by the user, means for detecting the user's emotional state using a built-in sensor, means for transmitting the captured video data and emotional data to the server, means for analyzing the transmitted video data using a generative AI model and recognizing objects and text information, means including an emotion engine for analyzing the emotional data and recognizing the user's emotional state, means for retrieving related information from a database based on the analysis result and the emotional state, and means for generating customized information and transmitting it to the user's device for display. This makes it possible to provide information customized according to the user's emotional state in real time.

[1157] A "device worn by the user" is a device such as AR glasses that can be worn by the user to take in images into their field of vision.

[1158] "Built-in sensors" are sensors built into devices to measure biometric information such as the user's heart rate and facial expressions.

[1159] "Video data" is digital data that captures video of objects or scenes that come into the user's field of view.

[1160] "Emotional data" refers to data that represents the user's emotional state as inferred from factors such as their heart rate and facial expression measured by the built-in sensor.

[1161] A "generative AI model" is an artificial intelligence model trained using large datasets, and is a technology for analyzing video data and recognizing objects and text information.

[1162] A "server" is a computer system that processes data received from a terminal over a network and returns the analysis results.

[1163] An "emotion engine" is an algorithm or software that analyzes emotional data to recognize a user's emotional state.

[1164] A "database" is a data storage system that stores related information and retrieves required information based on the results of analysis.

[1165] "Customized information" refers to detailed commentary, reviews, translations, and other information tailored to the user's emotional state.

[1166] This invention is a system that utilizes an emotion engine that recognizes the user's emotions and a generative AI model, and uses a device worn by the user and a server to analyze video data in real time and provide the user with the information they need instantly.

[1167] Hardware and Software Configuration

[1168] The system consists of the following major hardware and software components:

[1169] 1. Devices worn by users

[1170] The device worn by the user, such as AR glasses, is equipped with a camera and built-in sensors (such as a heart rate monitor and facial recognition sensor). This device captures images in the user's field of vision and simultaneously detects their emotional state. The device also includes a display and speaker, providing the acquired information visually and audibly to the user.

[1171] 2. Server

[1172] The server is a computer system that receives and processes data sent from the device worn by the user. The server is equipped with a generative AI model (e.g., implemented in TensorFlow or PyTorch) and an emotion engine (e.g., Emotion API or Affectiva).

[1173] Data processing and calculation

[1174] This system processes and calculates data through the following processes.

[1175] 1. Acquiring video data and emotion data

[1176] The user wears the device and captures the image of their field of vision. The built-in sensors detect the user's emotional state (e.g., excitement, satisfaction, confusion, etc.) in real time. This data is then sent from the device to a server.

[1177] 2. Analysis of video data and emotion data

[1178] On the server, the video data is first input into a generative AI model to recognize objects and text information, allowing it to identify, for example, specific tourist attractions or product names. At the same time, an emotion engine analyzes the emotion data and identifies the user's emotional state.

[1179] 3. Information Collection and Customization

[1180] Based on the analysis results of the generative AI model and emotion engine, the server retrieves relevant information from a database, such as the history and detailed descriptions of tourist attractions, product reviews and ratings, and translations of foreign language signs and menus. It then customizes the level of detail and format of the information depending on the user's emotional state.

[1181] 4. Provision of Information

[1182] The server then sends the customized information to the user's device, which then displays it visually or provides it as audio. For example, if the user is excited, detailed information is displayed, and if the user is tired, simple information is provided.

[1183] Specific use cases

[1184] As specific examples, we will explain visiting tourist spots, shopping, and translating signs and menus in foreign countries.

[1185] When visiting tourist spots

[1186] When a user visits a tourist attraction, they put on the AR glasses and look at the attraction. The device's camera captures the image, and the emotion sensor detects the user's excitement. This data is sent to the server, where it is analyzed to identify the name and historical background. The emotion engine recognizes the user's excitement, and the server sends detailed explanatory information to the device. The user can view this information in real time on the display.

[1187] For shopping

[1188] When a user looks at a product in a store, the device captures the image as they look at it through the AR glasses, and the emotion sensor detects their level of satisfaction. The server analyzes this data to identify the product name and brand. Depending on their level of satisfaction, detailed reviews and ratings are provided.

[1189] Translation of foreign signs and menus

[1190] When a user sees a foreign sign or menu, the device captures the video and an emotion sensor detects confusion. The server analyzes the video and extracts the foreign language text. A detailed translation based on the user's emotional state is generated and sent to the user's device.

[1191] Prompt Sentence Examples

[1192] "Taking video data of a tourist spot and the user's emotional state as input, generate a detailed commentary and output customized information by providing additional information if the user is excited."

[1193] With the above configuration, the present invention can provide users with appropriate and customized information in real time, improving their experience in various situations.

[1194] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1195] Step 1:

[1196] The user puts on the AR glasses and captures the image that comes into their field of vision. Specifically, when the user directs their gaze at a specific tourist attraction or product, the device's camera captures the image as digital data. The input is the image that appears in their field of vision, and the output is video data. In addition, built-in sensors measure biometric information such as heart rate and facial expressions, and record this as emotional data.

[1197] Step 2:

[1198] The device transmits the captured video data and emotion data to the server. Specifically, the data is encrypted and transmitted over the network. The input is the video data and emotion data captured by the device, and the output is the encrypted data transferred to the server.

[1199] Step 3:

[1200] The server receives and decrypts the data sent from the terminal. Specifically, it decrypts the received data into an appropriate format and separates the video data and emotion data. The input at this time is encrypted video data and emotion data, and the output is decrypted video data and emotion data.

[1201] Step 4:

[1202] The server analyzes the decoded video data using a generative AI model to recognize objects and text information. For example, TensorFlow or PyTorch can be used to analyze the video data and identify tourist attractions or product names. The input is the decoded video data, and the output is the object information and text information resulting from the analysis.

[1203] Step 5:

[1204] The server analyzes the decoded emotional data with an emotion engine to recognize the user's emotional state. Specifically, it processes the emotional data using Emotion API and Affectiva to determine whether the user is excited, tired, or confused. The input is the decoded emotional data, and the output is the user's emotional state.

[1205] Step 6:

[1206] The server retrieves related information from a database based on the analysis results. For example, it searches the database for detailed descriptions of tourist attractions, product reviews, translations of foreign texts, etc. The input is the object information and the user's emotional state, and the output is related information.

[1207] Step 7:

[1208] The server customizes the acquired information according to the user's emotional state. Specifically, it generates detailed information when the user is excited and concise information when the user is tired. The input is the acquired related information and the user's emotional state, and the output is the customized information.

[1209] Step 8:

[1210] The server encrypts and transmits the customized information to the user's device. The device decrypts the received information and displays it on a display or provides it as audio information from a speaker. The input in this case is the customized information, and the output is information provided to the user visually or audibly.

[1211] (Application example 2)

[1212] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1213] Conventional systems acquire and analyze video data through a wearable device worn by the user, but because they do not provide information that takes the user's emotional state into consideration, the information provided does not necessarily match the user's needs or situation.The present invention aims to provide a system that combines an emotion engine and provides adaptive information according to the user's emotional state, thereby improving the user experience.

[1214] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1215] In this invention, the server includes means for analyzing video data in real time using a generative AI model, means for capturing video data from a wearable device worn by a user, means for transmitting video data and emotion data to the server, means for analyzing the transmitted video data and recognizing objects, means for analyzing the user's emotion data using an emotion engine, means for retrieving related information from a database based on the analysis results, and means for transmitting the retrieved information to the user's wearable device and displaying it. This enables the provision of adaptive and detailed information according to the user's emotional state.

[1216] A "generative AI model" is a machine learning model that uses artificial intelligence to extract patterns and features from data and perform specific tasks.

[1217] "Real-time" refers to a processing method in which data is acquired and analyzed instantaneously, providing results immediately.

[1218] "Video Data" means visual information captured by a camera or other video capture device.

[1219] A "wearable device worn by a user" is an electronic device that can be worn by a user and has functions such as a camera, sensor, and display.

[1220] "Emotion data" is data that expresses the user's emotional state as numerical values ​​or categories.

[1221] "Transmitting means" refers to the functionality or technology for transferring data from one device to another.

[1222] A "server" is a computer system that processes, stores, and distributes data over a network.

[1223] "Target" refers to an object, place, person, etc. identified within video data.

[1224] An "emotion engine" is software or hardware that has the function of analyzing and recognizing the user's emotional state and outputting the results as data.

[1225] A "database" is a system for storing and managing data efficiently and systematically.

[1226] "Means for transmitting and displaying on a wearable device" refers to a function or technology for sending data to a wearable device and presenting information to the user using its display or audio output device.

[1227] The present invention relates to a system consisting of a wearable device (e.g., smart glasses) worn by a user and a server. This system can analyze video data in real time and provide information according to the user's emotional state.

[1228] The user wears a wearable device (smart glasses). The wearable device is equipped with a camera, an emotion sensor, a display, and an audio output device. The device captures the user's field of view and simultaneously monitors the user's emotional state in real time. The captured video data and emotion data are sent to a server.

[1229] When the server receives the video data, it uses the generative AI model to analyze the video data and recognize the object. For example, if a user looks at a product on a shelf, it recognizes the product and obtains information about it. It also uses an emotion engine to analyze the user's emotional data and understand their current emotional state. This allows the server to select appropriate information according to their emotional state.

[1230] Based on the analysis results, the server retrieves relevant information from the database. For example, if the user is excited, it displays detailed product reviews and ratings. If the user is confused, it provides a brief explanation and price comparison information. This information is sent to the wearable device and provided to the user through a display or audio output device.

[1231] As a concrete example, consider a user in a brick-and-mortar bookstore. The user puts on smart glasses and picks up a book. At this time, the camera in the smart glasses captures the book, and the emotion sensor detects the user's state of excitement. This data is sent to the server, which retrieves the book's title and detailed review and adaptively displays them to the user. The generation AI prompt sentence used is, "Please generate a book title and review. The user is excited, so please be detailed."

[1232] Furthermore, consider the case where a user is in an electronics store. The user puts on smart glasses and looks at a home appliance. The camera captures the product, and the emotion sensor detects a confused state. This allows the server to provide a brief description of the product's features and price comparison information. The generated AI prompt sentence is, "The user is confused. Please provide a brief description of the product's features and price comparison information."

[1233] In this way, the system of the present invention can provide appropriate information in real time according to the user's emotional state, thereby significantly improving the user experience.

[1234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1235] Step 1:

[1236] A user wears a wearable device (smart glasses) and visually recognizes products in a store. At this time, a camera captures the product image, and an emotion sensor acquires the user's emotion data in real time.

[1237] Input: Product video, emotion data

[1238] Output: Captured video and emotion data

[1239] Step 2:

[1240] The device transmits the captured video data and emotion data to a server using a communication module that bundles the video data and emotion data and transmits them to the server via a network.

[1241] Input: Captured video and emotion data

[1242] Output: Video and emotion data sent to the server

[1243] Step 3:

[1244] The server inputs the received video data into the generative AI model and performs video analysis. This is the process by which the generative AI model recognizes objects (e.g., products) from the video data.

[1245] Input: Video data sent to the server

[1246] Output: Data of recognized objects (products)

[1247] Step 4:

[1248] The server simultaneously inputs the received emotional data into the emotion engine, which analyzes the user's emotional state and quantifies it as happiness, excitement, confusion, or other emotional states.

[1249] Input: Emotion data sent to the server

[1250] Output: Parsed user's emotional state

[1251] Step 5:

[1252] The server retrieves relevant information from the database based on the object recognition results of the generative AI model and the emotional state analysis results of the emotion engine, such as product reviews and ratings, detailed product descriptions, and price information.

[1253] Input: Recognized object data, analyzed user emotional state

[1254] Output: Relevant information obtained

[1255] Step 6:

[1256] The server adaptively customizes the information it retrieves, tailoring it to the user's emotional state: for example, providing detailed reviews if the user is excited, or concise explanations if the user is confused.

[1257] Input: retrieved relevant information, analyzed user emotional state

[1258] Output: Customized information

[1259] Step 7:

[1260] The server sends the customized information to the terminal, which is then formatted as text data and sent to the wearable device via a network.

[1261] Input:Customized Information

[1262] Output: Information sent to the wearable device

[1263] Step 8:

[1264] The terminal (wearable device) displays the received information in the user's field of vision, showing detailed product reviews, ratings, and pricing information on the display, and providing audio information via an audio output device if necessary.

[1265] Input: Information sent to the wearable device

[1266] Output: Information displayed to the user's eyes or output as audio

[1267] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1268] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1269] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1270] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1271] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1272] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1273] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1274] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1275] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1276] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1277] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1278] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1279] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1280] 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.

[1281] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1282] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1283] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1284] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1285] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1286] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1287] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1288] The following is further disclosed regarding the above embodiment.

[1289] (Claim 1)

[1290] A means to analyze video data in real time using generative AI models;

[1291] means for capturing video data from a user-worn device;

[1292] means for transmitting video data to a server;

[1293] A means for analyzing the transmitted video data and recognizing an object;

[1294] means for retrieving related information from a database based on the analysis results;

[1295] means for transmitting the acquired information to a user's device and displaying the information;

[1296] A system including:

[1297] (Claim 2)

[1298] The system of claim 1, which provides a detailed description of the object based on the analysis results.

[1299] (Claim 3)

[1300] The system of claim 1, which provides a review or rating of the object based on the analysis results.

[1301] (Claim 4)

[1302] The system according to claim 1, wherein the displayed text is translated into another language based on the analysis results and provided.

[1303] "Example 1"

[1304] (Claim 1)

[1305] means for acquiring visual information from a device worn by a user;

[1306] means for transmitting the acquired visual information to a central processing unit;

[1307] means for receiving visual information at a central processing unit;

[1308] A means for analyzing the received visual information using a generative AI model;

[1309] means for retrieving relevant data from an information storage device based on the analysis results;

[1310] means for transmitting the acquired data to a user device;

[1311] means for displaying the received data on the user's device;

[1312] A system including:

[1313] (Claim 2)

[1314] The system of claim 1, which provides a detailed description of the object based on the analysis results.

[1315] (Claim 3)

[1316] The system of claim 1, which provides a review or rating of the object based on the analysis results.

[1317] "Application Example 1"

[1318] (Claim 1)

[1319] A means to analyze video data in real time using generative AI models;

[1320] means for capturing video data from a user-worn display device;

[1321] means for transmitting video data to a server via a network;

[1322] A means for analyzing the transmitted video data and recognizing an object;

[1323] means for retrieving related information from a database based on the analysis results;

[1324] means for transmitting the acquired information to a user's display device for display;

[1325] a means for obtaining product names, prices, ratings, and reviews based on the captured product information;

[1326] A means for providing the acquired product information to the user through a virtual cart function and making a payment;

[1327] A system including:

[1328] (Claim 2)

[1329] The system of claim 1, which provides a detailed description of the object based on the analysis results.

[1330] (Claim 3)

[1331] The system of claim 1, which provides a review or rating of the object based on the analysis results.

[1332] "Example 2: Combining Emotion Engines"

[1333] (Claim 1)

[1334] a means for capturing video data from a user-worn device;

[1335] means for detecting the emotional state of the user using an on-board sensor;

[1336] means for transmitting the captured video data and emotion data to a server;

[1337] A means for analyzing the transmitted video data using a generative AI model and recognizing objects and text information;

[1338] means including an emotion engine for analyzing emotion data to recognize an emotional state of a user;

[1339] means for retrieving relevant information from a database based on the analysis results and the emotional state;

[1340] means for generating, transmitting and displaying customized information on the user's device;

[1341] A system including:

[1342] (Claim 2)

[1343] The system of claim 1, which provides a detailed explanation according to the analysis results and the emotional state of the user.

[1344] (Claim 3)

[1345] The system of claim 1, providing reviews and ratings according to the analysis results and the emotional state of the user.

[1346] "Application example 2 when combining emotion engines"

[1347] (Claim 1)

[1348] A means to analyze video data in real time using generative AI models;

[1349] means for capturing video data from a wearable device worn by a user;

[1350] means for transmitting video data and emotion data to a server;

[1351] A means for analyzing the transmitted video data and recognizing an object;

[1352] means for analyzing user emotion data using an emotion engine;

[1353] means for retrieving related information from a database based on the analysis results;

[1354] means for transmitting the acquired information to a wearable device of the user and displaying the information;

[1355] A system including:

[1356] (Claim 2)

[1357] The system according to claim 1, wherein the system provides a detailed commentary according to the emotional state of the user based on the analysis results.

[1358] (Claim 3)

[1359] 2. The system according to claim 1, wherein the system provides reviews and ratings of the object according to the emotional state of the user based on the analysis results. [Explanation of symbols]

[1360] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to analyze video data in real time using generative AI models; means for capturing video data from a user-worn device; means for transmitting video data to a server; A means for analyzing the transmitted video data and recognizing an object; means for retrieving related information from a database based on the analysis results; means for transmitting the acquired information to a user's device and displaying the information; A system including:

2. The system of claim 1 , further comprising: a computer program for providing a detailed description of the object based on the analysis results.

3. The system of claim 1 , further comprising: a system for providing a review or rating of an object based on the analysis results.

4. The system according to claim 1, wherein the displayed text is translated into another language based on the analysis results and provided.

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