System

Abstract

A system is provided.SOLUTION: A system comprising: visual devices for capturing visual material; transferring means for transferring the visual material sent from the visual devices to cloud servers; analyzing means for analyzing and classifying the visual material on the cloud servers by generation AI; and storage means for storing and providing the analyzed and classified visual material in a form easily accessible to users.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] There is a need for systems that enable the efficient recording, organization, and playback of visual information, and provide users with easily accessible digital versions of the information and visual experiences they encounter daily. In particular, there is a need for systems that can accommodate a wide range of usage scenarios, including learning support, memory storage, lifestyle support for the visually impaired, and recording eyewitness accounts of incidents and accidents. However, existing visual information recording systems lack the means for users to efficiently manage and playback visual information, and they lack sufficient voice conversion functions for the visually impaired and methods for accurately recording and providing eyewitness accounts of incidents and accidents. [Means for solving the problem]

[0005] The system includes a visual device that captures visual data, a transfer means that transfers the visual data sent from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using generative AI, a storage means that stores and provides the analyzed and classified visual data in a form that is easily accessible to users, and a search means that searches the analyzed and classified visual data based on the user's keywords and provides it to the user's terminal, thereby achieving efficient recording, organization, and playback of visual information. The system also includes a voice conversion means that analyzes the captured visual data and converts it into voice data, and a voice output means that outputs voice data in real time, thereby providing support for the daily lives of visually impaired people. In this way, the system provides support for a wide variety of usage scenarios and meets user needs.

[0006] A "visual device" is a device that captures visual information that comes into the user's field of vision and collects it as data.

[0007] "Transfer Mechanism" is a function or process for sending visual data collected from a visual device to a cloud server.

[0008] "Analysis means" refers to the function or process that analyzes visual data on a cloud server using generative AI to understand and classify the content of the data.

[0009] "Storage means" refers to the functionality or process for storing and preserving the analyzed and categorized visual data in a form that is easily accessible to the user.

[0010] "Search means" refers to a function or process for searching the analyzed and classified visual data based on the user's keywords or specified conditions, and providing the required information.

[0011] "Audio conversion means" is a function or process for analyzing visual data captured by a visual device and converting it into audio data.

[0012] "Audio output means" is a function or process for providing audio data to a user in real time. [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] ---

[0035] This invention is a system for efficiently recording, organizing, and reproducing visual data, which digitizes the information and visual experiences that users see on a daily basis and provides them in an easily accessible form. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, and a user terminal.

[0036] Visual Devices

[0037] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0038] Cloud Server and Generative AI

[0039] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0040] User terminal

[0041] A user device (such as a smartphone or computer) provides an interface for accessing the visual data stored in the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for relevant visual data and provides it to the user device.

[0042] Specific examples

[0043] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0044] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0045] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[0046] As described above, the present invention is a system that effectively records, organizes, and plays back visual information, providing an environment in which users can easily manage and use visual data in a variety of usage scenarios.

[0047] ---

[0048] The processing flow will be explained below.

[0049] ---

[0050] Step 1:

[0051] The user wears the visual device, which begins capturing the user's visual information in real time.

[0052] Step 2:

[0053] The device temporarily stores the visual data captured from the vision device in its internal memory, where the captured visual data is preprocessed and noise is removed.

[0054] Step 3:

[0055] The device periodically generates packets of visual information, encrypts them, and transmits them to a cloud server, where the user's data is stored securely.

[0056] Step 4:

[0057] The server receives packets of visual data, which are then analyzed by the generative AI and evaluated for importance and interest.

[0058] Step 5:

[0059] The generative AI in the server segments the visual data frame by frame and identifies key scenes based on the user's intent and the environment, effectively classifying the data.

[0060] Step 6:

[0061] The server tags and classifies the data based on the analysis results and stores it in a format that is easily accessible to users.

[0062] Step 7:

[0063] A user requests specific visual data through a smartphone app, for example by entering keywords such as "scenery of a tourist spot."

[0064] Step 8:

[0065] The server receives the user's request and searches for relevant visual data based on tags and classification information, and the search results are filtered appropriately.

[0066] Step 9:

[0067] The server provides the retrieved visual data to the user's device in streaming or download format, allowing the user to replay the visual data on their smartphone and relive past visual experiences.

[0068] Step 10:

[0069] When a visually impaired person uses a visual device, the visual device captures and transmits the data in real time to a cloud server, where the visual information is analyzed and converted into audio data.

[0070] Step 11:

[0071] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[0072] Step 12:

[0073] When a user encounters an incident or accident, the vision device automatically initiates high-definition recording, which is then immediately sent to a cloud server for tagging and classification.

[0074] Step 13:

[0075] The server stores important incident and accident data, and upon request from law enforcement agencies, provides the data with necessary permission, thereby ensuring that information that is useful to society is used effectively.

[0076] ---

[0077] This completes the detailed step-by-step explanation of the programming process flow of the "Vision Bank" system.

[0078] Example 1

[0079] 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."

[0080] Modern society requires efficient recording, organization, and playback of visual data, but existing technologies make this process cumbersome and often provide insufficient support, particularly for people with visual impairments. Furthermore, there are limited methods for managing vast amounts of visual data and quickly accessing them when needed.

[0081] 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.

[0082] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a terminal, a transmission means that uploads the visual data from the terminal to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI model, and a storage means that tags the analyzed and classified visual data, stores it in storage, and provides it to the visually impaired. This enables efficient recording, organization, and playback of visual data, and provides support to the visually impaired.

[0083] "Visual data" refers to the video and environmental sound data captured by a visual device.

[0084] A "vision device" is a device worn by a user that captures visual data in real time.

[0085] The "transfer means" is a function for transferring the visual data captured by the visual device to the terminal.

[0086] The "transmission means" is a function that allows the terminal to upload visual data to the cloud server.

[0087] A "cloud server" is a remote server for analyzing, categorizing, and storing visual data.

[0088] A "generative AI model" is an artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[0089] "Analysis means" refers to the functionality of the cloud server to analyze and classify visual data using the generated AI model.

[0090] "Tagging" is the process of assigning tags to analyzed and classified visual data for search and identification purposes.

[0091] The "storage means" is a function for storing tagged visual data in a storage and providing it in a form that can be accessed by the user.

[0092] The "search means" is a function for searching the analyzed and classified visual data based on the user's prompt sentence and providing it to the user terminal.

[0093] The "audio conversion means" is a function that analyzes the visual data captured by the visual device and converts it into audio data.

[0094] The "audio output means" is a function that outputs the converted audio data as audio in real time.

[0095] A "prompt sentence" is a linguistic instruction that a user inputs from their device when searching for or requesting visual data from a cloud server.

[0096] This invention is a system for effectively recording, organizing and reproducing visual data, and has a wide range of applications, particularly in supporting the visually impaired. The processing of the program will now be described in detail.

[0097] Hardware and software used

[0098] Hardware

[0099] Vision devices: Eyeglasses or contact lenses worn by the user that capture visual data (video and audio) in real time.

[0100] Terminal: A smartphone or tablet operated by the user. It receives data from the visual device and sends it to the cloud server.

[0101] Cloud Server: A remote server that performs data analysis, classification, and storage.

[0102] software

[0103] Generative AI model: An artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[0104] Data capture with visual devices

[0105] The user wears a visual device that captures the user's field of vision and the surrounding audio in real time and stores this data in a built-in temporary memory. For example, when the user visits a tourist spot, the visual device continuously records the scenery and guide signs.

[0106] Transferring data to your device

[0107] The captured visual data is then transferred to the device via Wi-Fi or Bluetooth. For example, when a user enters a cafe and connects to Wi-Fi, the data is automatically transferred to the device.

[0108] Data reception and analysis by cloud server

[0109] The device uploads the received visual data to a cloud server, which then feeds the data to a generative AI model for analysis such as scene recognition, object detection, and voice recognition. For example, the generative AI model can identify tourist attractions and important signs in the video and extract relevant information.

[0110] Data storage and tagging

[0111] The analyzed and classified visual data is then tagged and stored in cloud server storage. This process makes the data easier to search and allows users to easily access the information they need. For example, data on tourist spots is tagged with "scenery," "famous places," and "signs."

[0112] Data request by user device

[0113] The user starts the smartphone app and inputs a prompt to send a data request to the cloud server. For example, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today."

[0114] Data search and provision via cloud servers

[0115] The cloud server receives the user's request, searches for relevant data from the tagged database, and once it finds the relevant data, it sends it to the user's device. For example, landscape data captured at a tourist spot is searched and provided to the user's smartphone.

[0116] Data playback on user terminal

[0117] The user's smartphone receives the data sent from the cloud server and plays the visual data through the app, allowing the user to re-experience the scenery and guide they saw at the tourist spot. For example, the scenery of the tourist spot is played on the smartphone screen, allowing the user to recreate the experience as if they were actually there.

[0118] Prompt Sentence Examples

[0119] "Play the photos I took today"

[0120] "Search for scenery of tourist spots"

[0121] "Can you give me directions to a nearby restaurant?"

[0122] In this way, the present invention realizes efficient recording, organization, and playback of visual data, providing an environment in which visual data can be easily managed and used in various usage scenarios, and can provide useful support especially for the visually impaired.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The user wears a visual device, which captures video and audio data in real time. The input is the video and environmental sounds that the user sees, which the visual device captures and temporarily stores in its internal memory. Specific operations include the continuous recording of video and audio by the camera and microphone built into the visual device.

[0126] Step 2:

[0127] The data captured by the vision device is transmitted to the terminal via Wi-Fi or Bluetooth. The input is the captured data in the vision device, which is received by the receiving module of the terminal. The specific operation includes the vision device transmitting the data to the terminal via Wi-Fi or Bluetooth. The output is the visual data transmitted to the terminal.

[0128] Step 3:

[0129] The visual data received by the device is uploaded to the cloud server. The input is the data received by the device, which is then sent to the cloud server. The specific operation involves the device uploading the visual data to the cloud server through an Internet connection. The output is the visual data stored on the cloud server.

[0130] Step 4:

[0131] The cloud server receives visual data and analyzes it using a generative AI model. The input is visual data stored on the cloud server, which the generative AI model analyzes frame by frame. Specific data processing includes scene recognition, object detection, and voice recognition. The output is analyzed and classified data.

[0132] Step 5:

[0133] The cloud server assigns appropriate tags to the analyzed and classified data and stores it in storage. The input is the analyzed and classified visual data, which is tagged with specific tags. Specific operations include the tagging and storage process in a database. The output is the tagged visual data stored in the database.

[0134] Step 6:

[0135] The user inputs a prompt via their device to request data from the cloud server. The input is the prompt entered by the user, which is sent to the cloud server by a dedicated app. In concrete terms, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today" into the app's search bar. The output is a request to the cloud server.

[0136] Step 7:

[0137] The cloud server searches for relevant data based on the prompt sentence and provides it to the user's device. The input is a database tagged with the prompt sentence, and the cloud server searches for data based on this. The specific data calculation involves evaluating the degree of match with the prompt sentence and extracting highly relevant data. The output is visual data sent to the user's device.

[0138] Step 8:

[0139] The user device receives the data sent from the cloud server and plays it through the app. The input is visual data sent from the cloud server, which the user views on their smartphone or tablet. Specific operations include the app streaming the received data. The output is visual data displayed on the user device.

[0140] (Application example 1)

[0141] 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."

[0142] Conventional autonomous vehicles lack a mechanism for efficiently collecting visual information from the surroundings, analyzing and classifying it in real time, and providing driving assistance information. This has resulted in insufficient information provision to improve driving safety and efficiency. Furthermore, there is also a lack of effective means for replaying and searching past driving data, making it difficult to recreate accidents or review driving conditions.

[0143] 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.

[0144] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generation AI, a driving assistance means that provides the analyzed visual data as driving assistance information, and a storage means that stores and provides the analyzed and classified visual data in a form that is easily accessible to users. This enables autonomous vehicles to collect and analyze visual information in real time and provide driving assistance information that improves safety and efficiency. Furthermore, the ability to effectively play back and search past driving data makes it easy to recreate accidents and review driving conditions.

[0145] "Visual data" is digital data that includes the user's visual experience and surrounding image information.

[0146] A "visual device" is a device worn by a user that captures visual data, and refers to a device that includes a camera, sensor, etc.

[0147] "Transfer means" refers to a communication means for transferring visual data acquired from a visual device to a cloud server, and includes wired communication and wireless communication.

[0148] A "cloud server" is a server system for storing, processing, and providing data via the Internet.

[0149] "Analysis means" refers to the function that analyzes visual data on a cloud server using AI to generate and classify and tag it.

[0150] "Generative AI" refers to programs and algorithms that use artificial intelligence technology to generate and analyze digital data.

[0151] "Driving assistance means" refers to functions that provide information and warnings necessary for driving based on analyzed visual data, improving safety and efficiency.

[0152] "Storage Means" refers to a storage device or system for storing analyzed and classified visual data and providing it in a form that is easily accessible to users.

[0153] "Search means" refers to a function that allows users to search for keywords or find and provide visual data based on specific conditions.

[0154] "Display means" refers to a device or screen that displays driving assistance information to the driver in real time based on analyzed visual data.

[0155] In implementing this invention, a system consisting of the following elements is used to enable the collection, analysis, driving assistance, and playback / search of visual data.

[0156] Hardware

[0157] 1. Visual devices:

[0158] Vehicle-mounted cameras: Capture visual data in real time.

[0159] Microphone: Collects audio data.

[0160] 2. User Device:

[0161] Smartphone or computer: To access and view the analyzed data.

[0162] 3. Cloud Server:

[0163] Cloud infrastructure: Stores, analyzes, and serves data.

[0164] software

[0165] 1. Means of transfer:

[0166] A communications protocol for transferring visual data to a cloud server via Wi-Fi or mobile data communications.

[0167] 2. Analysis method:

[0168] OpenCV: Used for visual data capture and image processing.

[0169] AWS® S3: A storage service for storing captured visual data in the cloud.

[0170] AWS Rekognition: A service that analyzes image data in the cloud and performs labeling and classification.

[0171] 3. Driving assistance measures:

[0172] An interface that provides real-time safety information and warnings to drivers, based on data analyzed on a cloud server.

[0173] 4. Storage Method:

[0174] Use AWS S3 or other cloud storage services to store the analyzed data and make it easily accessible to users.

[0175] 5. Search methods:

[0176] A function that allows users to search and play back past visual data by keyword or condition, using natural language processing (NLP) technology.

[0177] Specific examples

[0178] Specific use cases include the following scenarios:

[0179] Self-driving vehicles in operation

[0180] 1. Data Collection:

[0181] While the autonomous vehicle is driving, a camera mounted on the vehicle captures visual data of the area ahead in real time, while a microphone also collects environmental sounds.

[0182] 2. Data Transfer:

[0183] The collected visual and audio data is transferred to a cloud server using Wi-Fi or mobile data.

[0184] 3. Data Analysis:

[0185] Once the visual data reaches the cloud server, it is analyzed using AWS Rekognition, which includes object detection and labeling, extracting information such as "pedestrians" and "traffic lights."

[0186] 4. Driving assistance:

[0187] Based on the analyzed data, the driver assistance system provides real-time warnings and guidance, for example, a warning that "there is a pedestrian ahead" is displayed to the driver.

[0188] 5. Data Storage and Retrieval:

[0189] The analyzed and classified visual data is stored in cloud storage such as AWS S3 so that it can be searched and played back later. When the user inputs a request such as "play back past driving data," the relevant data is searched for and played back from the cloud server.

[0190] Prompt Sentence Examples

[0191] As a concrete example of a prompt sentence, for example, enter the following prompt sentence for Amazon Rekognition:

[0192] "Analyze uploaded autonomous vehicle images to identify pedestrians, vehicles, traffic lights, and road signs. Label them and provide a confidence score."

[0193] The above system configuration and processing enable autonomous vehicles to efficiently collect and analyze visual data, provide driving assistance in real time, and effectively search and replay past driving data.

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1: Capture visual data

[0196] A camera mounted on an autonomous vehicle captures visual data of the road ahead in real time. Specifically, the camera captures images frame by frame and temporarily stores them in memory. The input is the video frame acquired from the camera, and the output is the visual data stored in memory.

[0197] Step 2: Collecting audio data

[0198] The microphone collects environmental sounds and audio data from inside the vehicle. The microphone captures audio data in real time and temporarily stores it in memory. The audio data obtained from the microphone is the input, and the audio data stored in memory is generated as the output.

[0199] Step 3: Data Transfer

[0200] The acquired visual and audio data is transferred to a cloud server via Wi-Fi or mobile data communication. The input is the visual and audio data stored in the memory, and the output is the data transmitted to the cloud server. Specifically, the transmission means packetizes the data and transmits it to a specific address on the cloud server via the Internet.

[0201] Step 4: Data analysis

[0202] The cloud server analyzes the received visual and audio data. Specifically, it is stored in AWS S3 and analyzed by AWS Rekognition. The input is the visual and audio data stored on the cloud server, and the output is the analysis results. AWS generative AI models are used to detect, tag, and classify objects in the images.

[0203] Step 5: Generate driver assistance information

[0204] The cloud server generates driving assistance information based on the analysis results. The input is the analysis results of AWS Rekognition, and the output is the driving assistance information. Specifically, warning messages such as "There is a pedestrian ahead" or "The traffic light is red" are created.

[0205] Step 6: Providing driver assistance information

[0206] The cloud server generates driving assistance information and sends it to the vehicle's driving assistance system. The input is the driving assistance information generated by the cloud server, and the output is data provided to the driving assistance system. The driving assistance system displays this information to the driver in real time and provides warnings and guidance.

[0207] Step 7: Save Data

[0208] The analyzed and classified visual data is stored in cloud storage (AWS S3). The analyzed visual data is the input, and the data stored in cloud storage is generated as the output. This allows past driving data to be saved and later searched and played back.

[0209] Step 8: Data Retrieval

[0210] A user uses a smartphone application to search for past driving data. The input is keywords and conditions entered by the user, and the output is visual data as search results. Specifically, the cloud server searches the stored data based on the user's request and sends the relevant data to the user's device.

[0211] Step 9: Data playback

[0212] The user device plays back the visual data received from the cloud server. The input is the visual data sent from the cloud server, and the output is the video and audio displayed on the user device. The user can visually check past driving data on their smartphone or computer.

[0213] The above processing steps realize a system that collects and analyzes visual data in real time and provides driving assistance information. It also makes it possible to effectively search and replay past data.

[0214] 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.

[0215] ---

[0216] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0217] Visual Devices

[0218] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0219] Cloud Server and Generative AI

[0220] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0221] Emotion Engine

[0222] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[0223] User terminal

[0224] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[0225] Specific examples

[0226] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0227] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[0228] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0229] Additionally, if a user experiences a moment of stress, that information is also tagged by the emotion engine. When a user requests a "relaxing scene" to relax, the generative AI will recommend appropriate visual data based on the emotion engine's data, providing a visual experience to reduce stress. Furthermore, if a visually impaired person is wearing a visual device, the generative AI will analyze the captured visual data and convert it into audio data. This audio data is sent to the user's audio output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device will capture visual information about the platform and output that information as audio, such as, "There are stairs on your right."

[0230] As such, this invention is a system that effectively records, organizes, and plays back visual information, providing an environment where users can easily manage and use visual data in a variety of usage scenarios.The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, realizing a more personalized experience.

[0231] ---

[0232] The processing flow will be explained below.

[0233] ---

[0234] Step 1:

[0235] The user wears the visual device, which begins capturing the user's visual information in real time. The visual device is equipped with a camera and audio sensor, which simultaneously captures video and audio data.

[0236] Step 2:

[0237] The device temporarily stores the visual data captured from the visual device in its internal memory, along with the date, time, and location information.

[0238] Step 3:

[0239] The device performs preprocessing on the data sent from the vision device, removing noise if necessary, thereby improving the quality of the vision data.

[0240] Step 4:

[0241] The device collects visual data as well as emotional data from the user, which is then recognized in real time by an emotion engine based on the user's facial expressions, voice, and brainwaves.

[0242] Step 5:

[0243] The device transfers visual and emotional data together to a cloud server, where the data is encrypted before being sent.

[0244] Step 6:

[0245] The server analyzes the received visual and emotional data, and the generation AI divides the visual data into frames and evaluates their relevance to the emotional data.

[0246] Step 7:

[0247] The server's generation AI evaluates the importance and interest of the visual data and tags it in conjunction with the emotional data. For example, tags such as "emotion," "surprise," and "joy" can be added.

[0248] Step 8:

[0249] The server stores the analyzed and tagged visual data in a storage means, including the user's emotional state.

[0250] Step 9:

[0251] A user requests specific visual data through a smartphone app, for example, a "relaxing landscape."

[0252] Step 10:

[0253] The server receives the user's request, and the generation AI searches for the most suitable visual data based on the emotion engine data. The search results are filtered based on the user's emotional state.

[0254] Step 11:

[0255] The server provides the search results to the user's device in streaming or download format, and the user plays the visual data on their visual device or smartphone.

[0256] Step 12:

[0257] When a visually impaired person uses a visual device, the device captures visual information in real time and transmits it to a cloud server, which then analyzes the visual data and converts it into audio data.

[0258] Step 13:

[0259] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[0260] ---

[0261] The above is the specific processing flow when combining the "Vision Bank" system with an emotion engine.

[0262] Example 2

[0263] 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."

[0264] Conventional visual data management systems have difficulty efficiently recording, organizing, and replaying the visual information users acquire on a daily basis. Furthermore, they lack the ability to classify and recommend visual data based on the user's emotional state, making it difficult to provide a personalized experience. Furthermore, they lack a means for visually impaired people to intuitively understand visual data.

[0265] 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 a visual device means for capturing visual data, a transfer means for transferring the visual data transmitted from the visual device to a cloud server, an analysis means for analyzing and classifying the visual data on the cloud server using a generative AI, an emotion recognition means for associating emotion data with the analyzed and classified visual data, and a storage means for storing and providing the analyzed and classified visual data in a form easily accessible to the user. This enables efficient recording, organization, and playback of the user's visual information, and realizes the provision of personalized visual data based on the user's emotional state. Furthermore, the captured visual data can be converted into audio data to provide intuitively understandable information to visually impaired people.

[0266] "Visual data" refers to information such as images and environmental sounds that come into the user's field of vision.

[0267] "Vision device" refers to a device worn by a user that captures visual data.

[0268] "Transfer means" refers to a method or apparatus for transferring visual data sent from a visual device to a cloud server.

[0269] A "cloud server" refers to a server that stores and manages data on the Internet.

[0270] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze and classify data.

[0271] "Analysis Means" means a method or apparatus for analyzing and classifying visual data using generative AI.

[0272] "Emotion recognition means" refers to a method or apparatus for obtaining and associating user emotion data with analyzed visual data.

[0273] "Storage means" refers to a method or device for storing the analyzed and categorized visual data and making it easily accessible to the user.

[0274] "Search means" refers to a method or device for searching the analyzed and classified visual data based on a user's keywords and providing related visual data.

[0275] "Recommendation means" refers to a method or device that recommends appropriate visual data based on the emotional state of a user.

[0276] "Audio conversion means" refers to a method or device that analyzes captured visual data and converts it into audio data.

[0277] "Audio output means" refers to a method or device for outputting audio data in real time.

[0278] This invention is a system for efficiently recording, organizing, and replaying visual data, and in particular, for providing personalized visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0279] Visual Devices

[0280] The user wears a visual device. The visual device has the function of capturing visual information that comes into the user's field of vision in real time. Specifically, this includes eyeglass-type and contact lens-type devices. The visual device collects visual information and environmental sounds and temporarily stores them in its internal memory. This data is then transmitted to the terminal via wireless communication (Bluetooth or Wi-Fi).

[0281] Cloud Server and Generative AI

[0282] The terminal transfers the visual data sent from the visual device to a cloud server. Upon receiving the visual data, the cloud server analyzes the data using a generative AI. The generative AI uses a widely used artificial intelligence model (e.g., GPT-4 (registered trademark) by OpenAI (registered trademark)). The generative AI divides the visual data into frames and runs an algorithm to evaluate the user's interest and importance. Through this analysis, the data is effectively classified and assigned tags such as "tourist spot," "scenery," and "signpost." The analyzed data is stored in a cloud storage means (e.g., Amazon S3) for easy access by users.

[0283] Emotion Engine

[0284] The emotion engine acquires the user's emotional data in real time and associates it with the analyzed visual data. The emotion engine uses technology (e.g., Affectiva's emotion recognition API) that analyzes the user's facial expressions, voice, and brain waves. This allows the visual data to be tagged with emotion tags such as "emotion" or "stress."

[0285] User terminal

[0286] A user device (e.g., a smartphone or computer) provides an interface for accessing visual data stored in the cloud server. By requesting specific visual data (e.g., "scenery of a tourist spot"), the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data is recommended based on the emotions recognized by the emotion engine.

[0287] Specific examples

[0288] When a user visits a tourist spot, the visual device captures the scenery and guide signs. This data is transferred to a cloud server via the smartphone, where the generation AI analyzes the data and assigns tags such as "tourist spot" and "scenery." For example, if a user experiences a "moving moment," the data is tagged "moving." When the user requests in the app to "play the scenery of the tourist spot I saw today," the cloud server searches for the relevant data and plays it on the smartphone.

[0289] Furthermore, if a user requests a "relaxing scene" when a tag has been added to a moment of stress, the generative AI will recommend and provide appropriate visual data. Furthermore, if a visually impaired person is wearing a visual device, the captured visual data is converted into audio data, and audio guidance is provided in real time. For example, information is provided in the form of "There are stairs on your right."

[0290] Prompt Sentence Examples

[0291] "Today I went to a tourist spot. Organize the data of the scenery and tag the moments that moved me."

[0292] "Please recommend a relaxing landscape."

[0293] In this way, the Vision Bank system effectively records, organizes, and plays back visual information, providing users with a personalized visual experience. The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, making it effective for a wide range of scenarios in daily life.

[0294] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0295] Step 1:

[0296] A visual device captures video and audio data.

[0297] A visual device worn by the user captures surrounding video and audio data in real time. The visual device's camera and microphone acquire video frames and audio data as input data. These data are temporarily stored in the visual device's internal memory. Specific operations include capturing video at 30 frames per second and recording environmental sounds in stereo.

[0298] Step 2:

[0299] The device transfers the captured data to a cloud server.

[0300] The data stored in the internal memory of the visual device is retrieved and transferred to a terminal (e.g., a smartphone). The input data, which the terminal transfers using Bluetooth or Wi-Fi, is the video and audio data received from the visual device. The terminal receives this data and uploads it to a cloud server using the HTTPS protocol. Specific operations include data encryption and secure transfer.

[0301] Step 3:

[0302] The cloud server uses generated AI to analyze and classify the data.

[0303] The cloud server receives video and audio data as input, and uses a generative AI (e.g., GPT-4 model) to analyze and classify the data frame by frame. Specifically, the video data is first analyzed using an image recognition algorithm (e.g., YOLOv5) to identify important objects and scenes. The generative AI then uses natural language processing to assign appropriate tags (e.g., "tourist spot," "scenery," "signpost") to the identified objects and scenes. The output data is the analyzed and classified visual data and its tags.

[0304] Step 4:

[0305] The cloud server associates the emotion data using an emotion engine.

[0306] The emotion engine acquires the user's facial expressions, voice, and brainwave data in real time and recognizes emotional information as input data. The cloud server associates this with visual data. Specifically, the user's emotions (e.g., "emotion" or "surprise") are added as tags from information acquired using Affectiva's emotion recognition API. The output data includes visual data tagged with emotions.

[0307] Step 5:

[0308] The cloud server stores the analyzed and classified data in storage.

[0309] The cloud server stores the analyzed and classified visual data and its tags in a storage medium (e.g., Amazon S3). The input data is the analyzed and classified visual data and its tags, and measures are taken to store it safely and efficiently. Specific actions include data redundancy and backup.

[0310] Step 6:

[0311] A user accesses the visual data using a terminal.

[0312] A user launches a smartphone app and sends a request to the cloud server to request specific visual data. For example, they input a prompt such as "Play the scenery of the tourist spots I saw today." The input data includes the user's search keywords and emotional state. Based on this, the cloud server searches for relevant visual data and outputs it to the user's device in the form of streaming playback. Specific operations include filtering the search results and selecting appropriate data.

[0313] Step 7:

[0314] The cloud server makes recommendations based on the user's emotional state.

[0315] Based on the user's emotional state obtained by the emotion engine, the generative AI recommends appropriate visual data. For example, if a user requests a "relaxing landscape," past emotional data is referenced and visual data that will reduce stress is selected. The input data includes the user's emotional history and current emotional state, and the recommended visual data is provided as output data. Specific operations include searching the user's history and applying the recommendation algorithm.

[0316] In this way, the Vision Bank system effectively records, analyzes and plays back users' visual information, providing a personalized visual experience based on their emotional state.

[0317] (Application example 2)

[0318] 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."

[0319] Today, there is a need for efficient digitization and easy access to the information and visual experiences users see on a daily basis. However, simply storing visual data makes it difficult for users to replay emotionally significant visual experiences or use them effectively at the appropriate time. Furthermore, there are insufficient means for visually impaired people to access visual information as audio data in real time. Providing personalized visual information based on the user's emotional state would provide a richer user experience, but this is currently difficult to achieve.

[0320] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI, a storage means that stores and provides the analyzed and classified visual data in a form easily accessible to the user, an emotion recognition means that recognizes the user's emotional data in real time and associates it with the visual data, and a recommendation means that recommends related visual data based on the user's request and emotional state. This allows users to efficiently manage their daily visual experiences and use optimal visual data and content according to their emotional state. Furthermore, visual information captured in real time can be provided as audio data to visually impaired people, making the use of visual information more effective.

[0321] A "vision device" is a device worn by a user to capture visual data.

[0322] "Transfer means" refers to a means for transmitting visual data from a visual device to a cloud server.

[0323] A "cloud server" is a server that stores and manages data over the Internet.

[0324] "Generative AI" is an artificial intelligence technique for analyzing and classifying visual data.

[0325] "Analysis means" refers to a means for analyzing and classifying visual data on a cloud server using a generative AI.

[0326] "Storage means" means for storing and providing analyzed and classified visual data in a form that is easily accessible to the user.

[0327] The "emotion recognition means" is a means for recognizing the user's emotion data in real time and associating it with visual data.

[0328] A "recommendation means" is a means for recommending relevant visual data based on a user's request and emotional state.

[0329] The "search means" is a means for searching the analyzed and classified visual data based on the user's keywords and providing the data to the user terminal.

[0330] The "audio conversion means" is a means for analyzing visual data captured by a visually impaired person and converting it into audio data.

[0331] The "audio output means" is a means for outputting audio data in real time.

[0332] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0333] Visual Devices

[0334] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0335] Cloud Server and Generative AI

[0336] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0337] Emotion Engine

[0338] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[0339] User terminal

[0340] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[0341] Specific examples

[0342] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0343] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[0344] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0345] Additionally, if the user experiences a moment of stress, that information will also be tagged by the emotion engine. If the user requests a "relaxing scene" when they want to relax, the generative AI will recommend appropriate visual data based on the emotion engine data, providing a visual experience that will reduce stress.

[0346] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[0347] Example prompts to input to a generative AI model:

[0348] Which beach videos would you recommend if a user is feeling relaxed?

[0349] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0350] Step 1:

[0351] The user wears a visual device, which captures visual data from the surroundings. The input is camera video and audio data, and the output is raw video and audio data acquired in real time. The visual device temporarily stores these data in its internal memory.

[0352] Step 2:

[0353] The visual and audio data captured by the vision device is sent to the cloud server. The input is the captured visual and audio data, and the output is a notification of the completion of data transfer to the cloud server. The device uses the data transfer function to upload the stored data to the cloud server.

[0354] Step 3:

[0355] The cloud server analyzes the received visual data using a generative AI model. The input is the visual data transferred to the cloud server, and the output is the analyzed and frame-classified data. The generative AI model divides the data into frames, evaluates the user's interest and importance, and assigns appropriate tags.

[0356] Step 4:

[0357] The analyzed and classified data is stored in a storage on a cloud server. The input is the analyzed and tagged visual data, and the output is a notification that the data has been saved to the cloud storage. The cloud server uses a storage means to save the data so that the user can access it later.

[0358] Step 5:

[0359] The emotion engine recognizes emotion data in real time from the user's facial expressions, voice, brain waves, etc. The input is the user's facial expressions, voice, and brain wave data, and the output is the recognized emotion data. The emotion engine determines the user's emotional state from this data and associates it with the visual data.

[0360] Step 6:

[0361] A user requests specific visual data through a smartphone app. The input is the user's search query or keywords, and the output is the provision of related visual data to the user's smartphone device. The user uses the app to input keywords such as "tourist scenery" or "relaxing scenery."

[0362] Step 7:

[0363] The cloud server recommends relevant visual data based on the user's emotional state and request. The input is a search query and emotional data, and the output is a list of recommended visual data. The server uses the recommendation mechanism to select the most suitable visual data according to the user's emotions and interests.

[0364] Step 8:

[0365] The recommended visual data is streamed to the user's smartphone. The input is the recommended visual data, and the output is the playback video of the visual data on the smartphone. The user can view the visual data in the app and enjoy the played video and audio.

[0366] 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.

[0367] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0368] 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.

[0369] [Second embodiment]

[0370] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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).

[0376] 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. 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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."

[0382] ---

[0383] This invention is a system for efficiently recording, organizing, and reproducing visual data, which digitizes the information and visual experiences that users see on a daily basis and provides them in an easily accessible form. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, and a user terminal.

[0384] Visual Devices

[0385] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0386] Cloud Server and Generative AI

[0387] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0388] User terminal

[0389] A user device (such as a smartphone or computer) provides an interface for accessing the visual data stored in the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for relevant visual data and provides it to the user device.

[0390] Specific examples

[0391] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0392] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0393] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[0394] As described above, the present invention is a system that effectively records, organizes, and plays back visual information, providing an environment in which users can easily manage and use visual data in a variety of usage scenarios.

[0395] ---

[0396] The processing flow will be explained below.

[0397] ---

[0398] Step 1:

[0399] The user wears the visual device, which begins capturing the user's visual information in real time.

[0400] Step 2:

[0401] The device temporarily stores the visual data captured from the vision device in its internal memory, where the captured visual data is preprocessed and noise is removed.

[0402] Step 3:

[0403] The device periodically generates packets of visual information, encrypts them, and transmits them to a cloud server, where the user's data is stored securely.

[0404] Step 4:

[0405] The server receives packets of visual data, which are then analyzed by the generative AI and evaluated for importance and interest.

[0406] Step 5:

[0407] The generative AI in the server segments the visual data frame by frame and identifies key scenes based on the user's intent and the environment, effectively classifying the data.

[0408] Step 6:

[0409] The server tags and classifies the data based on the analysis results and stores it in a format that is easily accessible to users.

[0410] Step 7:

[0411] A user requests specific visual data through a smartphone app, for example by entering keywords such as "scenery of a tourist spot."

[0412] Step 8:

[0413] The server receives the user's request and searches for relevant visual data based on tags and classification information, and the search results are filtered appropriately.

[0414] Step 9:

[0415] The server provides the retrieved visual data to the user's device in streaming or download format, allowing the user to replay the visual data on their smartphone and relive past visual experiences.

[0416] Step 10:

[0417] When a visually impaired person uses a visual device, the visual device captures and transmits the data in real time to a cloud server, where the visual information is analyzed and converted into audio data.

[0418] Step 11:

[0419] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[0420] Step 12:

[0421] When a user encounters an incident or accident, the vision device automatically initiates high-definition recording, which is then immediately sent to a cloud server for tagging and classification.

[0422] Step 13:

[0423] The server stores important incident and accident data, and upon request from law enforcement agencies, provides the data with necessary permission, thereby ensuring that information that is useful to society is used effectively.

[0424] ---

[0425] This completes the detailed step-by-step explanation of the programming process flow of the "Vision Bank" system.

[0426] Example 1

[0427] 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."

[0428] Modern society requires efficient recording, organization, and playback of visual data, but existing technologies make this process cumbersome and often provide insufficient support, particularly for people with visual impairments. Furthermore, there are limited methods for managing vast amounts of visual data and quickly accessing them when needed.

[0429] 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.

[0430] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a terminal, a transmission means that uploads the visual data from the terminal to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI model, and a storage means that tags the analyzed and classified visual data, stores it in storage, and provides it to the visually impaired. This enables efficient recording, organization, and playback of visual data, and provides support to the visually impaired.

[0431] "Visual data" refers to the video and environmental sound data captured by a visual device.

[0432] A "vision device" is a device worn by a user that captures visual data in real time.

[0433] The "transfer means" is a function for transferring the visual data captured by the visual device to the terminal.

[0434] The "transmission means" is a function that allows the terminal to upload visual data to the cloud server.

[0435] A "cloud server" is a remote server for analyzing, categorizing, and storing visual data.

[0436] A "generative AI model" is an artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[0437] "Analysis means" refers to the functionality of the cloud server to analyze and classify visual data using the generated AI model.

[0438] "Tagging" is the process of assigning tags to analyzed and classified visual data for search and identification purposes.

[0439] The "storage means" is a function for storing tagged visual data in a storage and providing it in a form that can be accessed by the user.

[0440] The "search means" is a function for searching the analyzed and classified visual data based on the user's prompt sentence and providing it to the user terminal.

[0441] The "audio conversion means" is a function that analyzes the visual data captured by the visual device and converts it into audio data.

[0442] The "audio output means" is a function that outputs the converted audio data as audio in real time.

[0443] A "prompt sentence" is a linguistic instruction that a user inputs from their device when searching for or requesting visual data from a cloud server.

[0444] This invention is a system for effectively recording, organizing and reproducing visual data, and has a wide range of applications, particularly in supporting the visually impaired. The processing of the program will now be described in detail.

[0445] Hardware and software used

[0446] Hardware

[0447] Vision devices: Eyeglasses or contact lenses worn by the user that capture visual data (video and audio) in real time.

[0448] Terminal: A smartphone or tablet operated by the user. It receives data from the visual device and sends it to the cloud server.

[0449] Cloud Server: A remote server that performs data analysis, classification, and storage.

[0450] software

[0451] Generative AI model: An artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[0452] Data capture with visual devices

[0453] The user wears a visual device that captures the user's field of vision and the surrounding audio in real time and stores this data in a built-in temporary memory. For example, when the user visits a tourist spot, the visual device continuously records the scenery and guide signs.

[0454] Transferring data to your device

[0455] The captured visual data is then transferred to the device via Wi-Fi or Bluetooth. For example, when a user enters a cafe and connects to Wi-Fi, the data is automatically transferred to the device.

[0456] Data reception and analysis by cloud server

[0457] The device uploads the received visual data to a cloud server, which then feeds the data to a generative AI model for analysis such as scene recognition, object detection, and voice recognition. For example, the generative AI model can identify tourist attractions and important signs in the video and extract relevant information.

[0458] Data storage and tagging

[0459] The analyzed and classified visual data is then tagged and stored in cloud server storage. This process makes the data easier to search and allows users to easily access the information they need. For example, data on tourist spots is tagged with "scenery," "famous places," and "signs."

[0460] Data request by user device

[0461] The user starts the smartphone app and inputs a prompt to send a data request to the cloud server. For example, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today."

[0462] Data search and provision via cloud servers

[0463] The cloud server receives the user's request, searches for relevant data from the tagged database, and once it finds the relevant data, it sends it to the user's device. For example, landscape data captured at a tourist spot is searched and provided to the user's smartphone.

[0464] Data playback on user terminal

[0465] The user's smartphone receives the data sent from the cloud server and plays the visual data through the app, allowing the user to re-experience the scenery and guide they saw at the tourist spot. For example, the scenery of the tourist spot is played on the smartphone screen, allowing the user to recreate the experience as if they were actually there.

[0466] Prompt Sentence Examples

[0467] "Play the photos I took today"

[0468] "Search for scenery of tourist spots"

[0469] "Can you give me directions to a nearby restaurant?"

[0470] In this way, the present invention realizes efficient recording, organization, and playback of visual data, providing an environment in which visual data can be easily managed and used in various usage scenarios, and can provide useful support especially for the visually impaired.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] The user wears a visual device, which captures video and audio data in real time. The input is the video and environmental sounds that the user sees, which the visual device captures and temporarily stores in its internal memory. Specific operations include the continuous recording of video and audio by the camera and microphone built into the visual device.

[0474] Step 2:

[0475] The data captured by the vision device is transmitted to the terminal via Wi-Fi or Bluetooth. The input is the captured data in the vision device, which is received by the receiving module of the terminal. The specific operation includes the vision device transmitting the data to the terminal via Wi-Fi or Bluetooth. The output is the visual data transmitted to the terminal.

[0476] Step 3:

[0477] The visual data received by the device is uploaded to the cloud server. The input is the data received by the device, which is then sent to the cloud server. The specific operation involves the device uploading the visual data to the cloud server through an Internet connection. The output is the visual data stored on the cloud server.

[0478] Step 4:

[0479] The cloud server receives visual data and analyzes it using a generative AI model. The input is visual data stored on the cloud server, which the generative AI model analyzes frame by frame. Specific data processing includes scene recognition, object detection, and voice recognition. The output is analyzed and classified data.

[0480] Step 5:

[0481] The cloud server assigns appropriate tags to the analyzed and classified data and stores it in storage. The input is the analyzed and classified visual data, which is tagged with specific tags. Specific operations include the tagging and storage process in a database. The output is the tagged visual data stored in the database.

[0482] Step 6:

[0483] The user inputs a prompt via their device to request data from the cloud server. The input is the prompt entered by the user, which is sent to the cloud server by a dedicated app. In concrete terms, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today" into the app's search bar. The output is a request to the cloud server.

[0484] Step 7:

[0485] The cloud server searches for relevant data based on the prompt sentence and provides it to the user's device. The input is a database tagged with the prompt sentence, and the cloud server searches for data based on this. The specific data calculation involves evaluating the degree of match with the prompt sentence and extracting highly relevant data. The output is visual data sent to the user's device.

[0486] Step 8:

[0487] The user device receives the data sent from the cloud server and plays it through the app. The input is visual data sent from the cloud server, which the user views on their smartphone or tablet. Specific operations include the app streaming the received data. The output is visual data displayed on the user device.

[0488] (Application example 1)

[0489] 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."

[0490] Conventional autonomous vehicles lack a mechanism for efficiently collecting visual information from the surroundings, analyzing and classifying it in real time, and providing driving assistance information. This has resulted in insufficient information provision to improve driving safety and efficiency. Furthermore, there is also a lack of effective means for replaying and searching past driving data, making it difficult to recreate accidents or review driving conditions.

[0491] 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.

[0492] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generation AI, a driving assistance means that provides the analyzed visual data as driving assistance information, and a storage means that stores and provides the analyzed and classified visual data in a form that is easily accessible to users. This enables autonomous vehicles to collect and analyze visual information in real time and provide driving assistance information that improves safety and efficiency. Furthermore, the ability to effectively play back and search past driving data makes it easy to recreate accidents and review driving conditions.

[0493] "Visual data" is digital data that includes the user's visual experience and surrounding image information.

[0494] A "visual device" is a device worn by a user that captures visual data, and refers to a device that includes a camera, sensor, etc.

[0495] "Transfer means" refers to a communication means for transferring visual data acquired from a visual device to a cloud server, and includes wired communication and wireless communication.

[0496] A "cloud server" is a server system for storing, processing, and providing data via the Internet.

[0497] "Analysis means" refers to the function that analyzes visual data on a cloud server using AI to generate and classify and tag it.

[0498] "Generative AI" refers to programs and algorithms that use artificial intelligence technology to generate and analyze digital data.

[0499] "Driving assistance means" refers to functions that provide information and warnings necessary for driving based on analyzed visual data, improving safety and efficiency.

[0500] "Storage Means" refers to a storage device or system for storing analyzed and classified visual data and providing it in a form that is easily accessible to users.

[0501] "Search means" refers to a function that allows users to search for keywords or find and provide visual data based on specific conditions.

[0502] "Display means" refers to a device or screen that displays driving assistance information to the driver in real time based on analyzed visual data.

[0503] In implementing this invention, a system consisting of the following elements is used to enable the collection, analysis, driving assistance, and playback / search of visual data.

[0504] Hardware

[0505] 1. Visual devices:

[0506] Vehicle-mounted cameras: Capture visual data in real time.

[0507] Microphone: Collects audio data.

[0508] 2. User Device:

[0509] Smartphone or computer: To access and view the analyzed data.

[0510] 3. Cloud Server:

[0511] Cloud infrastructure: Stores, analyzes, and serves data.

[0512] software

[0513] 1. Means of transfer:

[0514] A communications protocol for transferring visual data to a cloud server via Wi-Fi or mobile data communications.

[0515] 2. Analysis method:

[0516] OpenCV: Used for visual data capture and image processing.

[0517] AWS S3: A storage service for storing captured visual data in the cloud.

[0518] AWS Rekognition: A service that analyzes image data in the cloud and performs labeling and classification.

[0519] 3. Driving assistance measures:

[0520] An interface that provides real-time safety information and warnings to drivers, based on data analyzed on a cloud server.

[0521] 4. Storage Method:

[0522] Use AWS S3 or other cloud storage services to store the analyzed data and make it easily accessible to users.

[0523] 5. Search methods:

[0524] A function that allows users to search and play back past visual data by keyword or condition, using natural language processing (NLP) technology.

[0525] Specific examples

[0526] Specific use cases include the following scenarios:

[0527] Self-driving vehicles in operation

[0528] 1. Data Collection:

[0529] While the autonomous vehicle is driving, a camera mounted on the vehicle captures visual data of the area ahead in real time, while a microphone also collects environmental sounds.

[0530] 2. Data Transfer:

[0531] The collected visual and audio data is transferred to a cloud server using Wi-Fi or mobile data.

[0532] 3. Data Analysis:

[0533] Once the visual data reaches the cloud server, it is analyzed using AWS Rekognition, which includes object detection and labeling, extracting information such as "pedestrians" and "traffic lights."

[0534] 4. Driving assistance:

[0535] Based on the analyzed data, the driver assistance system provides real-time warnings and guidance, for example, a warning that "there is a pedestrian ahead" is displayed to the driver.

[0536] 5. Data Storage and Retrieval:

[0537] The analyzed and classified visual data is stored in cloud storage such as AWS S3 so that it can be searched and played back later. When the user inputs a request such as "play back past driving data," the relevant data is searched for and played back from the cloud server.

[0538] Prompt Sentence Examples

[0539] As a concrete example of a prompt sentence, for example, enter the following prompt sentence for Amazon Rekognition:

[0540] "Analyze uploaded autonomous vehicle images to identify pedestrians, vehicles, traffic lights, and road signs. Label them and provide a confidence score."

[0541] The above system configuration and processing enable autonomous vehicles to efficiently collect and analyze visual data, provide driving assistance in real time, and effectively search and replay past driving data.

[0542] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0543] Step 1: Capture visual data

[0544] A camera mounted on an autonomous vehicle captures visual data of the road ahead in real time. Specifically, the camera captures images frame by frame and temporarily stores them in memory. The input is the video frame acquired from the camera, and the output is the visual data stored in memory.

[0545] Step 2: Collecting audio data

[0546] The microphone collects environmental sounds and audio data from inside the vehicle. The microphone captures audio data in real time and temporarily stores it in memory. The audio data obtained from the microphone is the input, and the audio data stored in memory is generated as the output.

[0547] Step 3: Data Transfer

[0548] The acquired visual and audio data is transferred to a cloud server via Wi-Fi or mobile data communication. The input is the visual and audio data stored in the memory, and the output is the data transmitted to the cloud server. Specifically, the transmission means packetizes the data and transmits it to a specific address on the cloud server via the Internet.

[0549] Step 4: Data analysis

[0550] The cloud server analyzes the received visual and audio data. Specifically, it is stored in AWS S3 and analyzed by AWS Rekognition. The input is the visual and audio data stored on the cloud server, and the output is the analysis results. AWS generative AI models are used to detect, tag, and classify objects in the images.

[0551] Step 5: Generate driver assistance information

[0552] The cloud server generates driving assistance information based on the analysis results. The input is the analysis results of AWS Rekognition, and the output is the driving assistance information. Specifically, warning messages such as "There is a pedestrian ahead" or "The traffic light is red" are created.

[0553] Step 6: Providing driver assistance information

[0554] The cloud server generates driving assistance information and sends it to the vehicle's driving assistance system. The input is the driving assistance information generated by the cloud server, and the output is data provided to the driving assistance system. The driving assistance system displays this information to the driver in real time and provides warnings and guidance.

[0555] Step 7: Save Data

[0556] The analyzed and classified visual data is stored in cloud storage (AWS S3). The analyzed visual data is the input, and the data stored in cloud storage is generated as the output. This allows past driving data to be saved and later searched and played back.

[0557] Step 8: Data Retrieval

[0558] A user uses a smartphone application to search for past driving data. The input is keywords and conditions entered by the user, and the output is visual data as search results. Specifically, the cloud server searches the stored data based on the user's request and sends the relevant data to the user's device.

[0559] Step 9: Data playback

[0560] The user device plays back the visual data received from the cloud server. The input is the visual data sent from the cloud server, and the output is the video and audio displayed on the user device. The user can visually check past driving data on their smartphone or computer.

[0561] The above processing steps realize a system that collects and analyzes visual data in real time and provides driving assistance information. It also makes it possible to effectively search and replay past data.

[0562] 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.

[0563] ---

[0564] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0565] Visual Devices

[0566] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0567] Cloud Server and Generative AI

[0568] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0569] Emotion Engine

[0570] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[0571] User terminal

[0572] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[0573] Specific examples

[0574] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0575] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[0576] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0577] Additionally, if a user experiences a moment of stress, that information is also tagged by the emotion engine. When a user requests a "relaxing scene" to relax, the generative AI will recommend appropriate visual data based on the emotion engine's data, providing a visual experience to reduce stress. Furthermore, if a visually impaired person is wearing a visual device, the generative AI will analyze the captured visual data and convert it into audio data. This audio data is sent to the user's audio output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device will capture visual information about the platform and output that information as audio, such as, "There are stairs on your right."

[0578] As such, this invention is a system that effectively records, organizes, and plays back visual information, providing an environment where users can easily manage and use visual data in a variety of usage scenarios.The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, realizing a more personalized experience.

[0579] ---

[0580] The processing flow will be explained below.

[0581] ---

[0582] Step 1:

[0583] The user wears the visual device, which begins capturing the user's visual information in real time. The visual device is equipped with a camera and audio sensor, which simultaneously captures video and audio data.

[0584] Step 2:

[0585] The device temporarily stores the visual data captured from the visual device in its internal memory, along with the date, time, and location information.

[0586] Step 3:

[0587] The device performs preprocessing on the data sent from the vision device, removing noise if necessary, thereby improving the quality of the vision data.

[0588] Step 4:

[0589] The device collects visual data as well as emotional data from the user, which is then recognized in real time by an emotion engine based on the user's facial expressions, voice, and brainwaves.

[0590] Step 5:

[0591] The device transfers visual and emotional data together to a cloud server, where the data is encrypted before being sent.

[0592] Step 6:

[0593] The server analyzes the received visual and emotional data, and the generation AI divides the visual data into frames and evaluates their relevance to the emotional data.

[0594] Step 7:

[0595] The server's generation AI evaluates the importance and interest of the visual data and tags it in conjunction with the emotional data. For example, tags such as "emotion," "surprise," and "joy" can be added.

[0596] Step 8:

[0597] The server stores the analyzed and tagged visual data in a storage means, including the user's emotional state.

[0598] Step 9:

[0599] A user requests specific visual data through a smartphone app, for example, a "relaxing landscape."

[0600] Step 10:

[0601] The server receives the user's request, and the generation AI searches for the most suitable visual data based on the emotion engine data. The search results are filtered based on the user's emotional state.

[0602] Step 11:

[0603] The server provides the search results to the user's device in streaming or download format, and the user plays the visual data on their visual device or smartphone.

[0604] Step 12:

[0605] When a visually impaired person uses a visual device, the device captures visual information in real time and transmits it to a cloud server, which then analyzes the visual data and converts it into audio data.

[0606] Step 13:

[0607] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[0608] ---

[0609] The above is the specific processing flow when combining the "Vision Bank" system with an emotion engine.

[0610] Example 2

[0611] 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."

[0612] Conventional visual data management systems have difficulty efficiently recording, organizing, and replaying the visual information users acquire on a daily basis. Furthermore, they lack the ability to classify and recommend visual data based on the user's emotional state, making it difficult to provide a personalized experience. Furthermore, they lack a means for visually impaired people to intuitively understand visual data.

[0613] 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 a visual device means for capturing visual data, a transfer means for transferring the visual data transmitted from the visual device to a cloud server, an analysis means for analyzing and classifying the visual data on the cloud server using a generative AI, an emotion recognition means for associating emotion data with the analyzed and classified visual data, and a storage means for storing and providing the analyzed and classified visual data in a form easily accessible to the user. This enables efficient recording, organization, and playback of the user's visual information, and realizes the provision of personalized visual data based on the user's emotional state. Furthermore, the captured visual data can be converted into audio data to provide intuitively understandable information to visually impaired people.

[0614] "Visual data" refers to information such as images and environmental sounds that come into the user's field of vision.

[0615] "Vision device" refers to a device worn by a user that captures visual data.

[0616] "Transfer means" refers to a method or apparatus for transferring visual data sent from a visual device to a cloud server.

[0617] A "cloud server" refers to a server that stores and manages data on the Internet.

[0618] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze and classify data.

[0619] "Analysis Means" means a method or apparatus for analyzing and classifying visual data using generative AI.

[0620] "Emotion recognition means" refers to a method or apparatus for obtaining and associating user emotion data with analyzed visual data.

[0621] "Storage means" refers to a method or device for storing the analyzed and categorized visual data and making it easily accessible to the user.

[0622] "Search means" refers to a method or device for searching the analyzed and classified visual data based on a user's keywords and providing related visual data.

[0623] "Recommendation means" refers to a method or device that recommends appropriate visual data based on the emotional state of a user.

[0624] "Audio conversion means" refers to a method or device that analyzes captured visual data and converts it into audio data.

[0625] "Audio output means" refers to a method or device for outputting audio data in real time.

[0626] This invention is a system for efficiently recording, organizing, and replaying visual data, and in particular, for providing personalized visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0627] Visual Devices

[0628] The user wears a visual device. The visual device has the function of capturing visual information that comes into the user's field of vision in real time. Specifically, this includes eyeglass-type and contact lens-type devices. The visual device collects visual information and environmental sounds and temporarily stores them in its internal memory. This data is then transmitted to the terminal via wireless communication (Bluetooth or Wi-Fi).

[0629] Cloud Server and Generative AI

[0630] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it uses a generative AI to analyze the data. The generative AI uses a widely used artificial intelligence model (e.g., OpenAI's GPT-4). The generative AI divides the visual data into frames and runs an algorithm to evaluate the user's interest and importance. Through this analysis, the data is effectively classified and tagged with tags such as "tourist spot," "scenery," and "signpost." The analyzed data is stored in a cloud storage solution (e.g., Amazon S3) for easy access by users.

[0631] Emotion Engine

[0632] The emotion engine acquires the user's emotional data in real time and associates it with the analyzed visual data. The emotion engine uses technology (e.g., Affectiva's emotion recognition API) that analyzes the user's facial expressions, voice, and brain waves. This allows the visual data to be tagged with emotion tags such as "emotion" or "stress."

[0633] User terminal

[0634] A user device (e.g., a smartphone or computer) provides an interface for accessing visual data stored in the cloud server. By requesting specific visual data (e.g., "scenery of a tourist spot"), the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data is recommended based on the emotions recognized by the emotion engine.

[0635] Specific examples

[0636] When a user visits a tourist spot, the visual device captures the scenery and guide signs. This data is transferred to a cloud server via the smartphone, where the generation AI analyzes the data and assigns tags such as "tourist spot" and "scenery." For example, if a user experiences a "moving moment," the data is tagged "moving." When the user requests in the app to "play the scenery of the tourist spot I saw today," the cloud server searches for the relevant data and plays it on the smartphone.

[0637] Furthermore, if a user requests a "relaxing scene" when a tag has been added to a moment of stress, the generative AI will recommend and provide appropriate visual data. Furthermore, if a visually impaired person is wearing a visual device, the captured visual data is converted into audio data, and audio guidance is provided in real time. For example, information is provided in the form of "There are stairs on your right."

[0638] Prompt Sentence Examples

[0639] "Today I went to a tourist spot. Organize the data of the scenery and tag the moments that moved me."

[0640] "Please recommend a relaxing landscape."

[0641] In this way, the Vision Bank system effectively records, organizes, and plays back visual information, providing users with a personalized visual experience. The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, making it effective for a wide range of scenarios in daily life.

[0642] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0643] Step 1:

[0644] A visual device captures video and audio data.

[0645] A visual device worn by the user captures surrounding video and audio data in real time. The visual device's camera and microphone acquire video frames and audio data as input data. These data are temporarily stored in the visual device's internal memory. Specific operations include capturing video at 30 frames per second and recording environmental sounds in stereo.

[0646] Step 2:

[0647] The device transfers the captured data to a cloud server.

[0648] The data stored in the internal memory of the visual device is retrieved and transferred to a terminal (e.g., a smartphone). The input data, which the terminal transfers using Bluetooth or Wi-Fi, is the video and audio data received from the visual device. The terminal receives this data and uploads it to a cloud server using the HTTPS protocol. Specific operations include data encryption and secure transfer.

[0649] Step 3:

[0650] The cloud server uses generated AI to analyze and classify the data.

[0651] The cloud server receives video and audio data as input, and uses a generative AI (e.g., GPT-4 model) to analyze and classify the data frame by frame. Specifically, the video data is first analyzed using an image recognition algorithm (e.g., YOLOv5) to identify important objects and scenes. The generative AI then uses natural language processing to assign appropriate tags (e.g., "tourist spot," "scenery," "signpost") to the identified objects and scenes. The output data is the analyzed and classified visual data and its tags.

[0652] Step 4:

[0653] The cloud server associates the emotion data using an emotion engine.

[0654] The emotion engine acquires the user's facial expressions, voice, and brainwave data in real time and recognizes emotional information as input data. The cloud server associates this with visual data. Specifically, the user's emotions (e.g., "emotion" or "surprise") are added as tags from information acquired using Affectiva's emotion recognition API. The output data includes visual data tagged with emotions.

[0655] Step 5:

[0656] The cloud server stores the analyzed and classified data in storage.

[0657] The cloud server stores the analyzed and classified visual data and its tags in a storage medium (e.g., Amazon S3). The input data is the analyzed and classified visual data and its tags, and measures are taken to store it safely and efficiently. Specific actions include data redundancy and backup.

[0658] Step 6:

[0659] A user accesses the visual data using a terminal.

[0660] A user launches a smartphone app and sends a request to the cloud server to request specific visual data. For example, they input a prompt such as "Play the scenery of the tourist spots I saw today." The input data includes the user's search keywords and emotional state. Based on this, the cloud server searches for relevant visual data and outputs it to the user's device in the form of streaming playback. Specific operations include filtering the search results and selecting appropriate data.

[0661] Step 7:

[0662] The cloud server makes recommendations based on the user's emotional state.

[0663] Based on the user's emotional state obtained by the emotion engine, the generative AI recommends appropriate visual data. For example, if a user requests a "relaxing landscape," past emotional data is referenced and visual data that will reduce stress is selected. The input data includes the user's emotional history and current emotional state, and the recommended visual data is provided as output data. Specific operations include searching the user's history and applying the recommendation algorithm.

[0664] In this way, the Vision Bank system effectively records, analyzes and plays back users' visual information, providing a personalized visual experience based on their emotional state.

[0665] (Application example 2)

[0666] 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."

[0667] Today, there is a need for efficient digitization and easy access to the information and visual experiences users see on a daily basis. However, simply storing visual data makes it difficult for users to replay emotionally significant visual experiences or use them effectively at the appropriate time. Furthermore, there are insufficient means for visually impaired people to access visual information as audio data in real time. Providing personalized visual information based on the user's emotional state would provide a richer user experience, but this is currently difficult to achieve.

[0668] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI, a storage means that stores and provides the analyzed and classified visual data in a form easily accessible to the user, an emotion recognition means that recognizes the user's emotional data in real time and associates it with the visual data, and a recommendation means that recommends related visual data based on the user's request and emotional state. This allows users to efficiently manage their daily visual experiences and use optimal visual data and content according to their emotional state. Furthermore, visual information captured in real time can be provided as audio data to visually impaired people, making the use of visual information more effective.

[0669] A "vision device" is a device worn by a user to capture visual data.

[0670] "Transfer means" refers to a means for transmitting visual data from a visual device to a cloud server.

[0671] A "cloud server" is a server that stores and manages data over the Internet.

[0672] "Generative AI" is an artificial intelligence technique for analyzing and classifying visual data.

[0673] "Analysis means" refers to a means for analyzing and classifying visual data on a cloud server using a generative AI.

[0674] "Storage means" means for storing and providing analyzed and classified visual data in a form that is easily accessible to the user.

[0675] The "emotion recognition means" is a means for recognizing the user's emotion data in real time and associating it with visual data.

[0676] A "recommendation means" is a means for recommending relevant visual data based on a user's request and emotional state.

[0677] The "search means" is a means for searching the analyzed and classified visual data based on the user's keywords and providing the data to the user terminal.

[0678] The "audio conversion means" is a means for analyzing visual data captured by a visually impaired person and converting it into audio data.

[0679] The "audio output means" is a means for outputting audio data in real time.

[0680] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0681] Visual Devices

[0682] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0683] Cloud Server and Generative AI

[0684] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0685] Emotion Engine

[0686] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[0687] User terminal

[0688] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[0689] Specific examples

[0690] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0691] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[0692] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0693] Additionally, if the user experiences a moment of stress, that information will also be tagged by the emotion engine. If the user requests a "relaxing scene" when they want to relax, the generative AI will recommend appropriate visual data based on the emotion engine data, providing a visual experience that will reduce stress.

[0694] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[0695] Example prompts to input to a generative AI model:

[0696] Which beach videos would you recommend if a user is feeling relaxed?

[0697] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0698] Step 1:

[0699] The user wears a visual device, which captures visual data from the surroundings. The input is camera video and audio data, and the output is raw video and audio data acquired in real time. The visual device temporarily stores these data in its internal memory.

[0700] Step 2:

[0701] The visual and audio data captured by the vision device is sent to the cloud server. The input is the captured visual and audio data, and the output is a notification of the completion of data transfer to the cloud server. The device uses the data transfer function to upload the stored data to the cloud server.

[0702] Step 3:

[0703] The cloud server analyzes the received visual data using a generative AI model. The input is the visual data transferred to the cloud server, and the output is the analyzed and frame-classified data. The generative AI model divides the data into frames, evaluates the user's interest and importance, and assigns appropriate tags.

[0704] Step 4:

[0705] The analyzed and classified data is stored in a storage on a cloud server. The input is the analyzed and tagged visual data, and the output is a notification that the data has been saved to the cloud storage. The cloud server uses a storage means to save the data so that the user can access it later.

[0706] Step 5:

[0707] The emotion engine recognizes emotion data in real time from the user's facial expressions, voice, brain waves, etc. The input is the user's facial expressions, voice, and brain wave data, and the output is the recognized emotion data. The emotion engine determines the user's emotional state from this data and associates it with the visual data.

[0708] Step 6:

[0709] A user requests specific visual data through a smartphone app. The input is the user's search query or keywords, and the output is the provision of related visual data to the user's smartphone device. The user uses the app to input keywords such as "tourist scenery" or "relaxing scenery."

[0710] Step 7:

[0711] The cloud server recommends relevant visual data based on the user's emotional state and request. The input is a search query and emotional data, and the output is a list of recommended visual data. The server uses the recommendation mechanism to select the most suitable visual data according to the user's emotions and interests.

[0712] Step 8:

[0713] The recommended visual data is streamed to the user's smartphone. The input is the recommended visual data, and the output is the playback video of the visual data on the smartphone. The user can view the visual data in the app and enjoy the played video and audio.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Third embodiment]

[0718] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] 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. 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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."

[0730] ---

[0731] This invention is a system for efficiently recording, organizing, and reproducing visual data, which digitizes the information and visual experiences that users see on a daily basis and provides them in an easily accessible form. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, and a user terminal.

[0732] Visual Devices

[0733] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0734] Cloud Server and Generative AI

[0735] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0736] User terminal

[0737] A user device (such as a smartphone or computer) provides an interface for accessing the visual data stored in the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for relevant visual data and provides it to the user device.

[0738] Specific examples

[0739] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0740] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0741] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[0742] As described above, the present invention is a system that effectively records, organizes, and plays back visual information, providing an environment in which users can easily manage and use visual data in a variety of usage scenarios.

[0743] ---

[0744] The processing flow will be explained below.

[0745] ---

[0746] Step 1:

[0747] The user wears the visual device, which begins capturing the user's visual information in real time.

[0748] Step 2:

[0749] The device temporarily stores the visual data captured from the vision device in its internal memory, where the captured visual data is preprocessed and noise is removed.

[0750] Step 3:

[0751] The device periodically generates packets of visual information, encrypts them, and transmits them to a cloud server, where the user's data is stored securely.

[0752] Step 4:

[0753] The server receives packets of visual data, which are then analyzed by the generative AI and evaluated for importance and interest.

[0754] Step 5:

[0755] The generative AI in the server segments the visual data frame by frame and identifies key scenes based on the user's intent and the environment, effectively classifying the data.

[0756] Step 6:

[0757] The server tags and classifies the data based on the analysis results and stores it in a format that is easily accessible to users.

[0758] Step 7:

[0759] A user requests specific visual data through a smartphone app, for example by entering keywords such as "scenery of a tourist spot."

[0760] Step 8:

[0761] The server receives the user's request and searches for relevant visual data based on tags and classification information, and the search results are filtered appropriately.

[0762] Step 9:

[0763] The server provides the retrieved visual data to the user's device in streaming or download format, allowing the user to replay the visual data on their smartphone and relive past visual experiences.

[0764] Step 10:

[0765] When a visually impaired person uses a visual device, the visual device captures and transmits the data in real time to a cloud server, where the visual information is analyzed and converted into audio data.

[0766] Step 11:

[0767] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[0768] Step 12:

[0769] When a user encounters an incident or accident, the vision device automatically initiates high-definition recording, which is then immediately sent to a cloud server for tagging and classification.

[0770] Step 13:

[0771] The server stores important incident and accident data, and upon request from law enforcement agencies, provides the data with necessary permission, thereby ensuring that information that is useful to society is used effectively.

[0772] ---

[0773] This completes the detailed step-by-step explanation of the programming process flow of the "Vision Bank" system.

[0774] Example 1

[0775] 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."

[0776] Modern society requires efficient recording, organization, and playback of visual data, but existing technologies make this process cumbersome and often provide insufficient support, particularly for people with visual impairments. Furthermore, there are limited methods for managing vast amounts of visual data and quickly accessing them when needed.

[0777] 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.

[0778] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a terminal, a transmission means that uploads the visual data from the terminal to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI model, and a storage means that tags the analyzed and classified visual data, stores it in storage, and provides it to the visually impaired. This enables efficient recording, organization, and playback of visual data, and provides support to the visually impaired.

[0779] "Visual data" refers to the video and environmental sound data captured by a visual device.

[0780] A "vision device" is a device worn by a user that captures visual data in real time.

[0781] The "transfer means" is a function for transferring the visual data captured by the visual device to the terminal.

[0782] The "transmission means" is a function that allows the terminal to upload visual data to the cloud server.

[0783] A "cloud server" is a remote server for analyzing, categorizing, and storing visual data.

[0784] A "generative AI model" is an artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[0785] "Analysis means" refers to the functionality of the cloud server to analyze and classify visual data using the generated AI model.

[0786] "Tagging" is the process of assigning tags to analyzed and classified visual data for search and identification purposes.

[0787] The "storage means" is a function for storing tagged visual data in a storage and providing it in a form that can be accessed by the user.

[0788] The "search means" is a function for searching the analyzed and classified visual data based on the user's prompt sentence and providing it to the user terminal.

[0789] The "audio conversion means" is a function that analyzes the visual data captured by the visual device and converts it into audio data.

[0790] The "audio output means" is a function that outputs the converted audio data as audio in real time.

[0791] A "prompt sentence" is a linguistic instruction that a user inputs from their device when searching for or requesting visual data from a cloud server.

[0792] This invention is a system for effectively recording, organizing and reproducing visual data, and has a wide range of applications, particularly in supporting the visually impaired. The processing of the program will now be described in detail.

[0793] Hardware and software used

[0794] Hardware

[0795] Vision devices: Eyeglasses or contact lenses worn by the user that capture visual data (video and audio) in real time.

[0796] Terminal: A smartphone or tablet operated by the user. It receives data from the visual device and sends it to the cloud server.

[0797] Cloud Server: A remote server that performs data analysis, classification, and storage.

[0798] software

[0799] Generative AI model: An artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[0800] Data capture with visual devices

[0801] The user wears a visual device that captures the user's field of vision and the surrounding audio in real time and stores this data in a built-in temporary memory. For example, when the user visits a tourist spot, the visual device continuously records the scenery and guide signs.

[0802] Transferring data to your device

[0803] The captured visual data is then transferred to the device via Wi-Fi or Bluetooth. For example, when a user enters a cafe and connects to Wi-Fi, the data is automatically transferred to the device.

[0804] Data reception and analysis by cloud server

[0805] The device uploads the received visual data to a cloud server, which then feeds the data to a generative AI model for analysis such as scene recognition, object detection, and voice recognition. For example, the generative AI model can identify tourist attractions and important signs in the video and extract relevant information.

[0806] Data storage and tagging

[0807] The analyzed and classified visual data is then tagged and stored in cloud server storage. This process makes the data easier to search and allows users to easily access the information they need. For example, data on tourist spots is tagged with "scenery," "famous places," and "signs."

[0808] Data request by user device

[0809] The user starts the smartphone app and inputs a prompt to send a data request to the cloud server. For example, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today."

[0810] Data search and provision via cloud servers

[0811] The cloud server receives the user's request, searches for relevant data from the tagged database, and once it finds the relevant data, it sends it to the user's device. For example, landscape data captured at a tourist spot is searched and provided to the user's smartphone.

[0812] Data playback on user terminal

[0813] The user's smartphone receives the data sent from the cloud server and plays the visual data through the app, allowing the user to re-experience the scenery and guide they saw at the tourist spot. For example, the scenery of the tourist spot is played on the smartphone screen, allowing the user to recreate the experience as if they were actually there.

[0814] Prompt Sentence Examples

[0815] "Play the photos I took today"

[0816] "Search for scenery of tourist spots"

[0817] "Can you give me directions to a nearby restaurant?"

[0818] In this way, the present invention realizes efficient recording, organization, and playback of visual data, providing an environment in which visual data can be easily managed and used in various usage scenarios, and can provide useful support especially for the visually impaired.

[0819] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0820] Step 1:

[0821] The user wears a visual device, which captures video and audio data in real time. The input is the video and environmental sounds that the user sees, which the visual device captures and temporarily stores in its internal memory. Specific operations include the continuous recording of video and audio by the camera and microphone built into the visual device.

[0822] Step 2:

[0823] The data captured by the vision device is transmitted to the terminal via Wi-Fi or Bluetooth. The input is the captured data in the vision device, which is received by the receiving module of the terminal. The specific operation includes the vision device transmitting the data to the terminal via Wi-Fi or Bluetooth. The output is the visual data transmitted to the terminal.

[0824] Step 3:

[0825] The visual data received by the device is uploaded to the cloud server. The input is the data received by the device, which is then sent to the cloud server. The specific operation involves the device uploading the visual data to the cloud server through an Internet connection. The output is the visual data stored on the cloud server.

[0826] Step 4:

[0827] The cloud server receives visual data and analyzes it using a generative AI model. The input is visual data stored on the cloud server, which the generative AI model analyzes frame by frame. Specific data processing includes scene recognition, object detection, and voice recognition. The output is analyzed and classified data.

[0828] Step 5:

[0829] The cloud server assigns appropriate tags to the analyzed and classified data and stores it in storage. The input is the analyzed and classified visual data, which is tagged with specific tags. Specific operations include the tagging and storage process in a database. The output is the tagged visual data stored in the database.

[0830] Step 6:

[0831] The user inputs a prompt via their device to request data from the cloud server. The input is the prompt entered by the user, which is sent to the cloud server by a dedicated app. In concrete terms, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today" into the app's search bar. The output is a request to the cloud server.

[0832] Step 7:

[0833] The cloud server searches for relevant data based on the prompt sentence and provides it to the user's device. The input is a database tagged with the prompt sentence, and the cloud server searches for data based on this. The specific data calculation involves evaluating the degree of match with the prompt sentence and extracting highly relevant data. The output is visual data sent to the user's device.

[0834] Step 8:

[0835] The user device receives the data sent from the cloud server and plays it through the app. The input is visual data sent from the cloud server, which the user views on their smartphone or tablet. Specific operations include the app streaming the received data. The output is visual data displayed on the user device.

[0836] (Application example 1)

[0837] 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."

[0838] Conventional autonomous vehicles lack a mechanism for efficiently collecting visual information from the surroundings, analyzing and classifying it in real time, and providing driving assistance information. This has resulted in insufficient information provision to improve driving safety and efficiency. Furthermore, there is also a lack of effective means for replaying and searching past driving data, making it difficult to recreate accidents or review driving conditions.

[0839] 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.

[0840] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generation AI, a driving assistance means that provides the analyzed visual data as driving assistance information, and a storage means that stores and provides the analyzed and classified visual data in a form that is easily accessible to users. This enables autonomous vehicles to collect and analyze visual information in real time and provide driving assistance information that improves safety and efficiency. Furthermore, the ability to effectively play back and search past driving data makes it easy to recreate accidents and review driving conditions.

[0841] "Visual data" is digital data that includes the user's visual experience and surrounding image information.

[0842] A "visual device" is a device worn by a user that captures visual data, and refers to a device that includes a camera, sensor, etc.

[0843] "Transfer means" refers to a communication means for transferring visual data acquired from a visual device to a cloud server, and includes wired communication and wireless communication.

[0844] A "cloud server" is a server system for storing, processing, and providing data via the Internet.

[0845] "Analysis means" refers to the function that analyzes visual data on a cloud server using AI to generate and classify and tag it.

[0846] "Generative AI" refers to programs and algorithms that use artificial intelligence technology to generate and analyze digital data.

[0847] "Driving assistance means" refers to functions that provide information and warnings necessary for driving based on analyzed visual data, improving safety and efficiency.

[0848] "Storage Means" refers to a storage device or system for storing analyzed and classified visual data and providing it in a form that is easily accessible to users.

[0849] "Search means" refers to a function that allows users to search for keywords or find and provide visual data based on specific conditions.

[0850] "Display means" refers to a device or screen that displays driving assistance information to the driver in real time based on analyzed visual data.

[0851] In implementing this invention, a system consisting of the following elements is used to enable the collection, analysis, driving assistance, and playback / search of visual data.

[0852] Hardware

[0853] 1. Visual devices:

[0854] Vehicle-mounted cameras: Capture visual data in real time.

[0855] Microphone: Collects audio data.

[0856] 2. User Device:

[0857] Smartphone or computer: To access and view the analyzed data.

[0858] 3. Cloud Server:

[0859] Cloud infrastructure: Stores, analyzes, and serves data.

[0860] software

[0861] 1. Means of transfer:

[0862] A communications protocol for transferring visual data to a cloud server via Wi-Fi or mobile data communications.

[0863] 2. Analysis method:

[0864] OpenCV: Used for visual data capture and image processing.

[0865] AWS S3: A storage service for storing captured visual data in the cloud.

[0866] AWS Rekognition: A service that analyzes image data in the cloud and performs labeling and classification.

[0867] 3. Driving assistance measures:

[0868] An interface that provides real-time safety information and warnings to drivers, based on data analyzed on a cloud server.

[0869] 4. Storage Method:

[0870] Use AWS S3 or other cloud storage services to store the analyzed data and make it easily accessible to users.

[0871] 5. Search methods:

[0872] A function that allows users to search and play back past visual data by keyword or condition, using natural language processing (NLP) technology.

[0873] Specific examples

[0874] Specific use cases include the following scenarios:

[0875] Self-driving vehicles in operation

[0876] 1. Data Collection:

[0877] While the autonomous vehicle is driving, a camera mounted on the vehicle captures visual data of the area ahead in real time, while a microphone also collects environmental sounds.

[0878] 2. Data Transfer:

[0879] The collected visual and audio data is transferred to a cloud server using Wi-Fi or mobile data.

[0880] 3. Data Analysis:

[0881] Once the visual data reaches the cloud server, it is analyzed using AWS Rekognition, which includes object detection and labeling, extracting information such as "pedestrians" and "traffic lights."

[0882] 4. Driving assistance:

[0883] Based on the analyzed data, the driver assistance system provides real-time warnings and guidance, for example, a warning that "there is a pedestrian ahead" is displayed to the driver.

[0884] 5. Data Storage and Retrieval:

[0885] The analyzed and classified visual data is stored in cloud storage such as AWS S3 so that it can be searched and played back later. When the user inputs a request such as "play back past driving data," the relevant data is searched for and played back from the cloud server.

[0886] Prompt Sentence Examples

[0887] As a concrete example of a prompt sentence, for example, enter the following prompt sentence for Amazon Rekognition:

[0888] "Analyze uploaded autonomous vehicle images to identify pedestrians, vehicles, traffic lights, and road signs. Label them and provide a confidence score."

[0889] The above system configuration and processing enable autonomous vehicles to efficiently collect and analyze visual data, provide driving assistance in real time, and effectively search and replay past driving data.

[0890] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0891] Step 1: Capture visual data

[0892] A camera mounted on an autonomous vehicle captures visual data of the road ahead in real time. Specifically, the camera captures images frame by frame and temporarily stores them in memory. The input is the video frame acquired from the camera, and the output is the visual data stored in memory.

[0893] Step 2: Collecting audio data

[0894] The microphone collects environmental sounds and audio data from inside the vehicle. The microphone captures audio data in real time and temporarily stores it in memory. The audio data obtained from the microphone is the input, and the audio data stored in memory is generated as the output.

[0895] Step 3: Data Transfer

[0896] The acquired visual and audio data is transferred to a cloud server via Wi-Fi or mobile data communication. The input is the visual and audio data stored in the memory, and the output is the data transmitted to the cloud server. Specifically, the transmission means packetizes the data and transmits it to a specific address on the cloud server via the Internet.

[0897] Step 4: Data analysis

[0898] The cloud server analyzes the received visual and audio data. Specifically, it is stored in AWS S3 and analyzed by AWS Rekognition. The input is the visual and audio data stored on the cloud server, and the output is the analysis results. AWS generative AI models are used to detect, tag, and classify objects in the images.

[0899] Step 5: Generate driver assistance information

[0900] The cloud server generates driving assistance information based on the analysis results. The input is the analysis results of AWS Rekognition, and the output is the driving assistance information. Specifically, warning messages such as "There is a pedestrian ahead" or "The traffic light is red" are created.

[0901] Step 6: Providing driver assistance information

[0902] The cloud server generates driving assistance information and sends it to the vehicle's driving assistance system. The input is the driving assistance information generated by the cloud server, and the output is data provided to the driving assistance system. The driving assistance system displays this information to the driver in real time and provides warnings and guidance.

[0903] Step 7: Save Data

[0904] The analyzed and classified visual data is stored in cloud storage (AWS S3). The analyzed visual data is the input, and the data stored in cloud storage is generated as the output. This allows past driving data to be saved and later searched and played back.

[0905] Step 8: Data Retrieval

[0906] A user uses a smartphone application to search for past driving data. The input is keywords and conditions entered by the user, and the output is visual data as search results. Specifically, the cloud server searches the stored data based on the user's request and sends the relevant data to the user's device.

[0907] Step 9: Data playback

[0908] The user device plays back the visual data received from the cloud server. The input is the visual data sent from the cloud server, and the output is the video and audio displayed on the user device. The user can visually check past driving data on their smartphone or computer.

[0909] The above processing steps realize a system that collects and analyzes visual data in real time and provides driving assistance information. It also makes it possible to effectively search and replay past data.

[0910] 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.

[0911] ---

[0912] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0913] Visual Devices

[0914] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[0915] Cloud Server and Generative AI

[0916] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[0917] Emotion Engine

[0918] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[0919] User terminal

[0920] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[0921] Specific examples

[0922] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[0923] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[0924] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[0925] Additionally, if a user experiences a moment of stress, that information is also tagged by the emotion engine. When a user requests a "relaxing scene" to relax, the generative AI will recommend appropriate visual data based on the emotion engine's data, providing a visual experience to reduce stress. Furthermore, if a visually impaired person is wearing a visual device, the generative AI will analyze the captured visual data and convert it into audio data. This audio data is sent to the user's audio output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device will capture visual information about the platform and output that information as audio, such as, "There are stairs on your right."

[0926] As such, this invention is a system that effectively records, organizes, and plays back visual information, providing an environment where users can easily manage and use visual data in a variety of usage scenarios.The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, realizing a more personalized experience.

[0927] ---

[0928] The processing flow will be explained below.

[0929] ---

[0930] Step 1:

[0931] The user wears the visual device, which begins capturing the user's visual information in real time. The visual device is equipped with a camera and audio sensor, which simultaneously captures video and audio data.

[0932] Step 2:

[0933] The device temporarily stores the visual data captured from the visual device in its internal memory, along with the date, time, and location information.

[0934] Step 3:

[0935] The device performs preprocessing on the data sent from the vision device, removing noise if necessary, thereby improving the quality of the vision data.

[0936] Step 4:

[0937] The device collects visual data as well as emotional data from the user, which is then recognized in real time by an emotion engine based on the user's facial expressions, voice, and brainwaves.

[0938] Step 5:

[0939] The device transfers visual and emotional data together to a cloud server, where the data is encrypted before being sent.

[0940] Step 6:

[0941] The server analyzes the received visual and emotional data, and the generation AI divides the visual data into frames and evaluates their relevance to the emotional data.

[0942] Step 7:

[0943] The server's generation AI evaluates the importance and interest of the visual data and tags it in conjunction with the emotional data. For example, tags such as "emotion," "surprise," and "joy" can be added.

[0944] Step 8:

[0945] The server stores the analyzed and tagged visual data in a storage means, including the user's emotional state.

[0946] Step 9:

[0947] A user requests specific visual data through a smartphone app, for example, a "relaxing landscape."

[0948] Step 10:

[0949] The server receives the user's request, and the generation AI searches for the most suitable visual data based on the emotion engine data. The search results are filtered based on the user's emotional state.

[0950] Step 11:

[0951] The server provides the search results to the user's device in streaming or download format, and the user plays the visual data on their visual device or smartphone.

[0952] Step 12:

[0953] When a visually impaired person uses a visual device, the device captures visual information in real time and transmits it to a cloud server, which then analyzes the visual data and converts it into audio data.

[0954] Step 13:

[0955] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[0956] ---

[0957] The above is the specific processing flow when combining the "Vision Bank" system with an emotion engine.

[0958] Example 2

[0959] 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."

[0960] Conventional visual data management systems have difficulty efficiently recording, organizing, and replaying the visual information users acquire on a daily basis. Furthermore, they lack the ability to classify and recommend visual data based on the user's emotional state, making it difficult to provide a personalized experience. Furthermore, they lack a means for visually impaired people to intuitively understand visual data.

[0961] 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 a visual device means for capturing visual data, a transfer means for transferring the visual data transmitted from the visual device to a cloud server, an analysis means for analyzing and classifying the visual data on the cloud server using a generative AI, an emotion recognition means for associating emotion data with the analyzed and classified visual data, and a storage means for storing and providing the analyzed and classified visual data in a form easily accessible to the user. This enables efficient recording, organization, and playback of the user's visual information, and realizes the provision of personalized visual data based on the user's emotional state. Furthermore, the captured visual data can be converted into audio data to provide intuitively understandable information to visually impaired people.

[0962] "Visual data" refers to information such as images and environmental sounds that come into the user's field of vision.

[0963] "Vision device" refers to a device worn by a user that captures visual data.

[0964] "Transfer means" refers to a method or apparatus for transferring visual data sent from a visual device to a cloud server.

[0965] A "cloud server" refers to a server that stores and manages data on the Internet.

[0966] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze and classify data.

[0967] "Analysis Means" means a method or apparatus for analyzing and classifying visual data using generative AI.

[0968] "Emotion recognition means" refers to a method or apparatus for obtaining and associating user emotion data with analyzed visual data.

[0969] "Storage means" refers to a method or device for storing the analyzed and categorized visual data and making it easily accessible to the user.

[0970] "Search means" refers to a method or device for searching the analyzed and classified visual data based on a user's keywords and providing related visual data.

[0971] "Recommendation means" refers to a method or device that recommends appropriate visual data based on the emotional state of a user.

[0972] "Audio conversion means" refers to a method or device that analyzes captured visual data and converts it into audio data.

[0973] "Audio output means" refers to a method or device for outputting audio data in real time.

[0974] This invention is a system for efficiently recording, organizing, and replaying visual data, and in particular, for providing personalized visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[0975] Visual Devices

[0976] The user wears a visual device. The visual device has the function of capturing visual information that comes into the user's field of vision in real time. Specifically, this includes eyeglass-type and contact lens-type devices. The visual device collects visual information and environmental sounds and temporarily stores them in its internal memory. This data is then transmitted to the terminal via wireless communication (Bluetooth or Wi-Fi).

[0977] Cloud Server and Generative AI

[0978] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it uses a generative AI to analyze the data. The generative AI uses a widely used artificial intelligence model (e.g., OpenAI's GPT-4). The generative AI divides the visual data into frames and runs an algorithm to evaluate the user's interest and importance. Through this analysis, the data is effectively classified and tagged with tags such as "tourist spot," "scenery," and "signpost." The analyzed data is stored in a cloud storage solution (e.g., Amazon S3) for easy access by users.

[0979] Emotion Engine

[0980] The emotion engine acquires the user's emotional data in real time and associates it with the analyzed visual data. The emotion engine uses technology (e.g., Affectiva's emotion recognition API) that analyzes the user's facial expressions, voice, and brain waves. This allows the visual data to be tagged with emotion tags such as "emotion" or "stress."

[0981] User terminal

[0982] A user device (e.g., a smartphone or computer) provides an interface for accessing visual data stored in the cloud server. By requesting specific visual data (e.g., "scenery of a tourist spot"), the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data is recommended based on the emotions recognized by the emotion engine.

[0983] Specific examples

[0984] When a user visits a tourist spot, the visual device captures the scenery and guide signs. This data is transferred to a cloud server via the smartphone, where the generation AI analyzes the data and assigns tags such as "tourist spot" and "scenery." For example, if a user experiences a "moving moment," the data is tagged "moving." When the user requests in the app to "play the scenery of the tourist spot I saw today," the cloud server searches for the relevant data and plays it on the smartphone.

[0985] Furthermore, if a user requests a "relaxing scene" when a tag has been added to a moment of stress, the generative AI will recommend and provide appropriate visual data. Furthermore, if a visually impaired person is wearing a visual device, the captured visual data is converted into audio data, and audio guidance is provided in real time. For example, information is provided in the form of "There are stairs on your right."

[0986] Prompt Sentence Examples

[0987] "Today I went to a tourist spot. Organize the data of the scenery and tag the moments that moved me."

[0988] "Please recommend a relaxing landscape."

[0989] In this way, the Vision Bank system effectively records, organizes, and plays back visual information, providing users with a personalized visual experience. The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, making it effective for a wide range of scenarios in daily life.

[0990] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0991] Step 1:

[0992] A visual device captures video and audio data.

[0993] A visual device worn by the user captures surrounding video and audio data in real time. The visual device's camera and microphone acquire video frames and audio data as input data. These data are temporarily stored in the visual device's internal memory. Specific operations include capturing video at 30 frames per second and recording environmental sounds in stereo.

[0994] Step 2:

[0995] The device transfers the captured data to a cloud server.

[0996] The data stored in the internal memory of the visual device is retrieved and transferred to a terminal (e.g., a smartphone). The input data, which the terminal transfers using Bluetooth or Wi-Fi, is the video and audio data received from the visual device. The terminal receives this data and uploads it to a cloud server using the HTTPS protocol. Specific operations include data encryption and secure transfer.

[0997] Step 3:

[0998] The cloud server uses generated AI to analyze and classify the data.

[0999] The cloud server receives video and audio data as input, and uses a generative AI (e.g., GPT-4 model) to analyze and classify the data frame by frame. Specifically, the video data is first analyzed using an image recognition algorithm (e.g., YOLOv5) to identify important objects and scenes. The generative AI then uses natural language processing to assign appropriate tags (e.g., "tourist spot," "scenery," "signpost") to the identified objects and scenes. The output data is the analyzed and classified visual data and its tags.

[1000] Step 4:

[1001] The cloud server associates the emotion data using an emotion engine.

[1002] The emotion engine acquires the user's facial expressions, voice, and brainwave data in real time and recognizes emotional information as input data. The cloud server associates this with visual data. Specifically, the user's emotions (e.g., "emotion" or "surprise") are added as tags from information acquired using Affectiva's emotion recognition API. The output data includes visual data tagged with emotions.

[1003] Step 5:

[1004] The cloud server stores the analyzed and classified data in storage.

[1005] The cloud server stores the analyzed and classified visual data and its tags in a storage medium (e.g., Amazon S3). The input data is the analyzed and classified visual data and its tags, and measures are taken to store it safely and efficiently. Specific actions include data redundancy and backup.

[1006] Step 6:

[1007] A user accesses the visual data using a terminal.

[1008] A user launches a smartphone app and sends a request to the cloud server to request specific visual data. For example, they input a prompt such as "Play the scenery of the tourist spots I saw today." The input data includes the user's search keywords and emotional state. Based on this, the cloud server searches for relevant visual data and outputs it to the user's device in the form of streaming playback. Specific operations include filtering the search results and selecting appropriate data.

[1009] Step 7:

[1010] The cloud server makes recommendations based on the user's emotional state.

[1011] Based on the user's emotional state obtained by the emotion engine, the generative AI recommends appropriate visual data. For example, if a user requests a "relaxing landscape," past emotional data is referenced and visual data that will reduce stress is selected. The input data includes the user's emotional history and current emotional state, and the recommended visual data is provided as output data. Specific operations include searching the user's history and applying the recommendation algorithm.

[1012] In this way, the Vision Bank system effectively records, analyzes and plays back users' visual information, providing a personalized visual experience based on their emotional state.

[1013] (Application example 2)

[1014] 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."

[1015] Today, there is a need for efficient digitization and easy access to the information and visual experiences users see on a daily basis. However, simply storing visual data makes it difficult for users to replay emotionally significant visual experiences or use them effectively at the appropriate time. Furthermore, there are insufficient means for visually impaired people to access visual information as audio data in real time. Providing personalized visual information based on the user's emotional state would provide a richer user experience, but this is currently difficult to achieve.

[1016] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI, a storage means that stores and provides the analyzed and classified visual data in a form easily accessible to the user, an emotion recognition means that recognizes the user's emotional data in real time and associates it with the visual data, and a recommendation means that recommends related visual data based on the user's request and emotional state. This allows users to efficiently manage their daily visual experiences and use optimal visual data and content according to their emotional state. Furthermore, visual information captured in real time can be provided as audio data to visually impaired people, making the use of visual information more effective.

[1017] A "vision device" is a device worn by a user to capture visual data.

[1018] "Transfer means" refers to a means for transmitting visual data from a visual device to a cloud server.

[1019] A "cloud server" is a server that stores and manages data over the Internet.

[1020] "Generative AI" is an artificial intelligence technique for analyzing and classifying visual data.

[1021] "Analysis means" refers to a means for analyzing and classifying visual data on a cloud server using a generative AI.

[1022] "Storage means" means for storing and providing analyzed and classified visual data in a form that is easily accessible to the user.

[1023] The "emotion recognition means" is a means for recognizing the user's emotion data in real time and associating it with visual data.

[1024] A "recommendation means" is a means for recommending relevant visual data based on a user's request and emotional state.

[1025] The "search means" is a means for searching the analyzed and classified visual data based on the user's keywords and providing the data to the user terminal.

[1026] The "audio conversion means" is a means for analyzing visual data captured by a visually impaired person and converting it into audio data.

[1027] The "audio output means" is a means for outputting audio data in real time.

[1028] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[1029] Visual Devices

[1030] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[1031] Cloud Server and Generative AI

[1032] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[1033] Emotion Engine

[1034] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[1035] User terminal

[1036] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[1037] Specific examples

[1038] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[1039] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[1040] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[1041] Additionally, if the user experiences a moment of stress, that information will also be tagged by the emotion engine. If the user requests a "relaxing scene" when they want to relax, the generative AI will recommend appropriate visual data based on the emotion engine data, providing a visual experience that will reduce stress.

[1042] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[1043] Example prompts to input to a generative AI model:

[1044] Which beach videos would you recommend if a user is feeling relaxed?

[1045] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1046] Step 1:

[1047] The user wears a visual device, which captures visual data from the surroundings. The input is camera video and audio data, and the output is raw video and audio data acquired in real time. The visual device temporarily stores these data in its internal memory.

[1048] Step 2:

[1049] The visual and audio data captured by the vision device is sent to the cloud server. The input is the captured visual and audio data, and the output is a notification of the completion of data transfer to the cloud server. The device uses the data transfer function to upload the stored data to the cloud server.

[1050] Step 3:

[1051] The cloud server analyzes the received visual data using a generative AI model. The input is the visual data transferred to the cloud server, and the output is the analyzed and frame-classified data. The generative AI model divides the data into frames, evaluates the user's interest and importance, and assigns appropriate tags.

[1052] Step 4:

[1053] The analyzed and classified data is stored in a storage on a cloud server. The input is the analyzed and tagged visual data, and the output is a notification that the data has been saved to the cloud storage. The cloud server uses a storage means to save the data so that the user can access it later.

[1054] Step 5:

[1055] The emotion engine recognizes emotion data in real time from the user's facial expressions, voice, brain waves, etc. The input is the user's facial expressions, voice, and brain wave data, and the output is the recognized emotion data. The emotion engine determines the user's emotional state from this data and associates it with the visual data.

[1056] Step 6:

[1057] A user requests specific visual data through a smartphone app. The input is the user's search query or keywords, and the output is the provision of related visual data to the user's smartphone device. The user uses the app to input keywords such as "tourist scenery" or "relaxing scenery."

[1058] Step 7:

[1059] The cloud server recommends relevant visual data based on the user's emotional state and request. The input is a search query and emotional data, and the output is a list of recommended visual data. The server uses the recommendation mechanism to select the most suitable visual data according to the user's emotions and interests.

[1060] Step 8:

[1061] The recommended visual data is streamed to the user's smartphone. The input is the recommended visual data, and the output is the playback video of the visual data on the smartphone. The user can view the visual data in the app and enjoy the played video and audio.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] [Fourth embodiment]

[1066] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1067] 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.

[1068] 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).

[1069] 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.

[1070] 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.

[1071] 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).

[1072] 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. 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.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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.

[1077] 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.

[1078] 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."

[1079] ---

[1080] This invention is a system for efficiently recording, organizing, and reproducing visual data, which digitizes the information and visual experiences that users see on a daily basis and provides them in an easily accessible form. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, and a user terminal.

[1081] Visual Devices

[1082] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[1083] Cloud Server and Generative AI

[1084] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[1085] User terminal

[1086] A user device (such as a smartphone or computer) provides an interface for accessing the visual data stored in the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for relevant visual data and provides it to the user device.

[1087] Specific examples

[1088] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[1089] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[1090] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[1091] As described above, the present invention is a system that effectively records, organizes, and plays back visual information, providing an environment in which users can easily manage and use visual data in a variety of usage scenarios.

[1092] ---

[1093] The processing flow will be explained below.

[1094] ---

[1095] Step 1:

[1096] The user wears the visual device, which begins capturing the user's visual information in real time.

[1097] Step 2:

[1098] The device temporarily stores the visual data captured from the vision device in its internal memory, where the captured visual data is preprocessed and noise is removed.

[1099] Step 3:

[1100] The device periodically generates packets of visual information, encrypts them, and transmits them to a cloud server, where the user's data is stored securely.

[1101] Step 4:

[1102] The server receives packets of visual data, which are then analyzed by the generative AI and evaluated for importance and interest.

[1103] Step 5:

[1104] The generative AI in the server segments the visual data frame by frame and identifies key scenes based on the user's intent and the environment, effectively classifying the data.

[1105] Step 6:

[1106] The server tags and classifies the data based on the analysis results and stores it in a format that is easily accessible to users.

[1107] Step 7:

[1108] A user requests specific visual data through a smartphone app, for example by entering keywords such as "scenery of a tourist spot."

[1109] Step 8:

[1110] The server receives the user's request and searches for relevant visual data based on tags and classification information, and the search results are filtered appropriately.

[1111] Step 9:

[1112] The server provides the retrieved visual data to the user's device in streaming or download format, allowing the user to replay the visual data on their smartphone and relive past visual experiences.

[1113] Step 10:

[1114] When a visually impaired person uses a visual device, the visual device captures and transmits the data in real time to a cloud server, where the visual information is analyzed and converted into audio data.

[1115] Step 11:

[1116] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[1117] Step 12:

[1118] When a user encounters an incident or accident, the vision device automatically initiates high-definition recording, which is then immediately sent to a cloud server for tagging and classification.

[1119] Step 13:

[1120] The server stores important incident and accident data, and upon request from law enforcement agencies, provides the data with necessary permission, thereby ensuring that information that is useful to society is used effectively.

[1121] ---

[1122] This completes the detailed step-by-step explanation of the programming process flow of the "Vision Bank" system.

[1123] Example 1

[1124] 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."

[1125] Modern society requires efficient recording, organization, and playback of visual data, but existing technologies make this process cumbersome and often provide insufficient support, particularly for people with visual impairments. Furthermore, there are limited methods for managing vast amounts of visual data and quickly accessing them when needed.

[1126] 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.

[1127] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a terminal, a transmission means that uploads the visual data from the terminal to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI model, and a storage means that tags the analyzed and classified visual data, stores it in storage, and provides it to the visually impaired. This enables efficient recording, organization, and playback of visual data, and provides support to the visually impaired.

[1128] "Visual data" refers to the video and environmental sound data captured by a visual device.

[1129] A "vision device" is a device worn by a user that captures visual data in real time.

[1130] The "transfer means" is a function for transferring the visual data captured by the visual device to the terminal.

[1131] The "transmission means" is a function that allows the terminal to upload visual data to the cloud server.

[1132] A "cloud server" is a remote server for analyzing, categorizing, and storing visual data.

[1133] A "generative AI model" is an artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[1134] "Analysis means" refers to the functionality of the cloud server to analyze and classify visual data using the generated AI model.

[1135] "Tagging" is the process of assigning tags to analyzed and classified visual data for search and identification purposes.

[1136] The "storage means" is a function for storing tagged visual data in a storage and providing it in a form that can be accessed by the user.

[1137] The "search means" is a function for searching the analyzed and classified visual data based on the user's prompt sentence and providing it to the user terminal.

[1138] The "audio conversion means" is a function that analyzes the visual data captured by the visual device and converts it into audio data.

[1139] The "audio output means" is a function that outputs the converted audio data as audio in real time.

[1140] A "prompt sentence" is a linguistic instruction that a user inputs from their device when searching for or requesting visual data from a cloud server.

[1141] This invention is a system for effectively recording, organizing and reproducing visual data, and has a wide range of applications, particularly in supporting the visually impaired. The processing of the program will now be described in detail.

[1142] Hardware and software used

[1143] Hardware

[1144] Vision devices: Eyeglasses or contact lenses worn by the user that capture visual data (video and audio) in real time.

[1145] Terminal: A smartphone or tablet operated by the user. It receives data from the visual device and sends it to the cloud server.

[1146] Cloud Server: A remote server that performs data analysis, classification, and storage.

[1147] software

[1148] Generative AI model: An artificial intelligence algorithm that runs on a cloud server and analyzes and classifies visual data.

[1149] Data capture with visual devices

[1150] The user wears a visual device that captures the user's field of vision and the surrounding audio in real time and stores this data in a built-in temporary memory. For example, when the user visits a tourist spot, the visual device continuously records the scenery and guide signs.

[1151] Transferring data to your device

[1152] The captured visual data is then transferred to the device via Wi-Fi or Bluetooth. For example, when a user enters a cafe and connects to Wi-Fi, the data is automatically transferred to the device.

[1153] Data reception and analysis by cloud server

[1154] The device uploads the received visual data to a cloud server, which then feeds the data to a generative AI model for analysis such as scene recognition, object detection, and voice recognition. For example, the generative AI model can identify tourist attractions and important signs in the video and extract relevant information.

[1155] Data storage and tagging

[1156] The analyzed and classified visual data is then tagged and stored in cloud server storage. This process makes the data easier to search and allows users to easily access the information they need. For example, data on tourist spots is tagged with "scenery," "famous places," and "signs."

[1157] Data request by user device

[1158] The user starts the smartphone app and inputs a prompt to send a data request to the cloud server. For example, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today."

[1159] Data search and provision via cloud servers

[1160] The cloud server receives the user's request, searches for relevant data from the tagged database, and once it finds the relevant data, it sends it to the user's device. For example, landscape data captured at a tourist spot is searched and provided to the user's smartphone.

[1161] Data playback on user terminal

[1162] The user's smartphone receives the data sent from the cloud server and plays the visual data through the app, allowing the user to re-experience the scenery and guide they saw at the tourist spot. For example, the scenery of the tourist spot is played on the smartphone screen, allowing the user to recreate the experience as if they were actually there.

[1163] Prompt Sentence Examples

[1164] "Play the photos I took today"

[1165] "Search for scenery of tourist spots"

[1166] "Can you give me directions to a nearby restaurant?"

[1167] In this way, the present invention realizes efficient recording, organization, and playback of visual data, providing an environment in which visual data can be easily managed and used in various usage scenarios, and can provide useful support especially for the visually impaired.

[1168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] The user wears a visual device, which captures video and audio data in real time. The input is the video and environmental sounds that the user sees, which the visual device captures and temporarily stores in its internal memory. Specific operations include the continuous recording of video and audio by the camera and microphone built into the visual device.

[1171] Step 2:

[1172] The data captured by the vision device is transmitted to the terminal via Wi-Fi or Bluetooth. The input is the captured data in the vision device, which is received by the receiving module of the terminal. The specific operation includes the vision device transmitting the data to the terminal via Wi-Fi or Bluetooth. The output is the visual data transmitted to the terminal.

[1173] Step 3:

[1174] The visual data received by the device is uploaded to the cloud server. The input is the data received by the device, which is then sent to the cloud server. The specific operation involves the device uploading the visual data to the cloud server through an Internet connection. The output is the visual data stored on the cloud server.

[1175] Step 4:

[1176] The cloud server receives visual data and analyzes it using a generative AI model. The input is visual data stored on the cloud server, which the generative AI model analyzes frame by frame. Specific data processing includes scene recognition, object detection, and voice recognition. The output is analyzed and classified data.

[1177] Step 5:

[1178] The cloud server assigns appropriate tags to the analyzed and classified data and stores it in storage. The input is the analyzed and classified visual data, which is tagged with specific tags. Specific operations include the tagging and storage process in a database. The output is the tagged visual data stored in the database.

[1179] Step 6:

[1180] The user inputs a prompt via their device to request data from the cloud server. The input is the prompt entered by the user, which is sent to the cloud server by a dedicated app. In concrete terms, the user inputs a prompt such as "Play the scenery of the tourist spots I saw today" into the app's search bar. The output is a request to the cloud server.

[1181] Step 7:

[1182] The cloud server searches for relevant data based on the prompt sentence and provides it to the user's device. The input is a database tagged with the prompt sentence, and the cloud server searches for data based on this. The specific data calculation involves evaluating the degree of match with the prompt sentence and extracting highly relevant data. The output is visual data sent to the user's device.

[1183] Step 8:

[1184] The user device receives the data sent from the cloud server and plays it through the app. The input is visual data sent from the cloud server, which the user views on their smartphone or tablet. Specific operations include the app streaming the received data. The output is visual data displayed on the user device.

[1185] (Application example 1)

[1186] 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."

[1187] Conventional autonomous vehicles lack a mechanism for efficiently collecting visual information from the surroundings, analyzing and classifying it in real time, and providing driving assistance information. This has resulted in insufficient information provision to improve driving safety and efficiency. Furthermore, there is also a lack of effective means for replaying and searching past driving data, making it difficult to recreate accidents or review driving conditions.

[1188] 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.

[1189] In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generation AI, a driving assistance means that provides the analyzed visual data as driving assistance information, and a storage means that stores and provides the analyzed and classified visual data in a form that is easily accessible to users. This enables autonomous vehicles to collect and analyze visual information in real time and provide driving assistance information that improves safety and efficiency. Furthermore, the ability to effectively play back and search past driving data makes it easy to recreate accidents and review driving conditions.

[1190] "Visual data" is digital data that includes the user's visual experience and surrounding image information.

[1191] A "visual device" is a device worn by a user that captures visual data, and refers to a device that includes a camera, sensor, etc.

[1192] "Transfer means" refers to a communication means for transferring visual data acquired from a visual device to a cloud server, and includes wired communication and wireless communication.

[1193] A "cloud server" is a server system for storing, processing, and providing data via the Internet.

[1194] "Analysis means" refers to the function that analyzes visual data on a cloud server using AI to generate and classify and tag it.

[1195] "Generative AI" refers to programs and algorithms that use artificial intelligence technology to generate and analyze digital data.

[1196] "Driving assistance means" refers to functions that provide information and warnings necessary for driving based on analyzed visual data, improving safety and efficiency.

[1197] "Storage Means" refers to a storage device or system for storing analyzed and classified visual data and providing it in a form that is easily accessible to users.

[1198] "Search means" refers to a function that allows users to search for keywords or find and provide visual data based on specific conditions.

[1199] "Display means" refers to a device or screen that displays driving assistance information to the driver in real time based on analyzed visual data.

[1200] In implementing this invention, a system consisting of the following elements is used to enable the collection, analysis, driving assistance, and playback / search of visual data.

[1201] Hardware

[1202] 1. Visual devices:

[1203] Vehicle-mounted cameras: Capture visual data in real time.

[1204] Microphone: Collects audio data.

[1205] 2. User Device:

[1206] Smartphone or computer: To access and view the analyzed data.

[1207] 3. Cloud Server:

[1208] Cloud infrastructure: Stores, analyzes, and serves data.

[1209] software

[1210] 1. Means of transfer:

[1211] A communications protocol for transferring visual data to a cloud server via Wi-Fi or mobile data communications.

[1212] 2. Analysis method:

[1213] OpenCV: Used for visual data capture and image processing.

[1214] AWS S3: A storage service for storing captured visual data in the cloud.

[1215] AWS Rekognition: A service that analyzes image data in the cloud and performs labeling and classification.

[1216] 3. Driving assistance measures:

[1217] An interface that provides real-time safety information and warnings to drivers, based on data analyzed on a cloud server.

[1218] 4. Storage Method:

[1219] Use AWS S3 or other cloud storage services to store the analyzed data and make it easily accessible to users.

[1220] 5. Search methods:

[1221] A function that allows users to search and play back past visual data by keyword or condition, using natural language processing (NLP) technology.

[1222] Specific examples

[1223] Specific use cases include the following scenarios:

[1224] Self-driving vehicles in operation

[1225] 1. Data Collection:

[1226] While the autonomous vehicle is driving, a camera mounted on the vehicle captures visual data of the area ahead in real time, while a microphone also collects environmental sounds.

[1227] 2. Data Transfer:

[1228] The collected visual and audio data is transferred to a cloud server using Wi-Fi or mobile data.

[1229] 3. Data Analysis:

[1230] Once the visual data reaches the cloud server, it is analyzed using AWS Rekognition, which includes object detection and labeling, extracting information such as "pedestrians" and "traffic lights."

[1231] 4. Driving assistance:

[1232] Based on the analyzed data, the driver assistance system provides real-time warnings and guidance, for example, a warning that "there is a pedestrian ahead" is displayed to the driver.

[1233] 5. Data Storage and Retrieval:

[1234] The analyzed and classified visual data is stored in cloud storage such as AWS S3 so that it can be searched and played back later. When the user inputs a request such as "play back past driving data," the relevant data is searched for and played back from the cloud server.

[1235] Prompt Sentence Examples

[1236] As a concrete example of a prompt sentence, for example, enter the following prompt sentence for Amazon Rekognition:

[1237] "Analyze uploaded autonomous vehicle images to identify pedestrians, vehicles, traffic lights, and road signs. Label them and provide a confidence score."

[1238] The above system configuration and processing enable autonomous vehicles to efficiently collect and analyze visual data, provide driving assistance in real time, and effectively search and replay past driving data.

[1239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1240] Step 1: Capture visual data

[1241] A camera mounted on an autonomous vehicle captures visual data of the road ahead in real time. Specifically, the camera captures images frame by frame and temporarily stores them in memory. The input is the video frame acquired from the camera, and the output is the visual data stored in memory.

[1242] Step 2: Collecting audio data

[1243] The microphone collects environmental sounds and audio data from inside the vehicle. The microphone captures audio data in real time and temporarily stores it in memory. The audio data obtained from the microphone is the input, and the audio data stored in memory is generated as the output.

[1244] Step 3: Data Transfer

[1245] The acquired visual and audio data is transferred to a cloud server via Wi-Fi or mobile data communication. The input is the visual and audio data stored in the memory, and the output is the data transmitted to the cloud server. Specifically, the transmission means packetizes the data and transmits it to a specific address on the cloud server via the Internet.

[1246] Step 4: Data analysis

[1247] The cloud server analyzes the received visual and audio data. Specifically, it is stored in AWS S3 and analyzed by AWS Rekognition. The input is the visual and audio data stored on the cloud server, and the output is the analysis results. AWS generative AI models are used to detect, tag, and classify objects in the images.

[1248] Step 5: Generate driver assistance information

[1249] The cloud server generates driving assistance information based on the analysis results. The input is the analysis results of AWS Rekognition, and the output is the driving assistance information. Specifically, warning messages such as "There is a pedestrian ahead" or "The traffic light is red" are created.

[1250] Step 6: Providing driver assistance information

[1251] The cloud server generates driving assistance information and sends it to the vehicle's driving assistance system. The input is the driving assistance information generated by the cloud server, and the output is data provided to the driving assistance system. The driving assistance system displays this information to the driver in real time and provides warnings and guidance.

[1252] Step 7: Save Data

[1253] The analyzed and classified visual data is stored in cloud storage (AWS S3). The analyzed visual data is the input, and the data stored in cloud storage is generated as the output. This allows past driving data to be saved and later searched and played back.

[1254] Step 8: Data Retrieval

[1255] A user uses a smartphone application to search for past driving data. The input is keywords and conditions entered by the user, and the output is visual data as search results. Specifically, the cloud server searches the stored data based on the user's request and sends the relevant data to the user's device.

[1256] Step 9: Data playback

[1257] The user device plays back the visual data received from the cloud server. The input is the visual data sent from the cloud server, and the output is the video and audio displayed on the user device. The user can visually check past driving data on their smartphone or computer.

[1258] The above processing steps realize a system that collects and analyzes visual data in real time and provides driving assistance information. It also makes it possible to effectively search and replay past data.

[1259] 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.

[1260] ---

[1261] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[1262] Visual Devices

[1263] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[1264] Cloud Server and Generative AI

[1265] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[1266] Emotion Engine

[1267] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[1268] User terminal

[1269] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[1270] Specific examples

[1271] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[1272] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[1273] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[1274] Additionally, if a user experiences a moment of stress, that information is also tagged by the emotion engine. When a user requests a "relaxing scene" to relax, the generative AI will recommend appropriate visual data based on the emotion engine's data, providing a visual experience to reduce stress. Furthermore, if a visually impaired person is wearing a visual device, the generative AI will analyze the captured visual data and convert it into audio data. This audio data is sent to the user's audio output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device will capture visual information about the platform and output that information as audio, such as, "There are stairs on your right."

[1275] As such, this invention is a system that effectively records, organizes, and plays back visual information, providing an environment where users can easily manage and use visual data in a variety of usage scenarios.The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, realizing a more personalized experience.

[1276] ---

[1277] The processing flow will be explained below.

[1278] ---

[1279] Step 1:

[1280] The user wears the visual device, which begins capturing the user's visual information in real time. The visual device is equipped with a camera and audio sensor, which simultaneously captures video and audio data.

[1281] Step 2:

[1282] The device temporarily stores the visual data captured from the visual device in its internal memory, along with the date, time, and location information.

[1283] Step 3:

[1284] The device performs preprocessing on the data sent from the vision device, removing noise if necessary, thereby improving the quality of the vision data.

[1285] Step 4:

[1286] The device collects visual data as well as emotional data from the user, which is then recognized in real time by an emotion engine based on the user's facial expressions, voice, and brainwaves.

[1287] Step 5:

[1288] The device transfers visual and emotional data together to a cloud server, where the data is encrypted before being sent.

[1289] Step 6:

[1290] The server analyzes the received visual and emotional data, and the generation AI divides the visual data into frames and evaluates their relevance to the emotional data.

[1291] Step 7:

[1292] The server's generation AI evaluates the importance and interest of the visual data and tags it in conjunction with the emotional data. For example, tags such as "emotion," "surprise," and "joy" can be added.

[1293] Step 8:

[1294] The server stores the analyzed and tagged visual data in a storage means, including the user's emotional state.

[1295] Step 9:

[1296] A user requests specific visual data through a smartphone app, for example, a "relaxing landscape."

[1297] Step 10:

[1298] The server receives the user's request, and the generation AI searches for the most suitable visual data based on the emotion engine data. The search results are filtered based on the user's emotional state.

[1299] Step 11:

[1300] The server provides the search results to the user's device in streaming or download format, and the user plays the visual data on their visual device or smartphone.

[1301] Step 12:

[1302] When a visually impaired person uses a visual device, the device captures visual information in real time and transmits it to a cloud server, which then analyzes the visual data and converts it into audio data.

[1303] Step 13:

[1304] The server generates voice data and sends it to the device of the visually impaired person in real time, allowing the visually impaired person to receive explanations and guidance about the environment in real time through the voice output means.

[1305] ---

[1306] The above is the specific processing flow when combining the "Vision Bank" system with an emotion engine.

[1307] Example 2

[1308] 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."

[1309] Conventional visual data management systems have difficulty efficiently recording, organizing, and replaying the visual information users acquire on a daily basis. Furthermore, they lack the ability to classify and recommend visual data based on the user's emotional state, making it difficult to provide a personalized experience. Furthermore, they lack a means for visually impaired people to intuitively understand visual data.

[1310] 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 a visual device means for capturing visual data, a transfer means for transferring the visual data transmitted from the visual device to a cloud server, an analysis means for analyzing and classifying the visual data on the cloud server using a generative AI, an emotion recognition means for associating emotion data with the analyzed and classified visual data, and a storage means for storing and providing the analyzed and classified visual data in a form easily accessible to the user. This enables efficient recording, organization, and playback of the user's visual information, and realizes the provision of personalized visual data based on the user's emotional state. Furthermore, the captured visual data can be converted into audio data to provide intuitively understandable information to visually impaired people.

[1311] "Visual data" refers to information such as images and environmental sounds that come into the user's field of vision.

[1312] "Vision device" refers to a device worn by a user that captures visual data.

[1313] "Transfer means" refers to a method or apparatus for transferring visual data sent from a visual device to a cloud server.

[1314] A "cloud server" refers to a server that stores and manages data on the Internet.

[1315] "Generative AI" refers to a system that uses artificial intelligence techniques to analyze and classify data.

[1316] "Analysis Means" means a method or apparatus for analyzing and classifying visual data using generative AI.

[1317] "Emotion recognition means" refers to a method or apparatus for obtaining and associating user emotion data with analyzed visual data.

[1318] "Storage means" refers to a method or device for storing the analyzed and categorized visual data and making it easily accessible to the user.

[1319] "Search means" refers to a method or device for searching the analyzed and classified visual data based on a user's keywords and providing related visual data.

[1320] "Recommendation means" refers to a method or device that recommends appropriate visual data based on the emotional state of a user.

[1321] "Audio conversion means" refers to a method or device that analyzes captured visual data and converts it into audio data.

[1322] "Audio output means" refers to a method or device for outputting audio data in real time.

[1323] This invention is a system for efficiently recording, organizing, and replaying visual data, and in particular, for providing personalized visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[1324] Visual Devices

[1325] The user wears a visual device. The visual device has the function of capturing visual information that comes into the user's field of vision in real time. Specifically, this includes eyeglass-type and contact lens-type devices. The visual device collects visual information and environmental sounds and temporarily stores them in its internal memory. This data is then transmitted to the terminal via wireless communication (Bluetooth or Wi-Fi).

[1326] Cloud Server and Generative AI

[1327] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it uses a generative AI to analyze the data. The generative AI uses a widely used artificial intelligence model (e.g., OpenAI's GPT-4). The generative AI divides the visual data into frames and runs an algorithm to evaluate the user's interest and importance. Through this analysis, the data is effectively classified and tagged with tags such as "tourist spot," "scenery," and "signpost." The analyzed data is stored in a cloud storage solution (e.g., Amazon S3) for easy access by users.

[1328] Emotion Engine

[1329] The emotion engine acquires the user's emotional data in real time and associates it with the analyzed visual data. The emotion engine uses technology (e.g., Affectiva's emotion recognition API) that analyzes the user's facial expressions, voice, and brain waves. This allows the visual data to be tagged with emotion tags such as "emotion" or "stress."

[1330] User terminal

[1331] A user device (e.g., a smartphone or computer) provides an interface for accessing visual data stored in the cloud server. By requesting specific visual data (e.g., "scenery of a tourist spot"), the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data is recommended based on the emotions recognized by the emotion engine.

[1332] Specific examples

[1333] When a user visits a tourist spot, the visual device captures the scenery and guide signs. This data is transferred to a cloud server via the smartphone, where the generation AI analyzes the data and assigns tags such as "tourist spot" and "scenery." For example, if a user experiences a "moving moment," the data is tagged "moving." When the user requests in the app to "play the scenery of the tourist spot I saw today," the cloud server searches for the relevant data and plays it on the smartphone.

[1334] Furthermore, if a user requests a "relaxing scene" when a tag has been added to a moment of stress, the generative AI will recommend and provide appropriate visual data. Furthermore, if a visually impaired person is wearing a visual device, the captured visual data is converted into audio data, and audio guidance is provided in real time. For example, information is provided in the form of "There are stairs on your right."

[1335] Prompt Sentence Examples

[1336] "Today I went to a tourist spot. Organize the data of the scenery and tag the moments that moved me."

[1337] "Please recommend a relaxing landscape."

[1338] In this way, the Vision Bank system effectively records, organizes, and plays back visual information, providing users with a personalized visual experience. The introduction of an emotion engine enables advanced visual data analysis and recommendation based on the user's emotional state, making it effective for a wide range of scenarios in daily life.

[1339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1340] Step 1:

[1341] A visual device captures video and audio data.

[1342] A visual device worn by the user captures surrounding video and audio data in real time. The visual device's camera and microphone acquire video frames and audio data as input data. These data are temporarily stored in the visual device's internal memory. Specific operations include capturing video at 30 frames per second and recording environmental sounds in stereo.

[1343] Step 2:

[1344] The device transfers the captured data to a cloud server.

[1345] The data stored in the internal memory of the visual device is retrieved and transferred to a terminal (e.g., a smartphone). The input data, which the terminal transfers using Bluetooth or Wi-Fi, is the video and audio data received from the visual device. The terminal receives this data and uploads it to a cloud server using the HTTPS protocol. Specific operations include data encryption and secure transfer.

[1346] Step 3:

[1347] The cloud server uses generated AI to analyze and classify the data.

[1348] The cloud server receives video and audio data as input, and uses a generative AI (e.g., GPT-4 model) to analyze and classify the data frame by frame. Specifically, the video data is first analyzed using an image recognition algorithm (e.g., YOLOv5) to identify important objects and scenes. The generative AI then uses natural language processing to assign appropriate tags (e.g., "tourist spot," "scenery," "signpost") to the identified objects and scenes. The output data is the analyzed and classified visual data and its tags.

[1349] Step 4:

[1350] The cloud server associates the emotion data using an emotion engine.

[1351] The emotion engine acquires the user's facial expressions, voice, and brainwave data in real time and recognizes emotional information as input data. The cloud server associates this with visual data. Specifically, the user's emotions (e.g., "emotion" or "surprise") are added as tags from information acquired using Affectiva's emotion recognition API. The output data includes visual data tagged with emotions.

[1352] Step 5:

[1353] The cloud server stores the analyzed and classified data in storage.

[1354] The cloud server stores the analyzed and classified visual data and its tags in a storage medium (e.g., Amazon S3). The input data is the analyzed and classified visual data and its tags, and measures are taken to store it safely and efficiently. Specific actions include data redundancy and backup.

[1355] Step 6:

[1356] A user accesses the visual data using a terminal.

[1357] A user launches a smartphone app and sends a request to the cloud server to request specific visual data. For example, they input a prompt such as "Play the scenery of the tourist spots I saw today." The input data includes the user's search keywords and emotional state. Based on this, the cloud server searches for relevant visual data and outputs it to the user's device in the form of streaming playback. Specific operations include filtering the search results and selecting appropriate data.

[1358] Step 7:

[1359] The cloud server makes recommendations based on the user's emotional state.

[1360] Based on the user's emotional state obtained by the emotion engine, the generative AI recommends appropriate visual data. For example, if a user requests a "relaxing landscape," past emotional data is referenced and visual data that will reduce stress is selected. The input data includes the user's emotional history and current emotional state, and the recommended visual data is provided as output data. Specific operations include searching the user's history and applying the recommendation algorithm.

[1361] In this way, the Vision Bank system effectively records, analyzes and plays back users' visual information, providing a personalized visual experience based on their emotional state.

[1362] (Application example 2)

[1363] 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."

[1364] Today, there is a need for efficient digitization and easy access to the information and visual experiences users see on a daily basis. However, simply storing visual data makes it difficult for users to replay emotionally significant visual experiences or use them effectively at the appropriate time. Furthermore, there are insufficient means for visually impaired people to access visual information as audio data in real time. Providing personalized visual information based on the user's emotional state would provide a richer user experience, but this is currently difficult to achieve.

[1365] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a visual device that captures visual data, a transfer means that transfers the visual data transmitted from the visual device to a cloud server, an analysis means that analyzes and classifies the visual data on the cloud server using a generative AI, a storage means that stores and provides the analyzed and classified visual data in a form easily accessible to the user, an emotion recognition means that recognizes the user's emotional data in real time and associates it with the visual data, and a recommendation means that recommends related visual data based on the user's request and emotional state. This allows users to efficiently manage their daily visual experiences and use optimal visual data and content according to their emotional state. Furthermore, visual information captured in real time can be provided as audio data to visually impaired people, making the use of visual information more effective.

[1366] A "vision device" is a device worn by a user to capture visual data.

[1367] "Transfer means" refers to a means for transmitting visual data from a visual device to a cloud server.

[1368] A "cloud server" is a server that stores and manages data over the Internet.

[1369] "Generative AI" is an artificial intelligence technique for analyzing and classifying visual data.

[1370] "Analysis means" refers to a means for analyzing and classifying visual data on a cloud server using a generative AI.

[1371] "Storage means" means for storing and providing analyzed and classified visual data in a form that is easily accessible to the user.

[1372] The "emotion recognition means" is a means for recognizing the user's emotion data in real time and associating it with visual data.

[1373] A "recommendation means" is a means for recommending relevant visual data based on a user's request and emotional state.

[1374] The "search means" is a means for searching the analyzed and classified visual data based on the user's keywords and providing the data to the user terminal.

[1375] The "audio conversion means" is a means for analyzing visual data captured by a visually impaired person and converting it into audio data.

[1376] The "audio output means" is a means for outputting audio data in real time.

[1377] This invention is a system for efficiently recording, organizing, and replaying visual data, digitizing the information and visual experiences that users see on a daily basis and providing them in an easily accessible format. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, it is possible to provide optimal visual data based on the user's emotional state. This system is realized by combining the following elements: a visual device, a cloud server, a generative AI, an emotion engine, and a user terminal.

[1378] Visual Devices

[1379] The user wears a visual device (e.g., glasses or contact lenses). The visual device has the function of capturing video information that falls within the user's field of vision in real time. In addition, the visual device can simultaneously capture audio data such as environmental sounds. The visual device temporarily stores the captured data in its internal memory and transfers it to a cloud server as needed.

[1380] Cloud Server and Generative AI

[1381] The device transfers the visual data sent from the visual device to a cloud server. Once the cloud server receives the visual data, it analyzes the data using a generative AI. The generative AI breaks the visual data down into frames and runs algorithms to assess the user's interest and importance. This analysis process effectively categorizes and tags the data. The analyzed data is then stored in a storage means for easy user access.

[1382] Emotion Engine

[1383] The emotion engine has the ability to recognize emotional data in real time from the user's facial expressions, voice, brain waves, etc. The emotional data recognized by the emotion engine is associated with the visual data captured from the vision device, which further improves the accuracy of the analysis and classification of visual data based on the user's emotional state.

[1384] User terminal

[1385] A user device (such as a smartphone or computer) provides an interface for accessing visual data stored on the cloud server. A user can request specific visual data through a smartphone app. For example, by entering a keyword such as "scenery of a tourist spot," the cloud server searches for related visual data and provides it to the user device. Furthermore, related visual data can be recommended based on the user's emotional state.

[1386] Specific examples

[1387] As a specific example of use, consider a scenario in which a user is visiting a tourist spot. When the user is wearing a visual device, the device captures visual information such as scenery and guide signs of the tourist spot in real time. The captured data is transferred to a cloud server via the device. On the cloud server, a generating AI analyzes the visual data and organizes and classifies it as information about the tourist spot.

[1388] Furthermore, the user's emotion engine recognizes the emotion of the moment and tags the data. If a user has a moment that makes them feel particularly moved, the visual data of that moment will be saved with the tag "emotion."

[1389] After returning home, the user launches the smartphone app and inputs a request such as "Play the scenery of the tourist spots I saw today." Based on this request, the cloud server searches for related visual data and streams it to the smartphone. This allows the user to visually re-experience the scenery and guide they saw at the tourist spots.

[1390] Additionally, if the user experiences a moment of stress, that information will also be tagged by the emotion engine. If the user requests a "relaxing scene" when they want to relax, the generative AI will recommend appropriate visual data based on the emotion engine data, providing a visual experience that will reduce stress.

[1391] Furthermore, if a visually impaired person is wearing a visual device, the AI ​​analyzes the captured visual data and converts it into voice data. This voice data is sent to the user's voice output device in real time and provided as an explanation of the environment or guidance information. For example, if a visually impaired person is standing on a train platform, the visual device captures the visual information of the platform and outputs that information as voice, such as "There are stairs on your right."

[1392] Example prompts to input to a generative AI model:

[1393] Which beach videos would you recommend if a user is feeling relaxed?

[1394] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1395] Step 1:

[1396] The user wears a visual device, which captures visual data from the surroundings. The input is camera video and audio data, and the output is raw video and audio data acquired in real time. The visual device temporarily stores these data in its internal memory.

[1397] Step 2:

[1398] The visual and audio data captured by the vision device is sent to the cloud server. The input is the captured visual and audio data, and the output is a notification of the completion of data transfer to the cloud server. The device uses the data transfer function to upload the stored data to the cloud server.

[1399] Step 3:

[1400] The cloud server analyzes the received visual data using a generative AI model. The input is the visual data transferred to the cloud server, and the output is the analyzed and frame-classified data. The generative AI model divides the data into frames, evaluates the user's interest and importance, and assigns appropriate tags.

[1401] Step 4:

[1402] The analyzed and classified data is stored in a storage on a cloud server. The input is the analyzed and tagged visual data, and the output is a notification that the data has been saved to the cloud storage. The cloud server uses a storage means to save the data so that the user can access it later.

[1403] Step 5:

[1404] The emotion engine recognizes emotion data in real time from the user's facial expressions, voice, brain waves, etc. The input is the user's facial expressions, voice, and brain wave data, and the output is the recognized emotion data. The emotion engine determines the user's emotional state from this data and associates it with the visual data.

[1405] Step 6:

[1406] A user requests specific visual data through a smartphone app. The input is the user's search query or keywords, and the output is the provision of related visual data to the user's smartphone device. The user uses the app to input keywords such as "tourist scenery" or "relaxing scenery."

[1407] Step 7:

[1408] The cloud server recommends relevant visual data based on the user's emotional state and request. The input is a search query and emotional data, and the output is a list of recommended visual data. The server uses the recommendation mechanism to select the most suitable visual data according to the user's emotions and interests.

[1409] Step 8:

[1410] The recommended visual data is streamed to the user's smartphone. The input is the recommended visual data, and the output is the playback video of the visual data on the smartphone. The user can view the visual data in the app and enjoy the played video and audio.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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).

[1418] 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.

[1419] 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."

[1420] 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.

[1421] 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).

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] The following is further disclosed regarding the above embodiment.

[1433] (Claim 1)

[1434] a visual device for capturing visual data;

[1435] a transfer means for transferring the visual data transmitted from the visual device to a cloud server;

[1436] An analysis method that uses AI to analyze and classify visual data stored on a cloud server;

[1437] a storage means for storing and providing the analyzed and classified visual data in a form that is easily accessible to a user;

[1438] A system including:

[1439] (Claim 2)

[1440] 2. The system according to claim 1, further comprising: a search means for searching the analyzed and classified visual data based on a user's keyword and providing the searched and classified visual data to a user terminal.

[1441] (Claim 3)

[1442] 2. The system according to claim 1, wherein the visual device comprises a voice conversion means for analyzing captured visual data and converting it into voice data for a visually impaired person, and a voice output means for outputting the voice data in real time.

[1443] "Example 1"

[1444] (Claim 1)

[1445] a visual device for capturing visual data;

[1446] a transfer means for transferring the visual data transmitted from the visual device to a terminal;

[1447] a transmitting means for uploading visual data from the terminal to a cloud server;

[1448] An analysis means for analyzing and classifying the visual data on the cloud server using a generative AI model;

[1449] a storage means for tagging the analyzed and classified visual data and storing and providing the same;

[1450] A system including:

[1451] (Claim 2)

[1452] 2. The system according to claim 1, further comprising a search means for searching the analyzed and classified visual data based on a prompt sentence entered by a user and providing the searched and classified visual data to a user terminal.

[1453] (Claim 3)

[1454] 2. The system according to claim 1, wherein the visual device comprises a voice conversion means for analyzing captured visual data and converting it into voice data for a visually impaired person, and a voice output means for outputting the voice data in real time.

[1455] "Application Example 1"

[1456] (Claim 1)

[1457] a visual device for capturing visual data;

[1458] a transfer means for transferring the visual data transmitted from the visual device to a cloud server;

[1459] An analysis method that uses AI to analyze and classify visual data stored on a cloud server;

[1460] a driving assistance means for providing the analyzed visual data as driving assistance information;

[1461] a storage means for storing and providing the analyzed and classified visual data in a form that is easily accessible to a user;

[1462] A system including:

[1463] (Claim 2)

[1464] 10. The system of claim 1, further comprising: a search means having a function for searching and playing back visual data that has been previously recorded.

[1465] (Claim 3)

[1466] 2. The system according to claim 1, further comprising a display means for providing information and warnings necessary for driving in real time based on the analyzed visual data.

[1467] "Example 2: Combining Emotion Engines"

[1468] (Claim 1)

[1469] a visual device for capturing visual data;

[1470] a transfer means for transferring the visual data transmitted from the visual device to a cloud server;

[1471] An analysis method that uses AI to analyze and classify visual data stored on a cloud server;

[1472] an emotion recognition means for associating emotion data with the analyzed and classified visual data;

[1473] a storage means for storing and providing the analyzed and classified visual data in a form that is easily accessible to a user;

[1474] A system including:

[1475] (Claim 2)

[1476] a search means for searching the analyzed and classified visual data based on a user's keyword and providing the user with relevant visual data;

[1477] a recommendation means for recommending visual data based on the emotional state of the user;

[1478] The system of claim 1 further comprising:

[1479] (Claim 3)

[1480] a voice conversion means for converting the captured visual data into voice data by analyzing the captured visual data;

[1481] 2. The system according to claim 1, further comprising an audio output means for outputting audio data in real time.

[1482] "Application example 2 when combining emotion engines"

[1483] (Claim 1)

[1484] a visual device for capturing visual data;

[1485] a transfer means for transferring the visual data transmitted from the visual device to a cloud server;

[1486] An analysis method that uses AI to analyze and classify visual data stored on a cloud server;

[1487] a storage means for storing and providing the analyzed and classified visual data in a form that is easily accessible to a user;

[1488] emotion recognition means for recognizing user emotion data in real time and associating it with visual data;

[1489] A recommendation method that recommends relevant visual data based on a user's request and emotional state

[1490] A system including:

[1491] (Claim 2)

[1492] The system according to claim 1, further comprising: a search means for searching the analyzed and classified visual data based on a user's keyword and providing the searched and classified visual data to the user terminal; and a recommendation means for recommending related visual data based on emotion data obtained by the emotion recognition means.

[1493] (Claim 3)

[1494] 2. The system according to claim 1, wherein the visual device comprises a voice conversion means for analyzing captured visual data and converting it into voice data for a visually impaired person, and a voice output means for outputting the voice data in real time. [Explanation of symbols]

[1495] 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 visual device for capturing visual data; a transfer means for transferring the visual data transmitted from the visual device to a cloud server; An analysis method that uses AI to analyze and classify visual data stored on a cloud server; a storage means for storing and providing the analyzed and classified visual data in a form that is easily accessible to a user; A system including:

2. 2. The system according to claim 1, further comprising: a search means for searching the analyzed and classified visual data based on a keyword entered by a user and providing the searched and classified visual data to a user terminal.

3. 2. The system according to claim 1, wherein the visual device comprises a voice conversion means for analyzing captured visual data and converting it into voice data for visually impaired people, and a voice output means for outputting voice data in real time.

Images