System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring video data from a vision-assist device worn by a user; means for encrypting the video data and transmitting the encrypted video data to a cloud server; means for analyzing the video data received on the cloud server; means for generating visual aid information based on an analysis result; means for re-encrypting the generated visual aid information and returning the re-encrypted visual aid information to the user; and means for displaying the returned visual aid information in a field of view of the user.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] When using wearable devices such as smart glasses for visual assistance or route guidance, there are risks of privacy violations, such as unauthorized use of video data or voyeurism. Furthermore, the risk of data leakage increases as data is stored on the user's device. There is a need to solve these problems and provide a safe and secure visual assistance system. [Means for solving the problem]

[0005] This invention provides a system that acquires video data from a visual aid device worn by a user, encrypts it, and sends it to a cloud server. The cloud server analyzes the received video data using an AI model to generate visual aid information. The generated visual aid information is re-encrypted and sent back to the user's visual aid device. The user's visual aid device decrypts the received visual aid information and displays it superimposed on the user's field of view. As a result, data is not stored on the user's device, reducing the risk of privacy violations and data leaks.

[0006] A "visual aid device" is a wearable device worn by a user, and has the function of collecting images of the surroundings and displaying visual aid information.

[0007] "Video data" refers to digital data representing visual information of the surrounding environment captured by the camera of the visual aid device.

[0008] "Encryption" is a security technology that converts information using a certain algorithm to make it unreadable to third parties.

[0009] A "cloud server" is a remote server that provides services and data over the Internet and is a device capable of processing and storing large amounts of data.

[0010] An "AI model" is an algorithm that uses techniques such as machine learning and deep learning to perform specific tasks.

[0011] "Analysis" is the process of extracting meaning and patterns from acquired data using specific rules and algorithms, and organizing and interpreting the information.

[0012] "Visual auxiliary information" is auxiliary information that is generated based on the analysis results and that supports the user's visual perception.

[0013] "Re-encryption" is the process of decrypting data that has already been encrypted and then encrypting that data again.

[0014] A "secure communication protocol" is a standardized communication procedure for securely sending and receiving data, and has the function of preventing unauthorized access and data tampering. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] Overview of the entire system

[0037] This invention is a system that uses a visual aid device worn by the user to collect surrounding video data, analyze it via a cloud server, and provide visual aids. Therefore, a wearable device such as smart glasses works in conjunction with a cloud-based AI analysis system. Specifically, it includes the following main elements:

[0038] 1. Visual aids (smart glasses)

[0039] 2. Cloud Server

[0040] 3. Communication Protocol

[0041] 4. AI Analysis Model

[0042] Program processing explanation

[0043] Visual aids (user's device)

[0044] The visual aid device worn by the user is equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. The user's device does not store any of the video data; all processing is done on the cloud server.

[0045] Video capture and encryption

[0046] The camera in the smart glasses captures the surrounding environment and collects video data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0047] Processing on the cloud server (server)

[0048] The cloud server receives and decrypts the encrypted video data sent from the user's device. The AI ​​model then analyzes the decrypted video data. The AI ​​model has facial, object, and text recognition capabilities, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[0049] Generation of visual aids

[0050] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, or AR advertisements for the user. The generated visual aid information is then encrypted again and sent back to the user's visual aid device.

[0051] Displaying information (user's terminal)

[0052] The user's visual aid device receives and decodes the visual aid information sent back from the cloud server. Finally, the decoded visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0053] Specific examples

[0054] For example, a visually impaired user may be using the system to navigate down a street.

[0055] 1. Video acquisition and transmission

[0056] The user wears the smart glasses and the camera captures images of the surrounding area.

[0057] The encrypted video data is sent to a cloud server.

[0058] 2. Analysis on the cloud server

[0059] The server receives the video data, decodes it, and performs AI analysis.

[0060] AI analysis recognizes important objects such as cars, pedestrians, and traffic lights.

[0061] 3. Generation and transmission of visual aids

[0062] Based on the recognition results, visual aid information including warning information for the user and the location of obstacles is generated.

[0063] The visual aid information is encrypted and sent back to the smart glasses.

[0064] 4. Displaying Information

[0065] The smart glasses receive and decode the visual aid information.

[0066] Auxiliary information is displayed superimposed on the user's field of vision to assist in safe walking.

[0067] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user's visual aid (smart glasses) is activated and the camera captures the surroundings in real time. The camera continuously captures images while the user is walking.

[0071] Step 2:

[0072] Video data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[0073] Step 3:

[0074] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video data.

[0075] Step 4:

[0076] The AI ​​model on the server analyzes the decoded video data and has facial, object, and character recognition capabilities to identify specific landmarks and obstacles.

[0077] Step 5:

[0078] The server generates visual aids based on the analysis results, including obstacle warnings, route guidance, and AR advertisements.

[0079] Step 6:

[0080] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[0081] Step 7:

[0082] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[0083] Step 8:

[0084] The user's visual aid device then displays the decoded visual aid information overlaid on the user's field of vision, allowing the user to view the necessary visual aid information in real time.

[0085] As a concrete example, consider a visually impaired user using the system while walking. The user's smart glasses capture the scenery ahead, and the cloud server analyzes it, identifying a traffic light ahead that is red. This information is encrypted and sent back to the user, where it is displayed as a red light on the smart glasses' display. This allows the user to stop and move safely.

[0086] Example 1

[0087] 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."

[0088] There is a need for an efficient method of providing information to visual aid devices that enable visually impaired users to move around safely. Conventional visual aid devices have difficulty performing highly accurate analysis in real time, and lack a mechanism for providing information while maintaining user privacy. This necessitates the development of a visual aid system that is user-friendly and enhances safety.

[0089] 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.

[0090] In this invention, the server includes a cloud server that decrypts data, a cloud server that encrypts the generated visual aid information, and a cloud server that analyzes the video data using an AI model. This enables the provision of highly accurate visual aid information to visually impaired users in real time, and enhances safety while protecting the user's privacy.

[0091] A "visual aid" is a wearable device that is worn by the user to supplement visual information and assist in safe movement.

[0092] "Video data" refers to visual information of the surrounding environment captured in real time by the camera of the visual aid device.

[0093] "Encryption" is a process that uses a special algorithm to convert acquired video data into a form that cannot be deciphered by others.

[0094] A "cloud server" is a server with computing resources in a remote location that can be accessed via a network, and is used to analyze, store, and communicate data.

[0095] "Analysis" is the process in which the cloud server processes the video data to extract and identify the necessary information.

[0096] "Visual aid information" is information that is generated based on analyzed video data and is used to aid the user's vision.

[0097] A "secure communication protocol" refers to a secure communication method that prevents eavesdropping or tampering by third parties when sending and receiving data.

[0098] An "AI model" is a computer program based on artificial intelligence algorithms used to analyze video data.

[0099] "Decryption" is the process of restoring encrypted data to its original form.

[0100] The present invention is a system that uses a visual aid device worn by a user to collect video data of the surroundings, analyzes it via a cloud server, and provides visual aid means. The overall configuration of the system is described in detail below.

[0101] Visual aids (user's device)

[0102] The visual aid worn by the user is a wearable device such as smart glasses. This visual aid is equipped with a camera, a communication module, and a display, and captures images of the surroundings in real time using the camera. The captured image data is immediately encrypted using an encryption algorithm (e.g., AES). The encrypted data is then sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0103] Processing on the cloud server (server)

[0104] The cloud server receives and decrypts the encrypted video data sent from the user's device. The server then uses a decryption algorithm (e.g., AES) to restore the data to its original form. The AI ​​model then analyzes the decrypted video data. The AI ​​model has capabilities such as facial recognition, object recognition, and text recognition, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[0105] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, AR advertisements, etc. The generated visual aid information is then encrypted again and sent back to the user's visual aid device using a secure communication protocol (e.g., HTTPS).

[0106] Displaying information (user's terminal)

[0107] The user's visual aid device receives the visual aid information sent back from the cloud server and decodes it again using a decoding algorithm. Finally, the decoded visual aid information is displayed superimposed on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0108] Specific examples

[0109] For example, imagine a visually impaired user walking down the street using this system. The camera in the smart glasses captures images of the surrounding area, and the encrypted image data is sent to a cloud server. The cloud server decodes the received image data and uses an AI analysis model to recognize important objects such as cars, pedestrians, and traffic lights. Based on the recognition results, visual aid information is generated that includes warning information for the user and the location of obstacles, and the encrypted visual aid information is sent back to the smart glasses. The smart glasses receive the visual aid information, decode it, and display it overlaid on the user's field of vision, helping them walk safely.

[0110] Prompt Sentence Examples

[0111] "Please tell me how to support visually impaired users to walk safely by wearing a visual aid and using real-time analysis via a cloud server."

[0112] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1: Acquire video data

[0115] The camera in the visual aid worn by the user captures the surrounding environment in real time. The input data is a real-world image of the surroundings, which the camera captures as video data. For example, the camera captures images of cars and pedestrians as the user walks down the street.

[0116] Step 2: Encrypting the video data

[0117] The video data captured by the device is immediately encrypted using an encryption algorithm (e.g., AES). The input is the captured video data, and the output is the encrypted data. Specifically, the AES algorithm is applied to the captured video frames to encrypt them.

[0118] Step 3: Sending Encrypted Data

[0119] The device sends encrypted video data to the cloud server via a secure communication protocol (e.g., HTTPS). The input is the encrypted video data, and the output is the sent data. Specifically, the data is sent using HTTPS.

[0120] Step 4: Receiving Encrypted Data

[0121] The server receives the encrypted video data sent from the user's terminal. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the data is received via the network.

[0122] Step 5: Decrypt the data

[0123] The server decrypts the encrypted data it receives using a decryption algorithm (e.g., AES). The input is the encrypted video data, and the output is the original video data. Specifically, the data is decrypted using the AES algorithm.

[0124] Step 6: Data analysis using AI

[0125] The server analyzes the decoded video data using an AI model. The input is the decoded video data, and the output is the analysis result. Specifically, it uses face recognition and object recognition models to identify important objects in the video.

[0126] Step 7: Generate visual aids

[0127] The server generates visual support information based on the analysis results. The input is the analysis result of the AI ​​model, and the output is visual support information. Specific operations include generating warnings and guidance information according to the recognized object and situation.

[0128] Step 8: Encrypt the auxiliary information

[0129] The visual auxiliary information generated by the server is again encrypted using an encryption algorithm. The input is the visual auxiliary information, and the output is the encrypted visual auxiliary information. Specifically, the information is encrypted using the AES algorithm.

[0130] Step 9: Sending encrypted auxiliary information

[0131] The server sends encrypted visual aid information to the user's visual aid device using a secure communication protocol (e.g., HTTPS). The input is the encrypted visual aid information, and the output is the transmitted data. Specifically, the information is sent using HTTPS.

[0132] Step 10: Receiving auxiliary information

[0133] The device receives encrypted visual aid information sent from the cloud server. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the device receives information via the network.

[0134] Step 11: Decoding the auxiliary information

[0135] The terminal decrypts the received visual aid information using a decryption algorithm. The input is the encrypted visual aid information, and the output is the original visual aid information. Specifically, the information is decrypted using the AES algorithm.

[0136] Step 12: Displaying information in the field of view

[0137] The terminal displays the decoded visual aid information superimposed on the user's field of vision. The input is the decoded visual aid information, and the output is the aid information displayed in the user's field of vision. Specific operations include displaying warning messages and route guidance information on the display.

[0138] (Application example 1)

[0139] 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."

[0140] The main function of conventional visual aids is to provide users with information about their surrounding environment. However, these devices do not perform adequately in the field of security monitoring. In particular, they do not meet advanced security requirements such as identifying intruders and detecting abnormal behavior. Therefore, in order to improve safety, a system that provides users with security-related information in real time is required.

[0141] 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.

[0142] In this invention, the server includes a cloud server that identifies intruders and detects abnormal behavior, a means for providing warning information to users, and a means for analyzing video data using a generative AI model, which enables security monitoring to quickly receive real-time warnings and important information via users' devices.

[0143] A "visual aid device worn by a user" is a wearable device that provides visual information when worn by a user.

[0144] "Video data" refers to visual information of the surroundings captured by a device such as a camera.

[0145] "Encryption" is the process of transforming data so that it cannot be easily deciphered by third parties.

[0146] A "cloud server" is a remote server available via the Internet that has the ability to store and process large amounts of data.

[0147] "Analysis" is the process of extracting information from acquired data and drawing conclusions according to the purpose.

[0148] "Visual aids" refers to additional visual data provided to help a user obtain information safely and efficiently.

[0149] "Intruder identification" refers to a technology that identifies unauthorized individuals based on video data.

[0150] "Abnormal behavior detection" refers to the technology of recognizing behavior that deviates from normal behavior patterns.

[0151] "Warning information" is a message for notifying the user of urgent information.

[0152] A "generative AI model" is a model trained using machine learning techniques and used to analyze video data, etc.

[0153] A "secure communication protocol" is a communication protocol that prevents unauthorized access by third parties when sending and receiving data.

[0154] This invention is a system that uses a user-worn visual aid device to collect surrounding video data, analyze it via a cloud server, and provide security-related visual aids. Specifically, it includes the following main elements:

[0155] 1. Visual aids (smart glasses)

[0156] The smart glasses worn by the user are equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. No data is stored on the smart glasses themselves; all analysis and processing is done on the cloud server.

[0157] 2. Cloud Server

[0158] The cloud server receives and decrypts the encrypted video data sent from the user's device. It then analyzes the video data using a generative AI model. This AI model has functions such as identifying intruders, detecting abnormal behavior, and recognizing faces. Based on the analysis results, it generates visual aids or security warning information.

[0159] 3. Communication Protocol

[0160] Video data is sent and received using a secure communication protocol (HTTPS), which ensures data encryption and safe transmission.

[0161] 4. AI Analysis Model

[0162] The AI ​​analysis model on the cloud server is implemented using TensorFlow and OpenCV to analyze the video data, which detects suspicious behavior and abnormal movements.

[0163] 5. Generation of visual support information

[0164] Based on the analysis results, the cloud server generates visual aids or warning information, re-encrypts it, and sends it back to the user's smart glasses. The smart glasses then decrypt the received information and display it overlaid on the user's field of vision, allowing the user to obtain important security information in real time.

[0165] Specific examples

[0166] For example, consider the case where a security guard uses this system in a large commercial facility. The security guard wears smart glasses and patrols the facility. A camera captures video of the surrounding area, encrypts it, and sends it to a cloud server. The cloud server analyzes the video data and detects abnormal behavior (e.g., climbing over a wall or leaving a bag unattended). Based on this, a warning message is sent to the security guard and displayed on the smart glasses' display.

[0167] Prompt Sentence Examples

[0168] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[0169] In this way, the system according to the present invention can provide real-time visual aids to improve user safety in security surveillance.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] The user wears the smart glasses, and the camera in the visual aid device captures images of the surrounding area.

[0173] Input: Real-time video data acquired by the camera.

[0174] Output: Video data.

[0175] How it works: A high-resolution camera mounted on the smart glasses captures the user's surroundings in real time and generates the images as digital data.

[0176] Step 2:

[0177] The video data captured by the visual aid device's camera is immediately encrypted using an encryption algorithm (e.g., AES).

[0178] Input: Acquired video data.

[0179] Output: Encrypted video data.

[0180] How it works: The encryption module built into the smart glasses converts the captured video data into a secure format using an encryption algorithm such as AES.

[0181] Step 3:

[0182] The encrypted video data is sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0183] Input: Encrypted video data.

[0184] Output: Data sent to the cloud server.

[0185] Specific operation: The communication module of the smart glasses securely transmits encrypted video data to a cloud server using HTTPS.

[0186] Step 4:

[0187] The cloud server decrypts the received encrypted video data.

[0188] Input: Encrypted video data sent to the cloud server.

[0189] Output: Decoded video data.

[0190] Specific operation: A decryption module installed on the cloud server converts the video data sent in a secure format back into its original format.

[0191] Step 5:

[0192] The generated AI model on the cloud server analyzes the decoded video data.

[0193] Input: Decoded video data.

[0194] Output: Analysis results (identification of intruders, detection of abnormal behavior, etc.).

[0195] How it works: A generative AI model (e.g., TensorFlow, OpenCV) deployed on a cloud server analyzes video data and performs facial recognition and abnormal behavior detection. Specifically, it uses a pre-trained dataset to identify objects and people in the video.

[0196] Step 6:

[0197] The cloud server generates warning information based on the analysis results.

[0198] Input: Analysis results.

[0199] Output: Alert information (such as suspicious person alert messages and abnormal behavior notifications).

[0200] Specific operation: Based on the results of the AI ​​analysis model, a program on the server generates a warning message in a format that is easy for the user to understand. For example, the warning message might say, "Suspicious behavior has been detected. Please check for details."

[0201] Step 7:

[0202] The generated warning information is encrypted and transmitted to the user's visual aid.

[0203] Input: Warning information.

[0204] Output: The encrypted warning information sent to the smart glasses.

[0205] Specific operation: The cloud server re-encrypts the generated warning information and sends it to the user's visual aid device using a secure communication protocol.

[0206] Step 8:

[0207] The visual aid decodes the received warning information and displays it in the user's field of view.

[0208] Input: Encrypted warning information.

[0209] Output: Warning information displayed in the user's view.

[0210] Specific operation: A decryption module built into the smart glasses decrypts the warning information sent from the cloud server and displays it on the screen. For example, it might display information such as "There is a suspicious person in the facility. Please be careful."

[0211] Prompt Sentence Examples

[0212] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[0213] 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.

[0214] System Configuration

[0215] This invention is a system that acquires video data from a visual aid device worn by a user, sends it to a cloud server for analysis, and, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide visual aid information based on the user's emotional state.

[0216] Overview of the entire system

[0217] The system consists of the following elements:

[0218] 1. Visual aids (smart glasses)

[0219] 2. Cloud Server

[0220] 3. Communication Protocol

[0221] 4. AI Analysis Model

[0222] 5. Emotion Engine

[0223] Program processing explanation

[0224] Visual aids (user's device)

[0225] The visual aid device worn by the user is equipped with a camera, a communication module, a display, and a microphone. This device captures surrounding video and audio in real time, encrypts the data, and transmits it to a cloud server. The video and audio data is not stored on the user's device; all processing is done on the cloud server.

[0226] Video and audio capture and encryption

[0227] The smart glasses' camera and microphone capture the surrounding environment and collect video and audio data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0228] Processing on the cloud server (server)

[0229] A cloud server receives and decrypts the encrypted data sent from the user's device. An AI model then analyzes the decrypted data to recognize specific landmarks and obstacles. Visual aids are generated based on the analyzed data and the user's emotional state.

[0230] Emotion engine analysis and tuning

[0231] The cloud server is equipped with an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. For example, it performs facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed. The emotion engine can then appropriately adjust the visual support information based on the user's emotional state. For example, if the user is feeling stressed, it may prioritize the display of warning information.

[0232] Generation and transmission of visual aids

[0233] Visual aids are generated based on the analysis results. These include obstacle warnings, route guidance, AR advertisements, etc. The generated visual aids are then encrypted again and sent to the user's device.

[0234] Displaying information (user's terminal)

[0235] The user's visual aid device receives and decrypts the encrypted data sent back from the cloud server. Finally, the decrypted visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0236] Specific examples

[0237] For example, imagine a visually impaired user walking through a busy downtown area using this system. The user's smart glasses capture video of the area ahead and audio of the surrounding area, and these data are sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data, and an emotion engine recognizes the user's impatience from their voice tone and facial expression. As a result, the server prioritizes generating visual aids with high urgency (e.g., a message urging them to pause) and sends them to the smart glasses. The smart glasses overlay the received information on the user's field of vision, helping the user to act safely.

[0238] In this way, the system according to the present invention can provide useful visual aids in real time, while preserving privacy, and taking into account the user's emotional state.

[0239] The processing flow will be explained below.

[0240] Step 1:

[0241] The user's visual aid (smart glasses) is activated, and the camera and microphone capture the surrounding images and sounds in real time. The camera continuously captures images while the user is walking, and the microphone collects environmental sounds and the user's voice.

[0242] Step 2:

[0243] Video and audio data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[0244] Step 3:

[0245] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video and audio data.

[0246] Step 4:

[0247] The AI ​​analysis model on the server analyzes the decoded video and audio data, recognizing specific landmarks and obstacles from the video data and analyzing the user's tone of voice and surrounding sounds from the audio data.

[0248] Step 5:

[0249] The server's emotion engine determines the user's emotional state based on the analyzed data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed.

[0250] Step 6:

[0251] The server generates visual support information based on the analysis results and the user's emotional state, including obstacle warnings, route guidance, AR advertisements, and warnings and support based on the user's emotions.

[0252] Step 7:

[0253] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[0254] Step 8:

[0255] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[0256] Step 9:

[0257] The user's visual aid device then overlays the decoded visual aid information onto their field of vision, allowing them to see the necessary visual aid information in real time and act safely.

[0258] As a concrete example, consider a visually impaired user walking through a busy downtown area. The user's smart glasses capture the scenery ahead and the surrounding sounds, and send them to a cloud server. The server uses video analysis to recognize obstacles and audio analysis to detect that the user is nervous. As a result, the server generates a warning saying, "Obstacle ahead. Be careful," and sends it to the smart glasses. The smart glasses display this warning in the user's field of vision, allowing the user to safely avoid the obstacle.

[0259] Example 2

[0260] 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."

[0261] Currently, support systems for visually impaired users to navigate safely have limitations in their functionality. Furthermore, they do not provide visual support information that takes into account the user's emotional state, making it difficult to adequately alleviate the user's stress and tension. Therefore, there is a need for a system that provides real-time information about the surrounding environment while also presenting support information that corresponds to the user's emotional state.

[0262] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring video data and audio data from a visual aid device worn by a user, means for encrypting the video data and audio data and transmitting them to a cloud server, means for decrypting the video data and audio data received on the cloud server, means for analyzing the decrypted data using an AI model and recognizing specific landmarks and obstacles, means for analyzing the user's emotional state using the emotion engine and adjusting the visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, and means for displaying the returned visual aid information in the user's field of view. This makes it possible to provide safe, real-time visual aid information while taking the user's emotional state into consideration.

[0263] A "visual aid device" is a wearable device worn by a user, and includes a camera and a microphone to capture video and audio data of the surrounding area.

[0264] "Encryption" is the process of transforming data using a specific algorithm (e.g., AES) to make the data unintelligible to third parties.

[0265] "Decryption" is the process of returning encrypted data to its original state, making the data received by the cloud server ready for analysis.

[0266] A "cloud server" is a remote computing system that stores, processes, and analyzes data over a network.

[0267] An "AI model" is an algorithm or program that uses artificial intelligence to analyze data, and can analyze video and audio data to recognize specific landmarks and obstacles.

[0268] An "emotion engine" is a system for analyzing a user's emotional state, and is a technology for determining a user's emotional state by performing facial expression recognition and voice tone analysis.

[0269] "Visual support information" is information generated based on analyzed data, and includes obstacle warnings, route guidance information, AR advertisements, etc.

[0270] A "secure communication protocol" is a protocol that ensures security during data transfer (e.g., HTTPS).

[0271] "Landmarks" are specific important points or landmarks that an AI model recognizes.

[0272] An "obstacle" is an object or structure that may impede the user's movement and is recognized by the AI ​​model.

[0273] In this invention, the visual aid device worn by the user (hereinafter referred to as the terminal) plays a key role. The terminal is equipped with a camera, microphone, communication module, and display, and captures video and audio data in real time. The video and audio data are immediately encrypted using the AES encryption algorithm and transmitted to a cloud server via the secure HTTP protocol (HTTPS).

[0274] The cloud server decrypts the received encrypted data and prepares it for analysis. An AI analysis model within the cloud server analyzes the decrypted data and recognizes specific landmarks and obstacles. An emotion engine also analyzes the video and audio data to determine the user's emotional state. This allows the cloud server to determine whether the user is currently feeling stressed or relaxed.

[0275] The cloud server then generates visual support information based on the data analysis and the emotional state analysis results. The generated visual support information includes location information of specific landmarks and warning messages about obstacles. Furthermore, the priority of information is adjusted taking into account the results of the emotion engine. For example, if the user is feeling stressed, information with a high level of urgency will be provided first.

[0276] The generated visual aid information is then AES encrypted and sent to the device. The user's device receives the data, decrypts it, and then displays the visual aid information overlaid on the display. This allows the user to simultaneously view the actual environment and the aid information.

[0277] As a concrete example, consider a scenario in which a visually impaired user is walking through a busy shopping district. The device's camera captures video of the area ahead, and its microphone collects surrounding audio. This data is encrypted and sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data. At the same time, an emotion engine analyzes the user's emotional state and identifies that the user is feeling impatient. As a result, a warning message such as "Obstacle ahead. Please stop moving" is generated, re-encrypted, and sent to the device. The device displays this information on its display, helping the user navigate safely.

[0278] Examples of prompts include:

[0279] "In this system, a visually impaired user wears smart glasses and transmits video and audio of the surrounding area to a cloud server for analysis. The cloud server analyzes the video and audio data and generates visual support information based on the user's emotional state. This information is sent to the user's smart glasses and displayed together with real-world information. As a specific example, please explain a case where the cloud server prioritizes the generation of visual support information with high urgency."

[0280] The present invention makes it possible to provide useful visual aid information in real time while taking into account the user's emotional state.

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1:

[0283] The user wears a visual aid, and the device's camera and microphone capture the surroundings in real time. The input is video and audio data. This data is immediately encrypted using the AES encryption algorithm. The encrypted data is sent to a cloud server using the HTTPS protocol. The output is encrypted video and audio data.

[0284] Step 2:

[0285] The cloud server receives the encrypted data sent from the device. The decryption module in the server decrypts the data using the AES algorithm. The input is the encrypted data, and the output is the decrypted video and audio data. This data is stored for analysis by the AI ​​model.

[0286] Step 3:

[0287] The AI ​​analysis model on the cloud server analyzes and recognizes specific landmarks and obstacles based on the decoded video data. The input is the decoded video data, and the output is the landmark and obstacle information as an analysis result. The AI ​​model detects landmarks and obstacles in each frame and identifies the user's location and direction of travel.

[0288] Step 4:

[0289] The emotion engine on the cloud server analyzes the decoded audio and video data to determine the user's emotional state. The input is the decoded audio and video data, and the output is a judgment of the user's emotional state. The emotion engine uses facial expression recognition algorithms and voice tone analysis to determine whether the user is stressed or relaxed.

[0290] Step 5:

[0291] The cloud server generates visual support information based on the results of data analysis and emotion analysis. The input is landmark and obstacle information and the user's emotional state, and the output is adjusted visual support information. For example, if the user is feeling stressed, priority is given to information with a high level of urgency. The generated visual support information includes obstacle warnings and route guidance information.

[0292] Step 6:

[0293] The cloud server re-encrypts the generated visual aid information using AES and sends it to the user's device. The input is the generated visual aid information, and the output is the encrypted visual aid information. The encrypted data is sent to the device via the HTTPS protocol.

[0294] Step 7:

[0295] The user's device receives and decrypts the encrypted data sent from the cloud server. The input is the encrypted visual aid information, and the output is the decrypted visual aid information. The decrypted information is finally overlaid on the display and displayed in the user's field of view. This allows the user to simultaneously view the real environment and the visual aid information.

[0296] As a specific example of how it works, after a user puts on the visual aid device, information about obstacles and landmarks ahead is acquired and analyzed in real time as the user walks through a busy shopping district, and warnings and guidance based on the results are displayed in the user's field of vision, helping the user to move around safely.

[0297] (Application example 2)

[0298] 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."

[0299] Current visual aid devices can acquire and analyze a user's visual information, but it is difficult to consider the user's emotional state. As a result, they are unable to provide appropriate visual aid information based on the user's emotions, which increases the burden on the user, especially in stressful situations or complex environments. Another issue is that they are unable to provide appropriate aid information to make shopping more comfortable for users in physical stores.

[0300] The specific processing by the specific 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 means for acquiring video data from a visual aid device worn by a user, means for encrypting the video data and transmitting it to a cloud server, means for analyzing the received video data on the cloud server, means for generating visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, means for displaying the returned visual aid information in the user's field of view, and means for adjusting the visual aid information based on the user's emotional state. This makes it possible to provide appropriate visual aid information in real time according to the user's emotions, thereby alleviating user stress and improving the shopping experience, particularly in physical stores.

[0301] A "visual aid device" is a device worn by a user to provide visual assistance, and is equipped with a camera, a communication module, a display, etc.

[0302] "Video data" refers to video information of the surroundings captured by the camera of the visual aid device.

[0303] "Encryption" refers to the process of protecting video and other data using security protocols.

[0304] A "cloud server" is a remote computer system that exists on the Internet and stores, analyzes, and processes data.

[0305] An "AI model" is a computational model that uses algorithms such as machine learning or deep learning to analyze data and recognize specific patterns or features.

[0306] "Visual supplementary information" refers to supplementary visual information provided to the user, and includes route guidance information, obstacle warnings, product information, and the like.

[0307] An "emotion engine" is software or an algorithm that analyzes video and audio data to identify and analyze the user's emotional state.

[0308] A "security communication protocol" is a set of communication rules for safely sending and receiving data, and uses encryption technology to protect data.

[0309] System Configuration

[0310] This system acquires video data from a visual aid device, encrypts it, and sends it to a cloud server. The cloud server analyzes the received data and generates visual aid information based on the analysis results. The generated information is then re-encrypted and sent back to the user, who then displays it in the field of view of the visual aid device worn by the user. This allows the user to simultaneously view the real world and the aid information.

[0311] Hardware and software used

[0312] Visual aids: Wearable devices equipped with a camera, communication module, display, and microphone. Specifically, smart glasses (e.g., Google Glass) are used.

[0313] Cloud server: A remote computer system for data analysis and emotion recognition. AWS, Google Cloud, etc. are used.

[0314] Cryptography library: Uses Fernet from the cryptography package to ensure data security.

[0315] Communication protocol: HTTPS protocol is used to ensure secure data transmission.

[0316] Acquisition and transmission of video data

[0317] When a user wears the visual aid, the device's camera captures real-time images of the surroundings and instantly encrypts the data, which is then sent to a cloud server via a secure communication protocol.

[0318] Data analysis using a cloud server

[0319] The cloud server decrypts the received encrypted data and analyzes it using an AI model. This analysis identifies specific landmarks, obstacles, and in-store product information. The cloud server also incorporates an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. This analysis is achieved, for example, by performing facial expression recognition and voice tone analysis.

[0320] Creating and providing visual support information

[0321] Visual aids are generated based on the user's emotional state and the results of video analysis. This visual aid information includes obstacle warnings, route guidance, in-store product and sale information, etc. The generated information is then re-encrypted and sent back to the user's visual aid device.

[0322] Displaying Information

[0323] The visual aid device receives the encrypted data, decrypts it, and displays it in the user's field of view. This allows the user to simultaneously view the real-world image and the auxiliary information. This can be particularly useful in brick-and-mortar stores, where the aid information can be provided to make shopping more comfortable for the user.

[0324] Specific examples

[0325] For example, when a user visits a shopping mall, a visual aid device (smart glasses) captures the product shelves in the store. The cloud server recognizes these products and, based on that information, displays the product the user is looking for or recommended products as visual aid information. If the user shows a favorable expression toward a particular product, the emotion engine detects this and displays special sale information or discount coupons for that product.

[0326] Prompt Sentence Examples

[0327] "Identify products in a store and analyze the user's emotional state from video and audio data captured by the user wearing a visual aid. If the user is unsure, recommend products, or offer discounts if the user likes a particular product."

[0328] In this way, the system of the present invention can provide useful visual aids in real time while properly taking into account the user's emotional state.

[0329] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0330] Step 1:

[0331] The user wears a visual aid device, and the device's camera captures images of the surroundings. This data is input to the terminal as image data. The terminal immediately encrypts this image data. The encryption technology used is a high-security encryption algorithm such as AES. After encryption, the encrypted image data is sent to a cloud server by a communication module. The input is image data of the real environment, and the output is encrypted image data.

[0332] Step 2:

[0333] The cloud server receives the encrypted data. The server decrypts the received data and retrieves the original video data. Decryption is performed using a key shared between the sites. The input is the encrypted data, and the output is the decrypted video data. Specifically, this operation is handled by a dedicated decryption module on the cloud server.

[0334] Step 3:

[0335] The cloud server inputs the decoded video data into the AI ​​model and begins data analysis. The AI ​​model uses machine learning techniques to identify landmarks, obstacles, and in-store products. The result of data analysis is the location and attribute information of specific objects. The input is the decoded video data, and the output is information about the analyzed objects. Specifically, the cloud server runs the object detection algorithm.

[0336] Step 4:

[0337] In parallel, the cloud server inputs the acquired video and audio data into the emotion engine to analyze the user's emotional state. The emotion engine uses facial expression recognition technology and voice tone analysis technology to identify the user's emotional state. This analysis provides information such as whether the user is relaxed or nervous. The input is video and audio data, and the output is information about the user's emotional state. Specifically, the emotion engine performs pattern matching on facial expressions and voice tone.

[0338] Step 5:

[0339] The cloud server generates appropriate visual support information based on the analysis results and emotion recognition results. Information that is likely to interest the user, such as product information in the shop, special offers, and discount coupons, is generated. This information is adjusted according to the user's emotional state. For example, if the emotion engine indicates that the user is excited, related products and special offers will be displayed. The input is the data analysis results and emotion recognition results, and the output is visual support information.

[0340] Step 6:

[0341] The generated visual aid is again encrypted and sent back to the user's visual aid using the same encryption technology as used in step 1. To maintain the security of the information, all communication is done via HTTPS protocol. The input is the generated visual aid and the output is the encrypted information.

[0342] Step 7:

[0343] The user's visual aid device receives and decrypts the encrypted data sent from the cloud server. After decryption, the decrypted visual aid information is overlaid on the device's display, allowing the user to see the aid information in their field of vision in the real world. The input is the encrypted visual aid information, and the output is the information displayed in the user's field of vision. Specifically, the device's decryption module and display function work together to display the information.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] [Second embodiment]

[0348] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0349] 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.

[0350] 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).

[0351] 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.

[0352] 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.

[0353] 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).

[0354] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] 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."

[0360] Overview of the entire system

[0361] This invention is a system that uses a visual aid device worn by the user to collect surrounding video data, analyze it via a cloud server, and provide visual aids. Therefore, a wearable device such as smart glasses works in conjunction with a cloud-based AI analysis system. Specifically, it includes the following main elements:

[0362] 1. Visual aids (smart glasses)

[0363] 2. Cloud Server

[0364] 3. Communication Protocol

[0365] 4. AI Analysis Model

[0366] Program processing explanation

[0367] Visual aids (user's device)

[0368] The visual aid device worn by the user is equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. The user's device does not store any of the video data; all processing is done on the cloud server.

[0369] Video capture and encryption

[0370] The camera in the smart glasses captures the surrounding environment and collects video data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0371] Processing on the cloud server (server)

[0372] The cloud server receives and decrypts the encrypted video data sent from the user's device. The AI ​​model then analyzes the decrypted video data. The AI ​​model has facial, object, and text recognition capabilities, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[0373] Generation of visual aids

[0374] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, or AR advertisements for the user. The generated visual aid information is then encrypted again and sent back to the user's visual aid device.

[0375] Displaying information (user's terminal)

[0376] The user's visual aid device receives and decodes the visual aid information sent back from the cloud server. Finally, the decoded visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0377] Specific examples

[0378] For example, a visually impaired user may be using the system to navigate down a street.

[0379] 1. Video acquisition and transmission

[0380] The user wears the smart glasses and the camera captures images of the surrounding area.

[0381] The encrypted video data is sent to a cloud server.

[0382] 2. Analysis on the cloud server

[0383] The server receives the video data, decodes it, and performs AI analysis.

[0384] AI analysis recognizes important objects such as cars, pedestrians, and traffic lights.

[0385] 3. Generation and transmission of visual aids

[0386] Based on the recognition results, visual aid information including warning information for the user and the location of obstacles is generated.

[0387] The visual aid information is encrypted and sent back to the smart glasses.

[0388] 4. Displaying Information

[0389] The smart glasses receive and decode the visual aid information.

[0390] Auxiliary information is displayed superimposed on the user's field of vision to assist in safe walking.

[0391] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[0392] The processing flow will be explained below.

[0393] Step 1:

[0394] The user's visual aid (smart glasses) is activated and the camera captures the surroundings in real time. The camera continuously captures images while the user is walking.

[0395] Step 2:

[0396] Video data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[0397] Step 3:

[0398] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video data.

[0399] Step 4:

[0400] The AI ​​model on the server analyzes the decoded video data and has facial, object, and character recognition capabilities to identify specific landmarks and obstacles.

[0401] Step 5:

[0402] The server generates visual aids based on the analysis results, including obstacle warnings, route guidance, and AR advertisements.

[0403] Step 6:

[0404] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[0405] Step 7:

[0406] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[0407] Step 8:

[0408] The user's visual aid device then displays the decoded visual aid information overlaid on the user's field of vision, allowing the user to view the necessary visual aid information in real time.

[0409] As a concrete example, consider a visually impaired user using the system while walking. The user's smart glasses capture the scenery ahead, and the cloud server analyzes it, identifying a traffic light ahead that is red. This information is encrypted and sent back to the user, where it is displayed as a red light on the smart glasses' display. This allows the user to stop and move safely.

[0410] Example 1

[0411] 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."

[0412] There is a need for an efficient method of providing information to visual aid devices that enable visually impaired users to move around safely. Conventional visual aid devices have difficulty performing highly accurate analysis in real time, and lack a mechanism for providing information while maintaining user privacy. This necessitates the development of a visual aid system that is user-friendly and enhances safety.

[0413] 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.

[0414] In this invention, the server includes a cloud server that decrypts data, a cloud server that encrypts the generated visual aid information, and a cloud server that analyzes the video data using an AI model. This enables the provision of highly accurate visual aid information to visually impaired users in real time, and enhances safety while protecting the user's privacy.

[0415] A "visual aid" is a wearable device that is worn by the user to supplement visual information and assist in safe movement.

[0416] "Video data" refers to visual information of the surrounding environment captured in real time by the camera of the visual aid device.

[0417] "Encryption" is a process that uses a special algorithm to convert acquired video data into a form that cannot be deciphered by others.

[0418] A "cloud server" is a server with computing resources in a remote location that can be accessed via a network, and is used to analyze, store, and communicate data.

[0419] "Analysis" is the process in which the cloud server processes the video data to extract and identify the necessary information.

[0420] "Visual aid information" is information that is generated based on analyzed video data and is used to aid the user's vision.

[0421] A "secure communication protocol" refers to a secure communication method that prevents eavesdropping or tampering by third parties when sending and receiving data.

[0422] An "AI model" is a computer program based on artificial intelligence algorithms used to analyze video data.

[0423] "Decryption" is the process of restoring encrypted data to its original form.

[0424] The present invention is a system that uses a visual aid device worn by a user to collect video data of the surroundings, analyzes it via a cloud server, and provides visual aid means. The overall configuration of the system is described in detail below.

[0425] Visual aids (user's device)

[0426] The visual aid worn by the user is a wearable device such as smart glasses. This visual aid is equipped with a camera, a communication module, and a display, and captures images of the surroundings in real time using the camera. The captured image data is immediately encrypted using an encryption algorithm (e.g., AES). The encrypted data is then sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0427] Processing on the cloud server (server)

[0428] The cloud server receives and decrypts the encrypted video data sent from the user's device. The server then uses a decryption algorithm (e.g., AES) to restore the data to its original form. The AI ​​model then analyzes the decrypted video data. The AI ​​model has capabilities such as facial recognition, object recognition, and text recognition, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[0429] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, AR advertisements, etc. The generated visual aid information is then encrypted again and sent back to the user's visual aid device using a secure communication protocol (e.g., HTTPS).

[0430] Displaying information (user's terminal)

[0431] The user's visual aid device receives the visual aid information sent back from the cloud server and decodes it again using a decoding algorithm. Finally, the decoded visual aid information is displayed superimposed on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0432] Specific examples

[0433] For example, imagine a visually impaired user walking down the street using this system. The camera in the smart glasses captures images of the surrounding area, and the encrypted image data is sent to a cloud server. The cloud server decodes the received image data and uses an AI analysis model to recognize important objects such as cars, pedestrians, and traffic lights. Based on the recognition results, visual aid information is generated that includes warning information for the user and the location of obstacles, and the encrypted visual aid information is sent back to the smart glasses. The smart glasses receive the visual aid information, decode it, and display it overlaid on the user's field of vision, helping them walk safely.

[0434] Prompt Sentence Examples

[0435] "Please tell me how to support visually impaired users to walk safely by wearing a visual aid and using real-time analysis via a cloud server."

[0436] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[0437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0438] Step 1: Acquire video data

[0439] The camera in the visual aid worn by the user captures the surrounding environment in real time. The input data is a real-world image of the surroundings, which the camera captures as video data. For example, the camera captures images of cars and pedestrians as the user walks down the street.

[0440] Step 2: Encrypting the video data

[0441] The video data captured by the device is immediately encrypted using an encryption algorithm (e.g., AES). The input is the captured video data, and the output is the encrypted data. Specifically, the AES algorithm is applied to the captured video frames to encrypt them.

[0442] Step 3: Sending Encrypted Data

[0443] The device sends encrypted video data to the cloud server via a secure communication protocol (e.g., HTTPS). The input is the encrypted video data, and the output is the sent data. Specifically, the data is sent using HTTPS.

[0444] Step 4: Receiving Encrypted Data

[0445] The server receives the encrypted video data sent from the user's terminal. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the data is received via the network.

[0446] Step 5: Decrypt the data

[0447] The server decrypts the encrypted data it receives using a decryption algorithm (e.g., AES). The input is the encrypted video data, and the output is the original video data. Specifically, the data is decrypted using the AES algorithm.

[0448] Step 6: Data analysis using AI

[0449] The server analyzes the decoded video data using an AI model. The input is the decoded video data, and the output is the analysis result. Specifically, it uses face recognition and object recognition models to identify important objects in the video.

[0450] Step 7: Generate visual aids

[0451] The server generates visual support information based on the analysis results. The input is the analysis result of the AI ​​model, and the output is visual support information. Specific operations include generating warnings and guidance information according to the recognized object and situation.

[0452] Step 8: Encrypt the auxiliary information

[0453] The visual auxiliary information generated by the server is again encrypted using an encryption algorithm. The input is the visual auxiliary information, and the output is the encrypted visual auxiliary information. Specifically, the information is encrypted using the AES algorithm.

[0454] Step 9: Sending encrypted auxiliary information

[0455] The server sends encrypted visual aid information to the user's visual aid device using a secure communication protocol (e.g., HTTPS). The input is the encrypted visual aid information, and the output is the transmitted data. Specifically, the information is sent using HTTPS.

[0456] Step 10: Receiving auxiliary information

[0457] The device receives encrypted visual aid information sent from the cloud server. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the device receives information via the network.

[0458] Step 11: Decoding the auxiliary information

[0459] The terminal decrypts the received visual aid information using a decryption algorithm. The input is the encrypted visual aid information, and the output is the original visual aid information. Specifically, the information is decrypted using the AES algorithm.

[0460] Step 12: Displaying information in the field of view

[0461] The terminal displays the decoded visual aid information superimposed on the user's field of vision. The input is the decoded visual aid information, and the output is the aid information displayed in the user's field of vision. Specific operations include displaying warning messages and route guidance information on the display.

[0462] (Application example 1)

[0463] 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."

[0464] The main function of conventional visual aids is to provide users with information about their surrounding environment. However, these devices do not perform adequately in the field of security monitoring. In particular, they do not meet advanced security requirements such as identifying intruders and detecting abnormal behavior. Therefore, in order to improve safety, a system that provides users with security-related information in real time is required.

[0465] 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.

[0466] In this invention, the server includes a cloud server that identifies intruders and detects abnormal behavior, a means for providing warning information to users, and a means for analyzing video data using a generative AI model, which enables security monitoring to quickly receive real-time warnings and important information via users' devices.

[0467] A "visual aid device worn by a user" is a wearable device that provides visual information when worn by a user.

[0468] "Video data" refers to visual information of the surroundings captured by a device such as a camera.

[0469] "Encryption" is the process of transforming data so that it cannot be easily deciphered by third parties.

[0470] A "cloud server" is a remote server available via the Internet that has the ability to store and process large amounts of data.

[0471] "Analysis" is the process of extracting information from acquired data and drawing conclusions according to the purpose.

[0472] "Visual aids" refers to additional visual data provided to help a user obtain information safely and efficiently.

[0473] "Intruder identification" refers to a technology that identifies unauthorized individuals based on video data.

[0474] "Abnormal behavior detection" refers to the technology of recognizing behavior that deviates from normal behavior patterns.

[0475] "Warning information" is a message for notifying the user of urgent information.

[0476] A "generative AI model" is a model trained using machine learning techniques and used to analyze video data, etc.

[0477] A "secure communication protocol" is a communication protocol that prevents unauthorized access by third parties when sending and receiving data.

[0478] This invention is a system that uses a user-worn visual aid device to collect surrounding video data, analyze it via a cloud server, and provide security-related visual aids. Specifically, it includes the following main elements:

[0479] 1. Visual aids (smart glasses)

[0480] The smart glasses worn by the user are equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. No data is stored on the smart glasses themselves; all analysis and processing is done on the cloud server.

[0481] 2. Cloud Server

[0482] The cloud server receives and decrypts the encrypted video data sent from the user's device. It then analyzes the video data using a generative AI model. This AI model has functions such as identifying intruders, detecting abnormal behavior, and recognizing faces. Based on the analysis results, it generates visual aids or security warning information.

[0483] 3. Communication Protocol

[0484] Video data is sent and received using a secure communication protocol (HTTPS), which ensures data encryption and safe transmission.

[0485] 4. AI Analysis Model

[0486] The AI ​​analysis model on the cloud server is implemented using TensorFlow and OpenCV to analyze the video data, which detects suspicious behavior and abnormal movements.

[0487] 5. Generation of visual support information

[0488] Based on the analysis results, the cloud server generates visual aids or warning information, re-encrypts it, and sends it back to the user's smart glasses. The smart glasses then decrypt the received information and display it overlaid on the user's field of vision, allowing the user to obtain important security information in real time.

[0489] Specific examples

[0490] For example, consider the case where a security guard uses this system in a large commercial facility. The security guard wears smart glasses and patrols the facility. A camera captures video of the surrounding area, encrypts it, and sends it to a cloud server. The cloud server analyzes the video data and detects abnormal behavior (e.g., climbing over a wall or leaving a bag unattended). Based on this, a warning message is sent to the security guard and displayed on the smart glasses' display.

[0491] Prompt Sentence Examples

[0492] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[0493] In this way, the system according to the present invention can provide real-time visual aids to improve user safety in security surveillance.

[0494] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0495] Step 1:

[0496] The user wears the smart glasses, and the camera in the visual aid device captures images of the surrounding area.

[0497] Input: Real-time video data acquired by the camera.

[0498] Output: Video data.

[0499] How it works: A high-resolution camera mounted on the smart glasses captures the user's surroundings in real time and generates the images as digital data.

[0500] Step 2:

[0501] The video data captured by the visual aid device's camera is immediately encrypted using an encryption algorithm (e.g., AES).

[0502] Input: Acquired video data.

[0503] Output: Encrypted video data.

[0504] How it works: The encryption module built into the smart glasses converts the captured video data into a secure format using an encryption algorithm such as AES.

[0505] Step 3:

[0506] The encrypted video data is sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0507] Input: Encrypted video data.

[0508] Output: Data sent to the cloud server.

[0509] Specific operation: The communication module of the smart glasses securely transmits encrypted video data to a cloud server using HTTPS.

[0510] Step 4:

[0511] The cloud server decrypts the received encrypted video data.

[0512] Input: Encrypted video data sent to the cloud server.

[0513] Output: Decoded video data.

[0514] Specific operation: A decryption module installed on the cloud server converts the video data sent in a secure format back into its original format.

[0515] Step 5:

[0516] The generated AI model on the cloud server analyzes the decoded video data.

[0517] Input: Decoded video data.

[0518] Output: Analysis results (identification of intruders, detection of abnormal behavior, etc.).

[0519] How it works: A generative AI model (e.g., TensorFlow, OpenCV) deployed on a cloud server analyzes video data and performs facial recognition and abnormal behavior detection. Specifically, it uses a pre-trained dataset to identify objects and people in the video.

[0520] Step 6:

[0521] The cloud server generates warning information based on the analysis results.

[0522] Input: Analysis results.

[0523] Output: Alert information (such as suspicious person alert messages and abnormal behavior notifications).

[0524] Specific operation: Based on the results of the AI ​​analysis model, a program on the server generates a warning message in a format that is easy for the user to understand. For example, the warning message might say, "Suspicious behavior has been detected. Please check for details."

[0525] Step 7:

[0526] The generated warning information is encrypted and transmitted to the user's visual aid.

[0527] Input: Warning information.

[0528] Output: The encrypted warning information sent to the smart glasses.

[0529] Specific operation: The cloud server re-encrypts the generated warning information and sends it to the user's visual aid device using a secure communication protocol.

[0530] Step 8:

[0531] The visual aid decodes the received warning information and displays it in the user's field of view.

[0532] Input: Encrypted warning information.

[0533] Output: Warning information displayed in the user's view.

[0534] Specific operation: A decryption module built into the smart glasses decrypts the warning information sent from the cloud server and displays it on the screen. For example, it might display information such as "There is a suspicious person in the facility. Please be careful."

[0535] Prompt Sentence Examples

[0536] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[0537] 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.

[0538] System Configuration

[0539] This invention is a system that acquires video data from a visual aid device worn by a user, sends it to a cloud server for analysis, and, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide visual aid information based on the user's emotional state.

[0540] Overview of the entire system

[0541] The system consists of the following elements:

[0542] 1. Visual aids (smart glasses)

[0543] 2. Cloud Server

[0544] 3. Communication Protocol

[0545] 4. AI Analysis Model

[0546] 5. Emotion Engine

[0547] Program processing explanation

[0548] Visual aids (user's device)

[0549] The visual aid device worn by the user is equipped with a camera, a communication module, a display, and a microphone. This device captures surrounding video and audio in real time, encrypts the data, and transmits it to a cloud server. The video and audio data is not stored on the user's device; all processing is done on the cloud server.

[0550] Video and audio capture and encryption

[0551] The smart glasses' camera and microphone capture the surrounding environment and collect video and audio data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0552] Processing on the cloud server (server)

[0553] A cloud server receives and decrypts the encrypted data sent from the user's device. An AI model then analyzes the decrypted data to recognize specific landmarks and obstacles. Visual aids are generated based on the analyzed data and the user's emotional state.

[0554] Emotion engine analysis and tuning

[0555] The cloud server is equipped with an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. For example, it performs facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed. The emotion engine can then appropriately adjust the visual support information based on the user's emotional state. For example, if the user is feeling stressed, it may prioritize the display of warning information.

[0556] Generation and transmission of visual aids

[0557] Visual aids are generated based on the analysis results. These include obstacle warnings, route guidance, AR advertisements, etc. The generated visual aids are then encrypted again and sent to the user's device.

[0558] Displaying information (user's terminal)

[0559] The user's visual aid device receives and decrypts the encrypted data sent back from the cloud server. Finally, the decrypted visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0560] Specific examples

[0561] For example, imagine a visually impaired user walking through a busy downtown area using this system. The user's smart glasses capture video of the area ahead and audio of the surrounding area, and these data are sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data, and an emotion engine recognizes the user's impatience from their voice tone and facial expression. As a result, the server prioritizes generating visual aids with high urgency (e.g., a message urging them to pause) and sends them to the smart glasses. The smart glasses overlay the received information on the user's field of vision, helping the user to act safely.

[0562] In this way, the system according to the present invention can provide useful visual aids in real time, while preserving privacy, and taking into account the user's emotional state.

[0563] The processing flow will be explained below.

[0564] Step 1:

[0565] The user's visual aid (smart glasses) is activated, and the camera and microphone capture the surrounding images and sounds in real time. The camera continuously captures images while the user is walking, and the microphone collects environmental sounds and the user's voice.

[0566] Step 2:

[0567] Video and audio data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[0568] Step 3:

[0569] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video and audio data.

[0570] Step 4:

[0571] The AI ​​analysis model on the server analyzes the decoded video and audio data, recognizing specific landmarks and obstacles from the video data and analyzing the user's tone of voice and surrounding sounds from the audio data.

[0572] Step 5:

[0573] The server's emotion engine determines the user's emotional state based on the analyzed data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed.

[0574] Step 6:

[0575] The server generates visual support information based on the analysis results and the user's emotional state, including obstacle warnings, route guidance, AR advertisements, and warnings and support based on the user's emotions.

[0576] Step 7:

[0577] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[0578] Step 8:

[0579] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[0580] Step 9:

[0581] The user's visual aid device then overlays the decoded visual aid information onto their field of vision, allowing them to see the necessary visual aid information in real time and act safely.

[0582] As a concrete example, consider a visually impaired user walking through a busy downtown area. The user's smart glasses capture the scenery ahead and the surrounding sounds, and send them to a cloud server. The server uses video analysis to recognize obstacles and audio analysis to detect that the user is nervous. As a result, the server generates a warning saying, "Obstacle ahead. Be careful," and sends it to the smart glasses. The smart glasses display this warning in the user's field of vision, allowing the user to safely avoid the obstacle.

[0583] Example 2

[0584] 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."

[0585] Currently, support systems for visually impaired users to navigate safely have limitations in their functionality. Furthermore, they do not provide visual support information that takes into account the user's emotional state, making it difficult to adequately alleviate the user's stress and tension. Therefore, there is a need for a system that provides real-time information about the surrounding environment while also presenting support information that corresponds to the user's emotional state.

[0586] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring video data and audio data from a visual aid device worn by a user, means for encrypting the video data and audio data and transmitting them to a cloud server, means for decrypting the video data and audio data received on the cloud server, means for analyzing the decrypted data using an AI model and recognizing specific landmarks and obstacles, means for analyzing the user's emotional state using the emotion engine and adjusting the visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, and means for displaying the returned visual aid information in the user's field of view. This makes it possible to provide safe, real-time visual aid information while taking the user's emotional state into consideration.

[0587] A "visual aid device" is a wearable device worn by a user, and includes a camera and a microphone to capture video and audio data of the surrounding area.

[0588] "Encryption" is the process of transforming data using a specific algorithm (e.g., AES) to make the data unintelligible to third parties.

[0589] "Decryption" is the process of returning encrypted data to its original state, making the data received by the cloud server ready for analysis.

[0590] A "cloud server" is a remote computing system that stores, processes, and analyzes data over a network.

[0591] An "AI model" is an algorithm or program that uses artificial intelligence to analyze data, and can analyze video and audio data to recognize specific landmarks and obstacles.

[0592] An "emotion engine" is a system for analyzing a user's emotional state, and is a technology for determining a user's emotional state by performing facial expression recognition and voice tone analysis.

[0593] "Visual support information" is information generated based on analyzed data, and includes obstacle warnings, route guidance information, AR advertisements, etc.

[0594] A "secure communication protocol" is a protocol that ensures security during data transfer (e.g., HTTPS).

[0595] "Landmarks" are specific important points or landmarks that an AI model recognizes.

[0596] An "obstacle" is an object or structure that may impede the user's movement and is recognized by the AI ​​model.

[0597] In this invention, the visual aid device worn by the user (hereinafter referred to as the terminal) plays a key role. The terminal is equipped with a camera, microphone, communication module, and display, and captures video and audio data in real time. The video and audio data are immediately encrypted using the AES encryption algorithm and transmitted to a cloud server via the secure HTTP protocol (HTTPS).

[0598] The cloud server decrypts the received encrypted data and prepares it for analysis. An AI analysis model within the cloud server analyzes the decrypted data and recognizes specific landmarks and obstacles. An emotion engine also analyzes the video and audio data to determine the user's emotional state. This allows the cloud server to determine whether the user is currently feeling stressed or relaxed.

[0599] The cloud server then generates visual support information based on the data analysis and the emotional state analysis results. The generated visual support information includes location information of specific landmarks and warning messages about obstacles. Furthermore, the priority of information is adjusted taking into account the results of the emotion engine. For example, if the user is feeling stressed, information with a high level of urgency will be provided first.

[0600] The generated visual aid information is then AES encrypted and sent to the device. The user's device receives the data, decrypts it, and then displays the visual aid information overlaid on the display. This allows the user to simultaneously view the actual environment and the aid information.

[0601] As a concrete example, consider a scenario in which a visually impaired user is walking through a busy shopping district. The device's camera captures video of the area ahead, and its microphone collects surrounding audio. This data is encrypted and sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data. At the same time, an emotion engine analyzes the user's emotional state and identifies that the user is feeling impatient. As a result, a warning message such as "Obstacle ahead. Please stop moving" is generated, re-encrypted, and sent to the device. The device displays this information on its display, helping the user navigate safely.

[0602] Examples of prompts include:

[0603] "In this system, a visually impaired user wears smart glasses and transmits video and audio of the surrounding area to a cloud server for analysis. The cloud server analyzes the video and audio data and generates visual support information based on the user's emotional state. This information is sent to the user's smart glasses and displayed together with real-world information. As a specific example, please explain a case where the cloud server prioritizes the generation of visual support information with high urgency."

[0604] The present invention makes it possible to provide useful visual aid information in real time while taking into account the user's emotional state.

[0605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0606] Step 1:

[0607] The user wears a visual aid, and the device's camera and microphone capture the surroundings in real time. The input is video and audio data. This data is immediately encrypted using the AES encryption algorithm. The encrypted data is sent to a cloud server using the HTTPS protocol. The output is encrypted video and audio data.

[0608] Step 2:

[0609] The cloud server receives the encrypted data sent from the device. The decryption module in the server decrypts the data using the AES algorithm. The input is the encrypted data, and the output is the decrypted video and audio data. This data is stored for analysis by the AI ​​model.

[0610] Step 3:

[0611] The AI ​​analysis model on the cloud server analyzes and recognizes specific landmarks and obstacles based on the decoded video data. The input is the decoded video data, and the output is the landmark and obstacle information as an analysis result. The AI ​​model detects landmarks and obstacles in each frame and identifies the user's location and direction of travel.

[0612] Step 4:

[0613] The emotion engine on the cloud server analyzes the decoded audio and video data to determine the user's emotional state. The input is the decoded audio and video data, and the output is a judgment of the user's emotional state. The emotion engine uses facial expression recognition algorithms and voice tone analysis to determine whether the user is stressed or relaxed.

[0614] Step 5:

[0615] The cloud server generates visual support information based on the results of data analysis and emotion analysis. The input is landmark and obstacle information and the user's emotional state, and the output is adjusted visual support information. For example, if the user is feeling stressed, priority is given to information with a high level of urgency. The generated visual support information includes obstacle warnings and route guidance information.

[0616] Step 6:

[0617] The cloud server re-encrypts the generated visual aid information using AES and sends it to the user's device. The input is the generated visual aid information, and the output is the encrypted visual aid information. The encrypted data is sent to the device via the HTTPS protocol.

[0618] Step 7:

[0619] The user's device receives and decrypts the encrypted data sent from the cloud server. The input is the encrypted visual aid information, and the output is the decrypted visual aid information. The decrypted information is finally overlaid on the display and displayed in the user's field of view. This allows the user to simultaneously view the real environment and the visual aid information.

[0620] As a specific example of how it works, after a user puts on the visual aid device, information about obstacles and landmarks ahead is acquired and analyzed in real time as the user walks through a busy shopping district, and warnings and guidance based on the results are displayed in the user's field of vision, helping the user to move around safely.

[0621] (Application example 2)

[0622] 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."

[0623] Current visual aid devices can acquire and analyze a user's visual information, but it is difficult to consider the user's emotional state. As a result, they are unable to provide appropriate visual aid information based on the user's emotions, which increases the burden on the user, especially in stressful situations or complex environments. Another issue is that they are unable to provide appropriate aid information to make shopping more comfortable for users in physical stores.

[0624] The specific processing by the specific 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 means for acquiring video data from a visual aid device worn by a user, means for encrypting the video data and transmitting it to a cloud server, means for analyzing the received video data on the cloud server, means for generating visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, means for displaying the returned visual aid information in the user's field of view, and means for adjusting the visual aid information based on the user's emotional state. This makes it possible to provide appropriate visual aid information in real time according to the user's emotions, thereby alleviating user stress and improving the shopping experience, particularly in physical stores.

[0625] A "visual aid device" is a device worn by a user to provide visual assistance, and is equipped with a camera, a communication module, a display, etc.

[0626] "Video data" refers to video information of the surroundings captured by the camera of the visual aid device.

[0627] "Encryption" refers to the process of protecting video and other data using security protocols.

[0628] A "cloud server" is a remote computer system that exists on the Internet and stores, analyzes, and processes data.

[0629] An "AI model" is a computational model that uses algorithms such as machine learning or deep learning to analyze data and recognize specific patterns or features.

[0630] "Visual supplementary information" refers to supplementary visual information provided to the user, and includes route guidance information, obstacle warnings, product information, and the like.

[0631] An "emotion engine" is software or an algorithm that analyzes video and audio data to identify and analyze the user's emotional state.

[0632] A "security communication protocol" is a set of communication rules for safely sending and receiving data, and uses encryption technology to protect data.

[0633] System Configuration

[0634] This system acquires video data from a visual aid device, encrypts it, and sends it to a cloud server. The cloud server analyzes the received data and generates visual aid information based on the analysis results. The generated information is then re-encrypted and sent back to the user, who then displays it in the field of view of the visual aid device worn by the user. This allows the user to simultaneously view the real world and the aid information.

[0635] Hardware and software used

[0636] Visual aids: Wearable devices equipped with a camera, communication module, display, and microphone. Specifically, smart glasses (e.g., Google Glass) are used.

[0637] Cloud server: A remote computer system for data analysis and emotion recognition. AWS, Google Cloud, etc. are used.

[0638] Cryptography library: Uses Fernet from the cryptography package to ensure data security.

[0639] Communication protocol: HTTPS protocol is used to ensure secure data transmission.

[0640] Acquisition and transmission of video data

[0641] When a user wears the visual aid, the device's camera captures real-time images of the surroundings and instantly encrypts the data, which is then sent to a cloud server via a secure communication protocol.

[0642] Data analysis using a cloud server

[0643] The cloud server decrypts the received encrypted data and analyzes it using an AI model. This analysis identifies specific landmarks, obstacles, and in-store product information. The cloud server also incorporates an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. This analysis is achieved, for example, by performing facial expression recognition and voice tone analysis.

[0644] Creating and providing visual support information

[0645] Visual aids are generated based on the user's emotional state and the results of video analysis. This visual aid information includes obstacle warnings, route guidance, in-store product and sale information, etc. The generated information is then re-encrypted and sent back to the user's visual aid device.

[0646] Displaying Information

[0647] The visual aid device receives the encrypted data, decrypts it, and displays it in the user's field of view. This allows the user to simultaneously view the real-world image and the auxiliary information. This can be particularly useful in brick-and-mortar stores, where the aid information can be provided to make shopping more comfortable for the user.

[0648] Specific examples

[0649] For example, when a user visits a shopping mall, a visual aid device (smart glasses) captures the product shelves in the store. The cloud server recognizes these products and, based on that information, displays the product the user is looking for or recommended products as visual aid information. If the user shows a favorable expression toward a particular product, the emotion engine detects this and displays special sale information or discount coupons for that product.

[0650] Prompt Sentence Examples

[0651] "Identify products in a store and analyze the user's emotional state from video and audio data captured by the user wearing a visual aid. If the user is unsure, recommend products, or offer discounts if the user likes a particular product."

[0652] In this way, the system of the present invention can provide useful visual aids in real time while properly taking into account the user's emotional state.

[0653] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0654] Step 1:

[0655] The user wears a visual aid device, and the device's camera captures images of the surroundings. This data is input to the terminal as image data. The terminal immediately encrypts this image data. The encryption technology used is a high-security encryption algorithm such as AES. After encryption, the encrypted image data is sent to a cloud server by a communication module. The input is image data of the real environment, and the output is encrypted image data.

[0656] Step 2:

[0657] The cloud server receives the encrypted data. The server decrypts the received data and retrieves the original video data. Decryption is performed using a key shared between the sites. The input is the encrypted data, and the output is the decrypted video data. Specifically, this operation is handled by a dedicated decryption module on the cloud server.

[0658] Step 3:

[0659] The cloud server inputs the decoded video data into the AI ​​model and begins data analysis. The AI ​​model uses machine learning techniques to identify landmarks, obstacles, and in-store products. The result of data analysis is the location and attribute information of specific objects. The input is the decoded video data, and the output is information about the analyzed objects. Specifically, the cloud server runs the object detection algorithm.

[0660] Step 4:

[0661] In parallel, the cloud server inputs the acquired video and audio data into the emotion engine to analyze the user's emotional state. The emotion engine uses facial expression recognition technology and voice tone analysis technology to identify the user's emotional state. This analysis provides information such as whether the user is relaxed or nervous. The input is video and audio data, and the output is information about the user's emotional state. Specifically, the emotion engine performs pattern matching on facial expressions and voice tone.

[0662] Step 5:

[0663] The cloud server generates appropriate visual support information based on the analysis results and emotion recognition results. Information that is likely to interest the user, such as product information in the shop, special offers, and discount coupons, is generated. This information is adjusted according to the user's emotional state. For example, if the emotion engine indicates that the user is excited, related products and special offers will be displayed. The input is the data analysis results and emotion recognition results, and the output is visual support information.

[0664] Step 6:

[0665] The generated visual aid is again encrypted and sent back to the user's visual aid using the same encryption technology as used in step 1. To maintain the security of the information, all communication is done via HTTPS protocol. The input is the generated visual aid and the output is the encrypted information.

[0666] Step 7:

[0667] The user's visual aid device receives and decrypts the encrypted data sent from the cloud server. After decryption, the decrypted visual aid information is overlaid on the device's display, allowing the user to see the aid information in their field of vision in the real world. The input is the encrypted visual aid information, and the output is the information displayed in the user's field of vision. Specifically, the device's decryption module and display function work together to display the information.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] [Third embodiment]

[0672] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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."

[0684] Overview of the entire system

[0685] This invention is a system that uses a visual aid device worn by the user to collect surrounding video data, analyze it via a cloud server, and provide visual aids. Therefore, a wearable device such as smart glasses works in conjunction with a cloud-based AI analysis system. Specifically, it includes the following main elements:

[0686] 1. Visual aids (smart glasses)

[0687] 2. Cloud Server

[0688] 3. Communication Protocol

[0689] 4. AI Analysis Model

[0690] Program processing explanation

[0691] Visual aids (user's device)

[0692] The visual aid device worn by the user is equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. The user's device does not store any of the video data; all processing is done on the cloud server.

[0693] Video capture and encryption

[0694] The camera in the smart glasses captures the surrounding environment and collects video data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0695] Processing on the cloud server (server)

[0696] The cloud server receives and decrypts the encrypted video data sent from the user's device. The AI ​​model then analyzes the decrypted video data. The AI ​​model has facial, object, and text recognition capabilities, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[0697] Generation of visual aids

[0698] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, or AR advertisements for the user. The generated visual aid information is then encrypted again and sent back to the user's visual aid device.

[0699] Displaying information (user's terminal)

[0700] The user's visual aid device receives and decodes the visual aid information sent back from the cloud server. Finally, the decoded visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0701] Specific examples

[0702] For example, a visually impaired user may be using the system to navigate down a street.

[0703] 1. Video acquisition and transmission

[0704] The user wears the smart glasses and the camera captures images of the surrounding area.

[0705] The encrypted video data is sent to a cloud server.

[0706] 2. Analysis on the cloud server

[0707] The server receives the video data, decodes it, and performs AI analysis.

[0708] AI analysis recognizes important objects such as cars, pedestrians, and traffic lights.

[0709] 3. Generation and transmission of visual aids

[0710] Based on the recognition results, visual aid information including warning information for the user and the location of obstacles is generated.

[0711] The visual aid information is encrypted and sent back to the smart glasses.

[0712] 4. Displaying Information

[0713] The smart glasses receive and decode the visual aid information.

[0714] Auxiliary information is displayed superimposed on the user's field of vision to assist in safe walking.

[0715] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[0716] The processing flow will be explained below.

[0717] Step 1:

[0718] The user's visual aid (smart glasses) is activated and the camera captures the surroundings in real time. The camera continuously captures images while the user is walking.

[0719] Step 2:

[0720] Video data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[0721] Step 3:

[0722] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video data.

[0723] Step 4:

[0724] The AI ​​model on the server analyzes the decoded video data and has facial, object, and character recognition capabilities to identify specific landmarks and obstacles.

[0725] Step 5:

[0726] The server generates visual aids based on the analysis results, including obstacle warnings, route guidance, and AR advertisements.

[0727] Step 6:

[0728] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[0729] Step 7:

[0730] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[0731] Step 8:

[0732] The user's visual aid device then displays the decoded visual aid information overlaid on the user's field of vision, allowing the user to view the necessary visual aid information in real time.

[0733] As a concrete example, consider a visually impaired user using the system while walking. The user's smart glasses capture the scenery ahead, and the cloud server analyzes it, identifying a traffic light ahead that is red. This information is encrypted and sent back to the user, where it is displayed as a red light on the smart glasses' display. This allows the user to stop and move safely.

[0734] Example 1

[0735] 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."

[0736] There is a need for an efficient method of providing information to visual aid devices that enable visually impaired users to move around safely. Conventional visual aid devices have difficulty performing highly accurate analysis in real time, and lack a mechanism for providing information while maintaining user privacy. This necessitates the development of a visual aid system that is user-friendly and enhances safety.

[0737] 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.

[0738] In this invention, the server includes a cloud server that decrypts data, a cloud server that encrypts the generated visual aid information, and a cloud server that analyzes the video data using an AI model. This enables the provision of highly accurate visual aid information to visually impaired users in real time, and enhances safety while protecting the user's privacy.

[0739] A "visual aid" is a wearable device that is worn by the user to supplement visual information and assist in safe movement.

[0740] "Video data" refers to visual information of the surrounding environment captured in real time by the camera of the visual aid device.

[0741] "Encryption" is a process that uses a special algorithm to convert acquired video data into a form that cannot be deciphered by others.

[0742] A "cloud server" is a server with computing resources in a remote location that can be accessed via a network, and is used to analyze, store, and communicate data.

[0743] "Analysis" is the process in which the cloud server processes the video data to extract and identify the necessary information.

[0744] "Visual aid information" is information that is generated based on analyzed video data and is used to aid the user's vision.

[0745] A "secure communication protocol" refers to a secure communication method that prevents eavesdropping or tampering by third parties when sending and receiving data.

[0746] An "AI model" is a computer program based on artificial intelligence algorithms used to analyze video data.

[0747] "Decryption" is the process of restoring encrypted data to its original form.

[0748] The present invention is a system that uses a visual aid device worn by a user to collect video data of the surroundings, analyzes it via a cloud server, and provides visual aid means. The overall configuration of the system is described in detail below.

[0749] Visual aids (user's device)

[0750] The visual aid worn by the user is a wearable device such as smart glasses. This visual aid is equipped with a camera, a communication module, and a display, and captures images of the surroundings in real time using the camera. The captured image data is immediately encrypted using an encryption algorithm (e.g., AES). The encrypted data is then sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0751] Processing on the cloud server (server)

[0752] The cloud server receives and decrypts the encrypted video data sent from the user's device. The server then uses a decryption algorithm (e.g., AES) to restore the data to its original form. The AI ​​model then analyzes the decrypted video data. The AI ​​model has capabilities such as facial recognition, object recognition, and text recognition, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[0753] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, AR advertisements, etc. The generated visual aid information is then encrypted again and sent back to the user's visual aid device using a secure communication protocol (e.g., HTTPS).

[0754] Displaying information (user's terminal)

[0755] The user's visual aid device receives the visual aid information sent back from the cloud server and decodes it again using a decoding algorithm. Finally, the decoded visual aid information is displayed superimposed on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0756] Specific examples

[0757] For example, imagine a visually impaired user walking down the street using this system. The camera in the smart glasses captures images of the surrounding area, and the encrypted image data is sent to a cloud server. The cloud server decodes the received image data and uses an AI analysis model to recognize important objects such as cars, pedestrians, and traffic lights. Based on the recognition results, visual aid information is generated that includes warning information for the user and the location of obstacles, and the encrypted visual aid information is sent back to the smart glasses. The smart glasses receive the visual aid information, decode it, and display it overlaid on the user's field of vision, helping them walk safely.

[0758] Prompt Sentence Examples

[0759] "Please tell me how to support visually impaired users to walk safely by wearing a visual aid and using real-time analysis via a cloud server."

[0760] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[0761] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0762] Step 1: Acquire video data

[0763] The camera in the visual aid worn by the user captures the surrounding environment in real time. The input data is a real-world image of the surroundings, which the camera captures as video data. For example, the camera captures images of cars and pedestrians as the user walks down the street.

[0764] Step 2: Encrypting the video data

[0765] The video data captured by the device is immediately encrypted using an encryption algorithm (e.g., AES). The input is the captured video data, and the output is the encrypted data. Specifically, the AES algorithm is applied to the captured video frames to encrypt them.

[0766] Step 3: Sending Encrypted Data

[0767] The device sends encrypted video data to the cloud server via a secure communication protocol (e.g., HTTPS). The input is the encrypted video data, and the output is the sent data. Specifically, the data is sent using HTTPS.

[0768] Step 4: Receiving Encrypted Data

[0769] The server receives the encrypted video data sent from the user's terminal. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the data is received via the network.

[0770] Step 5: Decrypt the data

[0771] The server decrypts the encrypted data it receives using a decryption algorithm (e.g., AES). The input is the encrypted video data, and the output is the original video data. Specifically, the data is decrypted using the AES algorithm.

[0772] Step 6: Data analysis using AI

[0773] The server analyzes the decoded video data using an AI model. The input is the decoded video data, and the output is the analysis result. Specifically, it uses face recognition and object recognition models to identify important objects in the video.

[0774] Step 7: Generate visual aids

[0775] The server generates visual support information based on the analysis results. The input is the analysis result of the AI ​​model, and the output is visual support information. Specific operations include generating warnings and guidance information according to the recognized object and situation.

[0776] Step 8: Encrypt the auxiliary information

[0777] The visual auxiliary information generated by the server is again encrypted using an encryption algorithm. The input is the visual auxiliary information, and the output is the encrypted visual auxiliary information. Specifically, the information is encrypted using the AES algorithm.

[0778] Step 9: Sending encrypted auxiliary information

[0779] The server sends encrypted visual aid information to the user's visual aid device using a secure communication protocol (e.g., HTTPS). The input is the encrypted visual aid information, and the output is the transmitted data. Specifically, the information is sent using HTTPS.

[0780] Step 10: Receiving auxiliary information

[0781] The device receives encrypted visual aid information sent from the cloud server. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the device receives information via the network.

[0782] Step 11: Decoding the auxiliary information

[0783] The terminal decrypts the received visual aid information using a decryption algorithm. The input is the encrypted visual aid information, and the output is the original visual aid information. Specifically, the information is decrypted using the AES algorithm.

[0784] Step 12: Displaying information in the field of view

[0785] The terminal displays the decoded visual aid information superimposed on the user's field of vision. The input is the decoded visual aid information, and the output is the aid information displayed in the user's field of vision. Specific operations include displaying warning messages and route guidance information on the display.

[0786] (Application example 1)

[0787] 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."

[0788] The main function of conventional visual aids is to provide users with information about their surrounding environment. However, these devices do not perform adequately in the field of security monitoring. In particular, they do not meet advanced security requirements such as identifying intruders and detecting abnormal behavior. Therefore, in order to improve safety, a system that provides users with security-related information in real time is required.

[0789] 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.

[0790] In this invention, the server includes a cloud server that identifies intruders and detects abnormal behavior, a means for providing warning information to users, and a means for analyzing video data using a generative AI model, which enables security monitoring to quickly receive real-time warnings and important information via users' devices.

[0791] A "visual aid device worn by a user" is a wearable device that provides visual information when worn by a user.

[0792] "Video data" refers to visual information of the surroundings captured by a device such as a camera.

[0793] "Encryption" is the process of transforming data so that it cannot be easily deciphered by third parties.

[0794] A "cloud server" is a remote server available via the Internet that has the ability to store and process large amounts of data.

[0795] "Analysis" is the process of extracting information from acquired data and drawing conclusions according to the purpose.

[0796] "Visual aids" refers to additional visual data provided to help a user obtain information safely and efficiently.

[0797] "Intruder identification" refers to a technology that identifies unauthorized individuals based on video data.

[0798] "Abnormal behavior detection" refers to the technology of recognizing behavior that deviates from normal behavior patterns.

[0799] "Warning information" is a message for notifying the user of urgent information.

[0800] A "generative AI model" is a model trained using machine learning techniques and used to analyze video data, etc.

[0801] A "secure communication protocol" is a communication protocol that prevents unauthorized access by third parties when sending and receiving data.

[0802] This invention is a system that uses a user-worn visual aid device to collect surrounding video data, analyze it via a cloud server, and provide security-related visual aids. Specifically, it includes the following main elements:

[0803] 1. Visual aids (smart glasses)

[0804] The smart glasses worn by the user are equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. No data is stored on the smart glasses themselves; all analysis and processing is done on the cloud server.

[0805] 2. Cloud Server

[0806] The cloud server receives and decrypts the encrypted video data sent from the user's device. It then analyzes the video data using a generative AI model. This AI model has functions such as identifying intruders, detecting abnormal behavior, and recognizing faces. Based on the analysis results, it generates visual aids or security warning information.

[0807] 3. Communication Protocol

[0808] Video data is sent and received using a secure communication protocol (HTTPS), which ensures data encryption and safe transmission.

[0809] 4. AI Analysis Model

[0810] The AI ​​analysis model on the cloud server is implemented using TensorFlow and OpenCV to analyze the video data, which detects suspicious behavior and abnormal movements.

[0811] 5. Generation of visual support information

[0812] Based on the analysis results, the cloud server generates visual aids or warning information, re-encrypts it, and sends it back to the user's smart glasses. The smart glasses then decrypt the received information and display it overlaid on the user's field of vision, allowing the user to obtain important security information in real time.

[0813] Specific examples

[0814] For example, consider the case where a security guard uses this system in a large commercial facility. The security guard wears smart glasses and patrols the facility. A camera captures video of the surrounding area, encrypts it, and sends it to a cloud server. The cloud server analyzes the video data and detects abnormal behavior (e.g., climbing over a wall or leaving a bag unattended). Based on this, a warning message is sent to the security guard and displayed on the smart glasses' display.

[0815] Prompt Sentence Examples

[0816] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[0817] In this way, the system according to the present invention can provide real-time visual aids to improve user safety in security surveillance.

[0818] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0819] Step 1:

[0820] The user wears the smart glasses, and the camera in the visual aid device captures images of the surrounding area.

[0821] Input: Real-time video data acquired by the camera.

[0822] Output: Video data.

[0823] How it works: A high-resolution camera mounted on the smart glasses captures the user's surroundings in real time and generates the images as digital data.

[0824] Step 2:

[0825] The video data captured by the visual aid device's camera is immediately encrypted using an encryption algorithm (e.g., AES).

[0826] Input: Acquired video data.

[0827] Output: Encrypted video data.

[0828] How it works: The encryption module built into the smart glasses converts the captured video data into a secure format using an encryption algorithm such as AES.

[0829] Step 3:

[0830] The encrypted video data is sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0831] Input: Encrypted video data.

[0832] Output: Data sent to the cloud server.

[0833] Specific operation: The communication module of the smart glasses securely transmits encrypted video data to a cloud server using HTTPS.

[0834] Step 4:

[0835] The cloud server decrypts the received encrypted video data.

[0836] Input: Encrypted video data sent to the cloud server.

[0837] Output: Decoded video data.

[0838] Specific operation: A decryption module installed on the cloud server converts the video data sent in a secure format back into its original format.

[0839] Step 5:

[0840] The generated AI model on the cloud server analyzes the decoded video data.

[0841] Input: Decoded video data.

[0842] Output: Analysis results (identification of intruders, detection of abnormal behavior, etc.).

[0843] How it works: A generative AI model (e.g., TensorFlow, OpenCV) deployed on a cloud server analyzes video data and performs facial recognition and abnormal behavior detection. Specifically, it uses a pre-trained dataset to identify objects and people in the video.

[0844] Step 6:

[0845] The cloud server generates warning information based on the analysis results.

[0846] Input: Analysis results.

[0847] Output: Alert information (such as suspicious person alert messages and abnormal behavior notifications).

[0848] Specific operation: Based on the results of the AI ​​analysis model, a program on the server generates a warning message in a format that is easy for the user to understand. For example, the warning message might say, "Suspicious behavior has been detected. Please check for details."

[0849] Step 7:

[0850] The generated warning information is encrypted and transmitted to the user's visual aid.

[0851] Input: Warning information.

[0852] Output: The encrypted warning information sent to the smart glasses.

[0853] Specific operation: The cloud server re-encrypts the generated warning information and sends it to the user's visual aid device using a secure communication protocol.

[0854] Step 8:

[0855] The visual aid decodes the received warning information and displays it in the user's field of view.

[0856] Input: Encrypted warning information.

[0857] Output: Warning information displayed in the user's view.

[0858] Specific operation: A decryption module built into the smart glasses decrypts the warning information sent from the cloud server and displays it on the screen. For example, it might display information such as "There is a suspicious person in the facility. Please be careful."

[0859] Prompt Sentence Examples

[0860] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[0861] 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.

[0862] System Configuration

[0863] This invention is a system that acquires video data from a visual aid device worn by a user, sends it to a cloud server for analysis, and, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide visual aid information based on the user's emotional state.

[0864] Overview of the entire system

[0865] The system consists of the following elements:

[0866] 1. Visual aids (smart glasses)

[0867] 2. Cloud Server

[0868] 3. Communication Protocol

[0869] 4. AI Analysis Model

[0870] 5. Emotion Engine

[0871] Program processing explanation

[0872] Visual aids (user's device)

[0873] The visual aid device worn by the user is equipped with a camera, a communication module, a display, and a microphone. This device captures surrounding video and audio in real time, encrypts the data, and transmits it to a cloud server. The video and audio data is not stored on the user's device; all processing is done on the cloud server.

[0874] Video and audio capture and encryption

[0875] The smart glasses' camera and microphone capture the surrounding environment and collect video and audio data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[0876] Processing on the cloud server (server)

[0877] A cloud server receives and decrypts the encrypted data sent from the user's device. An AI model then analyzes the decrypted data to recognize specific landmarks and obstacles. Visual aids are generated based on the analyzed data and the user's emotional state.

[0878] Emotion engine analysis and tuning

[0879] The cloud server is equipped with an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. For example, it performs facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed. The emotion engine can then appropriately adjust the visual support information based on the user's emotional state. For example, if the user is feeling stressed, it may prioritize the display of warning information.

[0880] Generation and transmission of visual aids

[0881] Visual aids are generated based on the analysis results. These include obstacle warnings, route guidance, AR advertisements, etc. The generated visual aids are then encrypted again and sent to the user's device.

[0882] Displaying information (user's terminal)

[0883] The user's visual aid device receives and decrypts the encrypted data sent back from the cloud server. Finally, the decrypted visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[0884] Specific examples

[0885] For example, imagine a visually impaired user walking through a busy downtown area using this system. The user's smart glasses capture video of the area ahead and audio of the surrounding area, and these data are sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data, and an emotion engine recognizes the user's impatience from their voice tone and facial expression. As a result, the server prioritizes generating visual aids with high urgency (e.g., a message urging them to pause) and sends them to the smart glasses. The smart glasses overlay the received information on the user's field of vision, helping the user to act safely.

[0886] In this way, the system according to the present invention can provide useful visual aids in real time, while preserving privacy, and taking into account the user's emotional state.

[0887] The processing flow will be explained below.

[0888] Step 1:

[0889] The user's visual aid (smart glasses) is activated, and the camera and microphone capture the surrounding images and sounds in real time. The camera continuously captures images while the user is walking, and the microphone collects environmental sounds and the user's voice.

[0890] Step 2:

[0891] Video and audio data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[0892] Step 3:

[0893] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video and audio data.

[0894] Step 4:

[0895] The AI ​​analysis model on the server analyzes the decoded video and audio data, recognizing specific landmarks and obstacles from the video data and analyzing the user's tone of voice and surrounding sounds from the audio data.

[0896] Step 5:

[0897] The server's emotion engine determines the user's emotional state based on the analyzed data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed.

[0898] Step 6:

[0899] The server generates visual support information based on the analysis results and the user's emotional state, including obstacle warnings, route guidance, AR advertisements, and warnings and support based on the user's emotions.

[0900] Step 7:

[0901] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[0902] Step 8:

[0903] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[0904] Step 9:

[0905] The user's visual aid device then overlays the decoded visual aid information onto their field of vision, allowing them to see the necessary visual aid information in real time and act safely.

[0906] As a concrete example, consider a visually impaired user walking through a busy downtown area. The user's smart glasses capture the scenery ahead and the surrounding sounds, and send them to a cloud server. The server uses video analysis to recognize obstacles and audio analysis to detect that the user is nervous. As a result, the server generates a warning saying, "Obstacle ahead. Be careful," and sends it to the smart glasses. The smart glasses display this warning in the user's field of vision, allowing the user to safely avoid the obstacle.

[0907] Example 2

[0908] 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."

[0909] Currently, support systems for visually impaired users to navigate safely have limitations in their functionality. Furthermore, they do not provide visual support information that takes into account the user's emotional state, making it difficult to adequately alleviate the user's stress and tension. Therefore, there is a need for a system that provides real-time information about the surrounding environment while also presenting support information that corresponds to the user's emotional state.

[0910] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring video data and audio data from a visual aid device worn by a user, means for encrypting the video data and audio data and transmitting them to a cloud server, means for decrypting the video data and audio data received on the cloud server, means for analyzing the decrypted data using an AI model and recognizing specific landmarks and obstacles, means for analyzing the user's emotional state using the emotion engine and adjusting the visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, and means for displaying the returned visual aid information in the user's field of view. This makes it possible to provide safe, real-time visual aid information while taking the user's emotional state into consideration.

[0911] A "visual aid device" is a wearable device worn by a user, and includes a camera and a microphone to capture video and audio data of the surrounding area.

[0912] "Encryption" is the process of transforming data using a specific algorithm (e.g., AES) to make the data unintelligible to third parties.

[0913] "Decryption" is the process of returning encrypted data to its original state, making the data received by the cloud server ready for analysis.

[0914] A "cloud server" is a remote computing system that stores, processes, and analyzes data over a network.

[0915] An "AI model" is an algorithm or program that uses artificial intelligence to analyze data, and can analyze video and audio data to recognize specific landmarks and obstacles.

[0916] An "emotion engine" is a system for analyzing a user's emotional state, and is a technology for determining a user's emotional state by performing facial expression recognition and voice tone analysis.

[0917] "Visual support information" is information generated based on analyzed data, and includes obstacle warnings, route guidance information, AR advertisements, etc.

[0918] A "secure communication protocol" is a protocol that ensures security during data transfer (e.g., HTTPS).

[0919] "Landmarks" are specific important points or landmarks that an AI model recognizes.

[0920] An "obstacle" is an object or structure that may impede the user's movement and is recognized by the AI ​​model.

[0921] In this invention, the visual aid device worn by the user (hereinafter referred to as the terminal) plays a key role. The terminal is equipped with a camera, microphone, communication module, and display, and captures video and audio data in real time. The video and audio data are immediately encrypted using the AES encryption algorithm and transmitted to a cloud server via the secure HTTP protocol (HTTPS).

[0922] The cloud server decrypts the received encrypted data and prepares it for analysis. An AI analysis model within the cloud server analyzes the decrypted data and recognizes specific landmarks and obstacles. An emotion engine also analyzes the video and audio data to determine the user's emotional state. This allows the cloud server to determine whether the user is currently feeling stressed or relaxed.

[0923] The cloud server then generates visual support information based on the data analysis and the emotional state analysis results. The generated visual support information includes location information of specific landmarks and warning messages about obstacles. Furthermore, the priority of information is adjusted taking into account the results of the emotion engine. For example, if the user is feeling stressed, information with a high level of urgency will be provided first.

[0924] The generated visual aid information is then AES encrypted and sent to the device. The user's device receives the data, decrypts it, and then displays the visual aid information overlaid on the display. This allows the user to simultaneously view the actual environment and the aid information.

[0925] As a concrete example, consider a scenario in which a visually impaired user is walking through a busy shopping district. The device's camera captures video of the area ahead, and its microphone collects surrounding audio. This data is encrypted and sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data. At the same time, an emotion engine analyzes the user's emotional state and identifies that the user is feeling impatient. As a result, a warning message such as "Obstacle ahead. Please stop moving" is generated, re-encrypted, and sent to the device. The device displays this information on its display, helping the user navigate safely.

[0926] Examples of prompts include:

[0927] "In this system, a visually impaired user wears smart glasses and transmits video and audio of the surrounding area to a cloud server for analysis. The cloud server analyzes the video and audio data and generates visual support information based on the user's emotional state. This information is sent to the user's smart glasses and displayed together with real-world information. As a specific example, please explain a case where the cloud server prioritizes the generation of visual support information with high urgency."

[0928] The present invention makes it possible to provide useful visual aid information in real time while taking into account the user's emotional state.

[0929] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0930] Step 1:

[0931] The user wears a visual aid, and the device's camera and microphone capture the surroundings in real time. The input is video and audio data. This data is immediately encrypted using the AES encryption algorithm. The encrypted data is sent to a cloud server using the HTTPS protocol. The output is encrypted video and audio data.

[0932] Step 2:

[0933] The cloud server receives the encrypted data sent from the device. The decryption module in the server decrypts the data using the AES algorithm. The input is the encrypted data, and the output is the decrypted video and audio data. This data is stored for analysis by the AI ​​model.

[0934] Step 3:

[0935] The AI ​​analysis model on the cloud server analyzes and recognizes specific landmarks and obstacles based on the decoded video data. The input is the decoded video data, and the output is the landmark and obstacle information as an analysis result. The AI ​​model detects landmarks and obstacles in each frame and identifies the user's location and direction of travel.

[0936] Step 4:

[0937] The emotion engine on the cloud server analyzes the decoded audio and video data to determine the user's emotional state. The input is the decoded audio and video data, and the output is a judgment of the user's emotional state. The emotion engine uses facial expression recognition algorithms and voice tone analysis to determine whether the user is stressed or relaxed.

[0938] Step 5:

[0939] The cloud server generates visual support information based on the results of data analysis and emotion analysis. The input is landmark and obstacle information and the user's emotional state, and the output is adjusted visual support information. For example, if the user is feeling stressed, priority is given to information with a high level of urgency. The generated visual support information includes obstacle warnings and route guidance information.

[0940] Step 6:

[0941] The cloud server re-encrypts the generated visual aid information using AES and sends it to the user's device. The input is the generated visual aid information, and the output is the encrypted visual aid information. The encrypted data is sent to the device via the HTTPS protocol.

[0942] Step 7:

[0943] The user's device receives and decrypts the encrypted data sent from the cloud server. The input is the encrypted visual aid information, and the output is the decrypted visual aid information. The decrypted information is finally overlaid on the display and displayed in the user's field of view. This allows the user to simultaneously view the real environment and the visual aid information.

[0944] As a specific example of how it works, after a user puts on the visual aid device, information about obstacles and landmarks ahead is acquired and analyzed in real time as the user walks through a busy shopping district, and warnings and guidance based on the results are displayed in the user's field of vision, helping the user to move around safely.

[0945] (Application example 2)

[0946] 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."

[0947] Current visual aid devices can acquire and analyze a user's visual information, but it is difficult to consider the user's emotional state. As a result, they are unable to provide appropriate visual aid information based on the user's emotions, which increases the burden on the user, especially in stressful situations or complex environments. Another issue is that they are unable to provide appropriate aid information to make shopping more comfortable for users in physical stores.

[0948] The specific processing by the specific 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 means for acquiring video data from a visual aid device worn by a user, means for encrypting the video data and transmitting it to a cloud server, means for analyzing the received video data on the cloud server, means for generating visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, means for displaying the returned visual aid information in the user's field of view, and means for adjusting the visual aid information based on the user's emotional state. This makes it possible to provide appropriate visual aid information in real time according to the user's emotions, thereby alleviating user stress and improving the shopping experience, particularly in physical stores.

[0949] A "visual aid device" is a device worn by a user to provide visual assistance, and is equipped with a camera, a communication module, a display, etc.

[0950] "Video data" refers to video information of the surroundings captured by the camera of the visual aid device.

[0951] "Encryption" refers to the process of protecting video and other data using security protocols.

[0952] A "cloud server" is a remote computer system that exists on the Internet and stores, analyzes, and processes data.

[0953] An "AI model" is a computational model that uses algorithms such as machine learning or deep learning to analyze data and recognize specific patterns or features.

[0954] "Visual supplementary information" refers to supplementary visual information provided to the user, and includes route guidance information, obstacle warnings, product information, and the like.

[0955] An "emotion engine" is software or an algorithm that analyzes video and audio data to identify and analyze the user's emotional state.

[0956] A "security communication protocol" is a set of communication rules for safely sending and receiving data, and uses encryption technology to protect data.

[0957] System Configuration

[0958] This system acquires video data from a visual aid device, encrypts it, and sends it to a cloud server. The cloud server analyzes the received data and generates visual aid information based on the analysis results. The generated information is then re-encrypted and sent back to the user, who then displays it in the field of view of the visual aid device worn by the user. This allows the user to simultaneously view the real world and the aid information.

[0959] Hardware and software used

[0960] Visual aids: Wearable devices equipped with a camera, communication module, display, and microphone. Specifically, smart glasses (e.g., Google Glass) are used.

[0961] Cloud server: A remote computer system for data analysis and emotion recognition. AWS, Google Cloud, etc. are used.

[0962] Cryptography library: Uses Fernet from the cryptography package to ensure data security.

[0963] Communication protocol: HTTPS protocol is used to ensure secure data transmission.

[0964] Acquisition and transmission of video data

[0965] When a user wears the visual aid, the device's camera captures real-time images of the surroundings and instantly encrypts the data, which is then sent to a cloud server via a secure communication protocol.

[0966] Data analysis using a cloud server

[0967] The cloud server decrypts the received encrypted data and analyzes it using an AI model. This analysis identifies specific landmarks, obstacles, and in-store product information. The cloud server also incorporates an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. This analysis is achieved, for example, by performing facial expression recognition and voice tone analysis.

[0968] Creating and providing visual support information

[0969] Visual aids are generated based on the user's emotional state and the results of video analysis. This visual aid information includes obstacle warnings, route guidance, in-store product and sale information, etc. The generated information is then re-encrypted and sent back to the user's visual aid device.

[0970] Displaying Information

[0971] The visual aid device receives the encrypted data, decrypts it, and displays it in the user's field of view. This allows the user to simultaneously view the real-world image and the auxiliary information. This can be particularly useful in brick-and-mortar stores, where the aid information can be provided to make shopping more comfortable for the user.

[0972] Specific examples

[0973] For example, when a user visits a shopping mall, a visual aid device (smart glasses) captures the product shelves in the store. The cloud server recognizes these products and, based on that information, displays the product the user is looking for or recommended products as visual aid information. If the user shows a favorable expression toward a particular product, the emotion engine detects this and displays special sale information or discount coupons for that product.

[0974] Prompt Sentence Examples

[0975] "Identify products in a store and analyze the user's emotional state from video and audio data captured by the user wearing a visual aid. If the user is unsure, recommend products, or offer discounts if the user likes a particular product."

[0976] In this way, the system of the present invention can provide useful visual aids in real time while properly taking into account the user's emotional state.

[0977] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0978] Step 1:

[0979] The user wears a visual aid device, and the device's camera captures images of the surroundings. This data is input to the terminal as image data. The terminal immediately encrypts this image data. The encryption technology used is a high-security encryption algorithm such as AES. After encryption, the encrypted image data is sent to a cloud server by a communication module. The input is image data of the real environment, and the output is encrypted image data.

[0980] Step 2:

[0981] The cloud server receives the encrypted data. The server decrypts the received data and retrieves the original video data. Decryption is performed using a key shared between the sites. The input is the encrypted data, and the output is the decrypted video data. Specifically, this operation is handled by a dedicated decryption module on the cloud server.

[0982] Step 3:

[0983] The cloud server inputs the decoded video data into the AI ​​model and begins data analysis. The AI ​​model uses machine learning techniques to identify landmarks, obstacles, and in-store products. The result of data analysis is the location and attribute information of specific objects. The input is the decoded video data, and the output is information about the analyzed objects. Specifically, the cloud server runs the object detection algorithm.

[0984] Step 4:

[0985] In parallel, the cloud server inputs the acquired video and audio data into the emotion engine to analyze the user's emotional state. The emotion engine uses facial expression recognition technology and voice tone analysis technology to identify the user's emotional state. This analysis provides information such as whether the user is relaxed or nervous. The input is video and audio data, and the output is information about the user's emotional state. Specifically, the emotion engine performs pattern matching on facial expressions and voice tone.

[0986] Step 5:

[0987] The cloud server generates appropriate visual support information based on the analysis results and emotion recognition results. Information that is likely to interest the user, such as product information in the shop, special offers, and discount coupons, is generated. This information is adjusted according to the user's emotional state. For example, if the emotion engine indicates that the user is excited, related products and special offers will be displayed. The input is the data analysis results and emotion recognition results, and the output is visual support information.

[0988] Step 6:

[0989] The generated visual aid is again encrypted and sent back to the user's visual aid using the same encryption technology as used in step 1. To maintain the security of the information, all communication is done via HTTPS protocol. The input is the generated visual aid and the output is the encrypted information.

[0990] Step 7:

[0991] The user's visual aid device receives and decrypts the encrypted data sent from the cloud server. After decryption, the decrypted visual aid information is overlaid on the device's display, allowing the user to see the aid information in their field of vision in the real world. The input is the encrypted visual aid information, and the output is the information displayed in the user's field of vision. Specifically, the device's decryption module and display function work together to display the information.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] [Fourth embodiment]

[0996] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0997] 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.

[0998] 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).

[0999] 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.

[1000] 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.

[1001] 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).

[1002] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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.

[1007] 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.

[1008] 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."

[1009] Overview of the entire system

[1010] This invention is a system that uses a visual aid device worn by the user to collect surrounding video data, analyze it via a cloud server, and provide visual aids. Therefore, a wearable device such as smart glasses works in conjunction with a cloud-based AI analysis system. Specifically, it includes the following main elements:

[1011] 1. Visual aids (smart glasses)

[1012] 2. Cloud Server

[1013] 3. Communication Protocol

[1014] 4. AI Analysis Model

[1015] Program processing explanation

[1016] Visual aids (user's device)

[1017] The visual aid device worn by the user is equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. The user's device does not store any of the video data; all processing is done on the cloud server.

[1018] Video capture and encryption

[1019] The camera in the smart glasses captures the surrounding environment and collects video data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[1020] Processing on the cloud server (server)

[1021] The cloud server receives and decrypts the encrypted video data sent from the user's device. The AI ​​model then analyzes the decrypted video data. The AI ​​model has facial, object, and text recognition capabilities, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[1022] Generation of visual aids

[1023] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, or AR advertisements for the user. The generated visual aid information is then encrypted again and sent back to the user's visual aid device.

[1024] Displaying information (user's terminal)

[1025] The user's visual aid device receives and decodes the visual aid information sent back from the cloud server. Finally, the decoded visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[1026] Specific examples

[1027] For example, a visually impaired user may be using the system to navigate down a street.

[1028] 1. Video acquisition and transmission

[1029] The user wears the smart glasses and the camera captures images of the surrounding area.

[1030] The encrypted video data is sent to a cloud server.

[1031] 2. Analysis on the cloud server

[1032] The server receives the video data, decodes it, and performs AI analysis.

[1033] AI analysis recognizes important objects such as cars, pedestrians, and traffic lights.

[1034] 3. Generation and transmission of visual aids

[1035] Based on the recognition results, visual aid information including warning information for the user and the location of obstacles is generated.

[1036] The visual aid information is encrypted and sent back to the smart glasses.

[1037] 4. Displaying Information

[1038] The smart glasses receive and decode the visual aid information.

[1039] Auxiliary information is displayed superimposed on the user's field of vision to assist in safe walking.

[1040] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] The user's visual aid (smart glasses) is activated and the camera captures the surroundings in real time. The camera continuously captures images while the user is walking.

[1044] Step 2:

[1045] Video data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[1046] Step 3:

[1047] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video data.

[1048] Step 4:

[1049] The AI ​​model on the server analyzes the decoded video data and has facial, object, and character recognition capabilities to identify specific landmarks and obstacles.

[1050] Step 5:

[1051] The server generates visual aids based on the analysis results, including obstacle warnings, route guidance, and AR advertisements.

[1052] Step 6:

[1053] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[1054] Step 7:

[1055] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[1056] Step 8:

[1057] The user's visual aid device then displays the decoded visual aid information overlaid on the user's field of vision, allowing the user to view the necessary visual aid information in real time.

[1058] As a concrete example, consider a visually impaired user using the system while walking. The user's smart glasses capture the scenery ahead, and the cloud server analyzes it, identifying a traffic light ahead that is red. This information is encrypted and sent back to the user, where it is displayed as a red light on the smart glasses' display. This allows the user to stop and move safely.

[1059] Example 1

[1060] 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."

[1061] There is a need for an efficient method of providing information to visual aid devices that enable visually impaired users to move around safely. Conventional visual aid devices have difficulty performing highly accurate analysis in real time, and lack a mechanism for providing information while maintaining user privacy. This necessitates the development of a visual aid system that is user-friendly and enhances safety.

[1062] 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.

[1063] In this invention, the server includes a cloud server that decrypts data, a cloud server that encrypts the generated visual aid information, and a cloud server that analyzes the video data using an AI model. This enables the provision of highly accurate visual aid information to visually impaired users in real time, and enhances safety while protecting the user's privacy.

[1064] A "visual aid" is a wearable device that is worn by the user to supplement visual information and assist in safe movement.

[1065] "Video data" refers to visual information of the surrounding environment captured in real time by the camera of the visual aid device.

[1066] "Encryption" is a process that uses a special algorithm to convert acquired video data into a form that cannot be deciphered by others.

[1067] A "cloud server" is a server with computing resources in a remote location that can be accessed via a network, and is used to analyze, store, and communicate data.

[1068] "Analysis" is the process in which the cloud server processes the video data to extract and identify the necessary information.

[1069] "Visual aid information" is information that is generated based on analyzed video data and is used to aid the user's vision.

[1070] A "secure communication protocol" refers to a secure communication method that prevents eavesdropping or tampering by third parties when sending and receiving data.

[1071] An "AI model" is a computer program based on artificial intelligence algorithms used to analyze video data.

[1072] "Decryption" is the process of restoring encrypted data to its original form.

[1073] The present invention is a system that uses a visual aid device worn by a user to collect video data of the surroundings, analyzes it via a cloud server, and provides visual aid means. The overall configuration of the system is described in detail below.

[1074] Visual aids (user's device)

[1075] The visual aid worn by the user is a wearable device such as smart glasses. This visual aid is equipped with a camera, a communication module, and a display, and captures images of the surroundings in real time using the camera. The captured image data is immediately encrypted using an encryption algorithm (e.g., AES). The encrypted data is then sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[1076] Processing on the cloud server (server)

[1077] The cloud server receives and decrypts the encrypted video data sent from the user's device. The server then uses a decryption algorithm (e.g., AES) to restore the data to its original form. The AI ​​model then analyzes the decrypted video data. The AI ​​model has capabilities such as facial recognition, object recognition, and text recognition, allowing it to accurately identify specific landmarks, obstacles, and human faces.

[1078] After the analysis is complete, visual aid information is generated based on the video data. This visual aid information can include warnings, route guidance, AR advertisements, etc. The generated visual aid information is then encrypted again and sent back to the user's visual aid device using a secure communication protocol (e.g., HTTPS).

[1079] Displaying information (user's terminal)

[1080] The user's visual aid device receives the visual aid information sent back from the cloud server and decodes it again using a decoding algorithm. Finally, the decoded visual aid information is displayed superimposed on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[1081] Specific examples

[1082] For example, imagine a visually impaired user walking down the street using this system. The camera in the smart glasses captures images of the surrounding area, and the encrypted image data is sent to a cloud server. The cloud server decodes the received image data and uses an AI analysis model to recognize important objects such as cars, pedestrians, and traffic lights. Based on the recognition results, visual aid information is generated that includes warning information for the user and the location of obstacles, and the encrypted visual aid information is sent back to the smart glasses. The smart glasses receive the visual aid information, decode it, and display it overlaid on the user's field of vision, helping them walk safely.

[1083] Prompt Sentence Examples

[1084] "Please tell me how to support visually impaired users to walk safely by wearing a visual aid and using real-time analysis via a cloud server."

[1085] In this way, the system according to the present invention can provide useful visual aids in real time while preserving the user's privacy.

[1086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1087] Step 1: Acquire video data

[1088] The camera in the visual aid worn by the user captures the surrounding environment in real time. The input data is a real-world image of the surroundings, which the camera captures as video data. For example, the camera captures images of cars and pedestrians as the user walks down the street.

[1089] Step 2: Encrypting the video data

[1090] The video data captured by the device is immediately encrypted using an encryption algorithm (e.g., AES). The input is the captured video data, and the output is the encrypted data. Specifically, the AES algorithm is applied to the captured video frames to encrypt them.

[1091] Step 3: Sending Encrypted Data

[1092] The device sends encrypted video data to the cloud server via a secure communication protocol (e.g., HTTPS). The input is the encrypted video data, and the output is the sent data. Specifically, the data is sent using HTTPS.

[1093] Step 4: Receiving Encrypted Data

[1094] The server receives the encrypted video data sent from the user's terminal. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the data is received via the network.

[1095] Step 5: Decrypt the data

[1096] The server decrypts the encrypted data it receives using a decryption algorithm (e.g., AES). The input is the encrypted video data, and the output is the original video data. Specifically, the data is decrypted using the AES algorithm.

[1097] Step 6: Data analysis using AI

[1098] The server analyzes the decoded video data using an AI model. The input is the decoded video data, and the output is the analysis result. Specifically, it uses face recognition and object recognition models to identify important objects in the video.

[1099] Step 7: Generate visual aids

[1100] The server generates visual support information based on the analysis results. The input is the analysis result of the AI ​​model, and the output is visual support information. Specific operations include generating warnings and guidance information according to the recognized object and situation.

[1101] Step 8: Encrypt the auxiliary information

[1102] The visual auxiliary information generated by the server is again encrypted using an encryption algorithm. The input is the visual auxiliary information, and the output is the encrypted visual auxiliary information. Specifically, the information is encrypted using the AES algorithm.

[1103] Step 9: Sending encrypted auxiliary information

[1104] The server sends encrypted visual aid information to the user's visual aid device using a secure communication protocol (e.g., HTTPS). The input is the encrypted visual aid information, and the output is the transmitted data. Specifically, the information is sent using HTTPS.

[1105] Step 10: Receiving auxiliary information

[1106] The device receives encrypted visual aid information sent from the cloud server. The input is the sent encrypted data, and the output is the received encrypted data. Specifically, the device receives information via the network.

[1107] Step 11: Decoding the auxiliary information

[1108] The terminal decrypts the received visual aid information using a decryption algorithm. The input is the encrypted visual aid information, and the output is the original visual aid information. Specifically, the information is decrypted using the AES algorithm.

[1109] Step 12: Displaying information in the field of view

[1110] The terminal displays the decoded visual aid information superimposed on the user's field of vision. The input is the decoded visual aid information, and the output is the aid information displayed in the user's field of vision. Specific operations include displaying warning messages and route guidance information on the display.

[1111] (Application example 1)

[1112] 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."

[1113] The main function of conventional visual aids is to provide users with information about their surrounding environment. However, these devices do not perform adequately in the field of security monitoring. In particular, they do not meet advanced security requirements such as identifying intruders and detecting abnormal behavior. Therefore, in order to improve safety, a system that provides users with security-related information in real time is required.

[1114] 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.

[1115] In this invention, the server includes a cloud server that identifies intruders and detects abnormal behavior, a means for providing warning information to users, and a means for analyzing video data using a generative AI model, which enables security monitoring to quickly receive real-time warnings and important information via users' devices.

[1116] A "visual aid device worn by a user" is a wearable device that provides visual information when worn by a user.

[1117] "Video data" refers to visual information of the surroundings captured by a device such as a camera.

[1118] "Encryption" is the process of transforming data so that it cannot be easily deciphered by third parties.

[1119] A "cloud server" is a remote server available via the Internet that has the ability to store and process large amounts of data.

[1120] "Analysis" is the process of extracting information from acquired data and drawing conclusions according to the purpose.

[1121] "Visual aids" refers to additional visual data provided to help a user obtain information safely and efficiently.

[1122] "Intruder identification" refers to a technology that identifies unauthorized individuals based on video data.

[1123] "Abnormal behavior detection" refers to the technology of recognizing behavior that deviates from normal behavior patterns.

[1124] "Warning information" is a message for notifying the user of urgent information.

[1125] A "generative AI model" is a model trained using machine learning techniques and used to analyze video data, etc.

[1126] A "secure communication protocol" is a communication protocol that prevents unauthorized access by third parties when sending and receiving data.

[1127] This invention is a system that uses a user-worn visual aid device to collect surrounding video data, analyze it via a cloud server, and provide security-related visual aids. Specifically, it includes the following main elements:

[1128] 1. Visual aids (smart glasses)

[1129] The smart glasses worn by the user are equipped with a camera, a communication module, and a display. The device captures real-time images of the surroundings with the camera, encrypts the data, and sends it to a cloud server. No data is stored on the smart glasses themselves; all analysis and processing is done on the cloud server.

[1130] 2. Cloud Server

[1131] The cloud server receives and decrypts the encrypted video data sent from the user's device. It then analyzes the video data using a generative AI model. This AI model has functions such as identifying intruders, detecting abnormal behavior, and recognizing faces. Based on the analysis results, it generates visual aids or security warning information.

[1132] 3. Communication Protocol

[1133] Video data is sent and received using a secure communication protocol (HTTPS), which ensures data encryption and safe transmission.

[1134] 4. AI Analysis Model

[1135] The AI ​​analysis model on the cloud server is implemented using TensorFlow and OpenCV to analyze the video data, which detects suspicious behavior and abnormal movements.

[1136] 5. Generation of visual support information

[1137] Based on the analysis results, the cloud server generates visual aids or warning information, re-encrypts it, and sends it back to the user's smart glasses. The smart glasses then decrypt the received information and display it overlaid on the user's field of vision, allowing the user to obtain important security information in real time.

[1138] Specific examples

[1139] For example, consider the case where a security guard uses this system in a large commercial facility. The security guard wears smart glasses and patrols the facility. A camera captures video of the surrounding area, encrypts it, and sends it to a cloud server. The cloud server analyzes the video data and detects abnormal behavior (e.g., climbing over a wall or leaving a bag unattended). Based on this, a warning message is sent to the security guard and displayed on the smart glasses' display.

[1140] Prompt Sentence Examples

[1141] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[1142] In this way, the system according to the present invention can provide real-time visual aids to improve user safety in security surveillance.

[1143] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1144] Step 1:

[1145] The user wears the smart glasses, and the camera in the visual aid device captures images of the surrounding area.

[1146] Input: Real-time video data acquired by the camera.

[1147] Output: Video data.

[1148] How it works: A high-resolution camera mounted on the smart glasses captures the user's surroundings in real time and generates the images as digital data.

[1149] Step 2:

[1150] The video data captured by the visual aid device's camera is immediately encrypted using an encryption algorithm (e.g., AES).

[1151] Input: Acquired video data.

[1152] Output: Encrypted video data.

[1153] How it works: The encryption module built into the smart glasses converts the captured video data into a secure format using an encryption algorithm such as AES.

[1154] Step 3:

[1155] The encrypted video data is sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[1156] Input: Encrypted video data.

[1157] Output: Data sent to the cloud server.

[1158] Specific operation: The communication module of the smart glasses securely transmits encrypted video data to a cloud server using HTTPS.

[1159] Step 4:

[1160] The cloud server decrypts the received encrypted video data.

[1161] Input: Encrypted video data sent to the cloud server.

[1162] Output: Decoded video data.

[1163] Specific operation: A decryption module installed on the cloud server converts the video data sent in a secure format back into its original format.

[1164] Step 5:

[1165] The generated AI model on the cloud server analyzes the decoded video data.

[1166] Input: Decoded video data.

[1167] Output: Analysis results (identification of intruders, detection of abnormal behavior, etc.).

[1168] How it works: A generative AI model (e.g., TensorFlow, OpenCV) deployed on a cloud server analyzes video data and performs facial recognition and abnormal behavior detection. Specifically, it uses a pre-trained dataset to identify objects and people in the video.

[1169] Step 6:

[1170] The cloud server generates warning information based on the analysis results.

[1171] Input: Analysis results.

[1172] Output: Alert information (such as suspicious person alert messages and abnormal behavior notifications).

[1173] Specific operation: Based on the results of the AI ​​analysis model, a program on the server generates a warning message in a format that is easy for the user to understand. For example, the warning message might say, "Suspicious behavior has been detected. Please check for details."

[1174] Step 7:

[1175] The generated warning information is encrypted and transmitted to the user's visual aid.

[1176] Input: Warning information.

[1177] Output: The encrypted warning information sent to the smart glasses.

[1178] Specific operation: The cloud server re-encrypts the generated warning information and sends it to the user's visual aid device using a secure communication protocol.

[1179] Step 8:

[1180] The visual aid decodes the received warning information and displays it in the user's field of view.

[1181] Input: Encrypted warning information.

[1182] Output: Warning information displayed in the user's view.

[1183] Specific operation: A decryption module built into the smart glasses decrypts the warning information sent from the cloud server and displays it on the screen. For example, it might display information such as "There is a suspicious person in the facility. Please be careful."

[1184] Prompt Sentence Examples

[1185] "Analyze real-time video data from within the facility and recognize the following behaviors: human faces, abnormal behavior (climbing over walls, leaving bags unattended), and generate appropriate warning messages."

[1186] 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.

[1187] System Configuration

[1188] This invention is a system that acquires video data from a visual aid device worn by a user, sends it to a cloud server for analysis, and, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide visual aid information based on the user's emotional state.

[1189] Overview of the entire system

[1190] The system consists of the following elements:

[1191] 1. Visual aids (smart glasses)

[1192] 2. Cloud Server

[1193] 3. Communication Protocol

[1194] 4. AI Analysis Model

[1195] 5. Emotion Engine

[1196] Program processing explanation

[1197] Visual aids (user's device)

[1198] The visual aid device worn by the user is equipped with a camera, a communication module, a display, and a microphone. This device captures surrounding video and audio in real time, encrypts the data, and transmits it to a cloud server. The video and audio data is not stored on the user's device; all processing is done on the cloud server.

[1199] Video and audio capture and encryption

[1200] The smart glasses' camera and microphone capture the surrounding environment and collect video and audio data, which is then immediately encrypted using an encryption algorithm (e.g., AES) and sent to a cloud server via a secure communication protocol (e.g., HTTPS).

[1201] Processing on the cloud server (server)

[1202] A cloud server receives and decrypts the encrypted data sent from the user's device. An AI model then analyzes the decrypted data to recognize specific landmarks and obstacles. Visual aids are generated based on the analyzed data and the user's emotional state.

[1203] Emotion engine analysis and tuning

[1204] The cloud server is equipped with an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. For example, it performs facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed. The emotion engine can then appropriately adjust the visual support information based on the user's emotional state. For example, if the user is feeling stressed, it may prioritize the display of warning information.

[1205] Generation and transmission of visual aids

[1206] Visual aids are generated based on the analysis results. These include obstacle warnings, route guidance, AR advertisements, etc. The generated visual aids are then encrypted again and sent to the user's device.

[1207] Displaying information (user's terminal)

[1208] The user's visual aid device receives and decrypts the encrypted data sent back from the cloud server. Finally, the decrypted visual aid information is displayed overlaid on the user's field of view, allowing the user to simultaneously view real-world information and the aid information.

[1209] Specific examples

[1210] For example, imagine a visually impaired user walking through a busy downtown area using this system. The user's smart glasses capture video of the area ahead and audio of the surrounding area, and these data are sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data, and an emotion engine recognizes the user's impatience from their voice tone and facial expression. As a result, the server prioritizes generating visual aids with high urgency (e.g., a message urging them to pause) and sends them to the smart glasses. The smart glasses overlay the received information on the user's field of vision, helping the user to act safely.

[1211] In this way, the system according to the present invention can provide useful visual aids in real time, while preserving privacy, and taking into account the user's emotional state.

[1212] The processing flow will be explained below.

[1213] Step 1:

[1214] The user's visual aid (smart glasses) is activated, and the camera and microphone capture the surrounding images and sounds in real time. The camera continuously captures images while the user is walking, and the microphone collects environmental sounds and the user's voice.

[1215] Step 2:

[1216] Video and audio data captured on the user's device is immediately encrypted using an encryption algorithm such as AES. The encrypted data is not stored as is, but is sent to a cloud server via a secure communication protocol (HTTPS).

[1217] Step 3:

[1218] The cloud server receives the encrypted data sent from the user's device, where it is decrypted and restored to the original video and audio data.

[1219] Step 4:

[1220] The AI ​​analysis model on the server analyzes the decoded video and audio data, recognizing specific landmarks and obstacles from the video data and analyzing the user's tone of voice and surrounding sounds from the audio data.

[1221] Step 5:

[1222] The server's emotion engine determines the user's emotional state based on the analyzed data, for example, by using facial expression recognition and voice tone analysis to determine whether the user is stressed or relaxed.

[1223] Step 6:

[1224] The server generates visual support information based on the analysis results and the user's emotional state, including obstacle warnings, route guidance, AR advertisements, and warnings and support based on the user's emotions.

[1225] Step 7:

[1226] The generated visual aid information is then encrypted again using an encryption algorithm to ensure a secure environment before being sent to the user's device.

[1227] Step 8:

[1228] The user's visual aid device receives the encrypted data returned from the cloud server, performs a decryption process, and obtains the visual aid information.

[1229] Step 9:

[1230] The user's visual aid device then overlays the decoded visual aid information onto their field of vision, allowing them to see the necessary visual aid information in real time and act safely.

[1231] As a concrete example, consider a visually impaired user walking through a busy downtown area. The user's smart glasses capture the scenery ahead and the surrounding sounds, and send them to a cloud server. The server uses video analysis to recognize obstacles and audio analysis to detect that the user is nervous. As a result, the server generates a warning saying, "Obstacle ahead. Be careful," and sends it to the smart glasses. The smart glasses display this warning in the user's field of vision, allowing the user to safely avoid the obstacle.

[1232] Example 2

[1233] 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."

[1234] Currently, support systems for visually impaired users to navigate safely have limitations in their functionality. Furthermore, they do not provide visual support information that takes into account the user's emotional state, making it difficult to adequately alleviate the user's stress and tension. Therefore, there is a need for a system that provides real-time information about the surrounding environment while also presenting support information that corresponds to the user's emotional state.

[1235] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring video data and audio data from a visual aid device worn by a user, means for encrypting the video data and audio data and transmitting them to a cloud server, means for decrypting the video data and audio data received on the cloud server, means for analyzing the decrypted data using an AI model and recognizing specific landmarks and obstacles, means for analyzing the user's emotional state using the emotion engine and adjusting the visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, and means for displaying the returned visual aid information in the user's field of view. This makes it possible to provide safe, real-time visual aid information while taking the user's emotional state into consideration.

[1236] A "visual aid device" is a wearable device worn by a user, and includes a camera and a microphone to capture video and audio data of the surrounding area.

[1237] "Encryption" is the process of transforming data using a specific algorithm (e.g., AES) to make the data unintelligible to third parties.

[1238] "Decryption" is the process of returning encrypted data to its original state, making the data received by the cloud server ready for analysis.

[1239] A "cloud server" is a remote computing system that stores, processes, and analyzes data over a network.

[1240] An "AI model" is an algorithm or program that uses artificial intelligence to analyze data, and can analyze video and audio data to recognize specific landmarks and obstacles.

[1241] An "emotion engine" is a system for analyzing a user's emotional state, and is a technology for determining a user's emotional state by performing facial expression recognition and voice tone analysis.

[1242] "Visual support information" is information generated based on analyzed data, and includes obstacle warnings, route guidance information, AR advertisements, etc.

[1243] A "secure communication protocol" is a protocol that ensures security during data transfer (e.g., HTTPS).

[1244] "Landmarks" are specific important points or landmarks that an AI model recognizes.

[1245] An "obstacle" is an object or structure that may impede the user's movement and is recognized by the AI ​​model.

[1246] In this invention, the visual aid device worn by the user (hereinafter referred to as the terminal) plays a key role. The terminal is equipped with a camera, microphone, communication module, and display, and captures video and audio data in real time. The video and audio data are immediately encrypted using the AES encryption algorithm and transmitted to a cloud server via the secure HTTP protocol (HTTPS).

[1247] The cloud server decrypts the received encrypted data and prepares it for analysis. An AI analysis model within the cloud server analyzes the decrypted data and recognizes specific landmarks and obstacles. An emotion engine also analyzes the video and audio data to determine the user's emotional state. This allows the cloud server to determine whether the user is currently feeling stressed or relaxed.

[1248] The cloud server then generates visual support information based on the data analysis and the emotional state analysis results. The generated visual support information includes location information of specific landmarks and warning messages about obstacles. Furthermore, the priority of information is adjusted taking into account the results of the emotion engine. For example, if the user is feeling stressed, information with a high level of urgency will be provided first.

[1249] The generated visual aid information is then AES encrypted and sent to the device. The user's device receives the data, decrypts it, and then displays the visual aid information overlaid on the display. This allows the user to simultaneously view the actual environment and the aid information.

[1250] As a concrete example, consider a scenario in which a visually impaired user is walking through a busy shopping district. The device's camera captures video of the area ahead, and its microphone collects surrounding audio. This data is encrypted and sent to a cloud server. On the cloud server, an AI model recognizes specific landmarks and obstacles from the video data. At the same time, an emotion engine analyzes the user's emotional state and identifies that the user is feeling impatient. As a result, a warning message such as "Obstacle ahead. Please stop moving" is generated, re-encrypted, and sent to the device. The device displays this information on its display, helping the user navigate safely.

[1251] Examples of prompts include:

[1252] "In this system, a visually impaired user wears smart glasses and transmits video and audio of the surrounding area to a cloud server for analysis. The cloud server analyzes the video and audio data and generates visual support information based on the user's emotional state. This information is sent to the user's smart glasses and displayed together with real-world information. As a specific example, please explain a case where the cloud server prioritizes the generation of visual support information with high urgency."

[1253] The present invention makes it possible to provide useful visual aid information in real time while taking into account the user's emotional state.

[1254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1255] Step 1:

[1256] The user wears a visual aid, and the device's camera and microphone capture the surroundings in real time. The input is video and audio data. This data is immediately encrypted using the AES encryption algorithm. The encrypted data is sent to a cloud server using the HTTPS protocol. The output is encrypted video and audio data.

[1257] Step 2:

[1258] The cloud server receives the encrypted data sent from the device. The decryption module in the server decrypts the data using the AES algorithm. The input is the encrypted data, and the output is the decrypted video and audio data. This data is stored for analysis by the AI ​​model.

[1259] Step 3:

[1260] The AI ​​analysis model on the cloud server analyzes and recognizes specific landmarks and obstacles based on the decoded video data. The input is the decoded video data, and the output is the landmark and obstacle information as an analysis result. The AI ​​model detects landmarks and obstacles in each frame and identifies the user's location and direction of travel.

[1261] Step 4:

[1262] The emotion engine on the cloud server analyzes the decoded audio and video data to determine the user's emotional state. The input is the decoded audio and video data, and the output is a judgment of the user's emotional state. The emotion engine uses facial expression recognition algorithms and voice tone analysis to determine whether the user is stressed or relaxed.

[1263] Step 5:

[1264] The cloud server generates visual support information based on the results of data analysis and emotion analysis. The input is landmark and obstacle information and the user's emotional state, and the output is adjusted visual support information. For example, if the user is feeling stressed, priority is given to information with a high level of urgency. The generated visual support information includes obstacle warnings and route guidance information.

[1265] Step 6:

[1266] The cloud server re-encrypts the generated visual aid information using AES and sends it to the user's device. The input is the generated visual aid information, and the output is the encrypted visual aid information. The encrypted data is sent to the device via the HTTPS protocol.

[1267] Step 7:

[1268] The user's device receives and decrypts the encrypted data sent from the cloud server. The input is the encrypted visual aid information, and the output is the decrypted visual aid information. The decrypted information is finally overlaid on the display and displayed in the user's field of view. This allows the user to simultaneously view the real environment and the visual aid information.

[1269] As a specific example of how it works, after a user puts on the visual aid device, information about obstacles and landmarks ahead is acquired and analyzed in real time as the user walks through a busy shopping district, and warnings and guidance based on the results are displayed in the user's field of vision, helping the user to move around safely.

[1270] (Application example 2)

[1271] 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."

[1272] Current visual aid devices can acquire and analyze a user's visual information, but it is difficult to consider the user's emotional state. As a result, they are unable to provide appropriate visual aid information based on the user's emotions, which increases the burden on the user, especially in stressful situations or complex environments. Another issue is that they are unable to provide appropriate aid information to make shopping more comfortable for users in physical stores.

[1273] The specific processing by the specific 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 means for acquiring video data from a visual aid device worn by a user, means for encrypting the video data and transmitting it to a cloud server, means for analyzing the received video data on the cloud server, means for generating visual aid information based on the analysis results, means for re-encrypting the generated visual aid information and returning it to the user, means for displaying the returned visual aid information in the user's field of view, and means for adjusting the visual aid information based on the user's emotional state. This makes it possible to provide appropriate visual aid information in real time according to the user's emotions, thereby alleviating user stress and improving the shopping experience, particularly in physical stores.

[1274] A "visual aid device" is a device worn by a user to provide visual assistance, and is equipped with a camera, a communication module, a display, etc.

[1275] "Video data" refers to video information of the surroundings captured by the camera of the visual aid device.

[1276] "Encryption" refers to the process of protecting video and other data using security protocols.

[1277] A "cloud server" is a remote computer system that exists on the Internet and stores, analyzes, and processes data.

[1278] An "AI model" is a computational model that uses algorithms such as machine learning or deep learning to analyze data and recognize specific patterns or features.

[1279] "Visual supplementary information" refers to supplementary visual information provided to the user, and includes route guidance information, obstacle warnings, product information, and the like.

[1280] An "emotion engine" is software or an algorithm that analyzes video and audio data to identify and analyze the user's emotional state.

[1281] A "security communication protocol" is a set of communication rules for safely sending and receiving data, and uses encryption technology to protect data.

[1282] System Configuration

[1283] This system acquires video data from a visual aid device, encrypts it, and sends it to a cloud server. The cloud server analyzes the received data and generates visual aid information based on the analysis results. The generated information is then re-encrypted and sent back to the user, who then displays it in the field of view of the visual aid device worn by the user. This allows the user to simultaneously view the real world and the aid information.

[1284] Hardware and software used

[1285] Visual aids: Wearable devices equipped with a camera, communication module, display, and microphone. Specifically, smart glasses (e.g., Google Glass) are used.

[1286] Cloud server: A remote computer system for data analysis and emotion recognition. AWS, Google Cloud, etc. are used.

[1287] Cryptography library: Uses Fernet from the cryptography package to ensure data security.

[1288] Communication protocol: HTTPS protocol is used to ensure secure data transmission.

[1289] Acquisition and transmission of video data

[1290] When a user wears the visual aid, the device's camera captures real-time images of the surroundings and instantly encrypts the data, which is then sent to a cloud server via a secure communication protocol.

[1291] Data analysis using a cloud server

[1292] The cloud server decrypts the received encrypted data and analyzes it using an AI model. This analysis identifies specific landmarks, obstacles, and in-store product information. The cloud server also incorporates an emotion engine that analyzes the captured video and audio data to identify the user's emotional state. This analysis is achieved, for example, by performing facial expression recognition and voice tone analysis.

[1293] Creating and providing visual support information

[1294] Visual aids are generated based on the user's emotional state and the results of video analysis. This visual aid information includes obstacle warnings, route guidance, in-store product and sale information, etc. The generated information is then re-encrypted and sent back to the user's visual aid device.

[1295] Displaying Information

[1296] The visual aid device receives the encrypted data, decrypts it, and displays it in the user's field of view. This allows the user to simultaneously view the real-world image and the auxiliary information. This can be particularly useful in brick-and-mortar stores, where the aid information can be provided to make shopping more comfortable for the user.

[1297] Specific examples

[1298] For example, when a user visits a shopping mall, a visual aid device (smart glasses) captures the product shelves in the store. The cloud server recognizes these products and, based on that information, displays the product the user is looking for or recommended products as visual aid information. If the user shows a favorable expression toward a particular product, the emotion engine detects this and displays special sale information or discount coupons for that product.

[1299] Prompt Sentence Examples

[1300] "Identify products in a store and analyze the user's emotional state from video and audio data captured by the user wearing a visual aid. If the user is unsure, recommend products, or offer discounts if the user likes a particular product."

[1301] In this way, the system of the present invention can provide useful visual aids in real time while properly taking into account the user's emotional state.

[1302] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1303] Step 1:

[1304] The user wears a visual aid device, and the device's camera captures images of the surroundings. This data is input to the terminal as image data. The terminal immediately encrypts this image data. The encryption technology used is a high-security encryption algorithm such as AES. After encryption, the encrypted image data is sent to a cloud server by a communication module. The input is image data of the real environment, and the output is encrypted image data.

[1305] Step 2:

[1306] The cloud server receives the encrypted data. The server decrypts the received data and retrieves the original video data. Decryption is performed using a key shared between the sites. The input is the encrypted data, and the output is the decrypted video data. Specifically, this operation is handled by a dedicated decryption module on the cloud server.

[1307] Step 3:

[1308] The cloud server inputs the decoded video data into the AI ​​model and begins data analysis. The AI ​​model uses machine learning techniques to identify landmarks, obstacles, and in-store products. The result of data analysis is the location and attribute information of specific objects. The input is the decoded video data, and the output is information about the analyzed objects. Specifically, the cloud server runs the object detection algorithm.

[1309] Step 4:

[1310] In parallel, the cloud server inputs the acquired video and audio data into the emotion engine to analyze the user's emotional state. The emotion engine uses facial expression recognition technology and voice tone analysis technology to identify the user's emotional state. This analysis provides information such as whether the user is relaxed or nervous. The input is video and audio data, and the output is information about the user's emotional state. Specifically, the emotion engine performs pattern matching on facial expressions and voice tone.

[1311] Step 5:

[1312] The cloud server generates appropriate visual support information based on the analysis results and emotion recognition results. Information that is likely to interest the user, such as product information in the shop, special offers, and discount coupons, is generated. This information is adjusted according to the user's emotional state. For example, if the emotion engine indicates that the user is excited, related products and special offers will be displayed. The input is the data analysis results and emotion recognition results, and the output is visual support information.

[1313] Step 6:

[1314] The generated visual aid is again encrypted and sent back to the user's visual aid using the same encryption technology as used in step 1. To maintain the security of the information, all communication is done via HTTPS protocol. The input is the generated visual aid and the output is the encrypted information.

[1315] Step 7:

[1316] The user's visual aid device receives and decrypts the encrypted data sent from the cloud server. After decryption, the decrypted visual aid information is overlaid on the device's display, allowing the user to see the aid information in their field of vision in the real world. The input is the encrypted visual aid information, and the output is the information displayed in the user's field of vision. Specifically, the device's decryption module and display function work together to display the information.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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).

[1324] 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.

[1325] 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."

[1326] 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.

[1327] 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).

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] The following is further disclosed regarding the above embodiment.

[1339] (Claim 1)

[1340] means for acquiring video data from a visual aid device worn by a user;

[1341] means for encrypting the video data and transmitting the data to a cloud server;

[1342] means for analyzing the received video data on the cloud server;

[1343] means for generating visual aid information based on the analysis results;

[1344] means for re-encrypting the generated visual aid information and returning it to the user;

[1345] means for displaying the returned visual aid information in the user's field of view;

[1346] A system including:

[1347] (Claim 2)

[1348] means for the cloud server to analyze video data using an AI model;

[1349] a means for generating obstacle locations and route guidance information based on the analysis results;

[1350] 10. The system of claim 1, comprising:

[1351] (Claim 3)

[1352] The visual aid device worn by the user is a wearable device equipped with a camera;

[1353] A means for encrypting and transmitting / receiving the video data using a secure communication protocol;

[1354] 10. The system of claim 1, comprising:

[1355] (Claim 4)

[1356] a means for displaying the generated visual aid information superimposed on the user's field of vision;

[1357] 10. The system of claim 1, comprising:

[1358] "Example 1"

[1359] (Claim 1)

[1360] means for acquiring video data from a visual aid device worn by a user;

[1361] means for encrypting the video data and transmitting the data to a cloud server;

[1362] means for analyzing the received video data on the cloud server;

[1363] means for generating visual aid information based on the analysis results;

[1364] means for re-encrypting the generated visual aid information and returning it to the user;

[1365] means for displaying the returned visual aid information in the user's field of view;

[1366] a means for the cloud server to decrypt the data; and

[1367] means for encrypting the visual aid information generated by the cloud server;

[1368] A system including:

[1369] (Claim 2)

[1370] means for the cloud server to analyze video data using an AI model;

[1371] a means for generating obstacle locations and route guidance information based on the analysis results;

[1372] 10. The system of claim 1, comprising:

[1373] (Claim 3)

[1374] The visual aid device worn by the user is a wearable device equipped with a camera;

[1375] A means for encrypting and transmitting / receiving the video data using a secure communication protocol;

[1376] 10. The system of claim 1, comprising:

[1377] "Application Example 1"

[1378] (Claim 1)

[1379] means for acquiring video data from a visual aid device worn by a user;

[1380] means for encrypting the video data and transmitting the data to a cloud server;

[1381] means for analyzing the received video data on the cloud server;

[1382] means for generating visual aid information based on the analysis results;

[1383] means for re-encrypting the generated visual aid information and returning it to the user;

[1384] means for displaying the returned visual aid information in the user's field of view;

[1385] A means for the cloud server to identify intruders and detect abnormal behavior;

[1386] means for providing warning information to a user;

[1387] A system including:

[1388] (Claim 2)

[1389] A means for the cloud server to analyze video data using the generated AI model;

[1390] means for generating security warning information based on the analysis result;

[1391] 10. The system of claim 1, comprising:

[1392] (Claim 3)

[1393] The visual aid device worn by the user is a wearable device equipped with a camera;

[1394] A means for encrypting and transmitting / receiving the video data using a secure communication protocol;

[1395] 10. The system of claim 1, comprising:

[1396] "Example 2: Combining Emotion Engines"

[1397] (Claim 1)

[1398] means for acquiring video data and audio data from a visual aid device worn by a user;

[1399] means for encrypting the video data and audio data and transmitting the data to a cloud server;

[1400] means for decoding the received video data and audio data on the cloud server;

[1401] means for analyzing the decoded data using an AI model to recognize specific landmarks and obstacles;

[1402] means for analyzing the emotional state of a user using the emotion engine and adjusting visual auxiliary information based on the analysis result;

[1403] means for re-encrypting the generated visual aid information and returning it to the user;

[1404] means for displaying the returned visual aid information in the user's field of view;

[1405] A system including:

[1406] (Claim 2)

[1407] A cloud server uses an AI model to analyze the video and audio data and recognize specific landmarks and obstacles; and

[1408] The system according to claim 1, further comprising means for generating visual auxiliary information based on the analysis result, and for adjusting the visual auxiliary information based on the emotional state of the user.

[1409] (Claim 3)

[1410] A means for the visual aid device worn by the user to be a wearable device equipped with a camera and a microphone;

[1411] 2. The system of claim 1, wherein the encryption and transmission of the video and audio data includes using a secure communication protocol.

[1412] "Application example 2 when combining emotion engines"

[1413] (Claim 1)

[1414] means for acquiring video data from a visual aid device worn by a user;

[1415] means for encrypting the video data and transmitting the data to a cloud server;

[1416] means for analyzing the received video data on the cloud server;

[1417] means for generating visual aid information based on the analysis results;

[1418] means for re-encrypting the generated visual aid information and returning it to the user;

[1419] means for displaying the returned visual aid information in the user's field of view;

[1420] means for adjusting the visual aid information based on the emotional state of the user;

[1421] A system including:

[1422] (Claim 2)

[1423] means for the cloud server to analyze video data using an AI model;

[1424] means for generating obstacle locations, route guidance information, and store product information and sale information based on the analysis results;

[1425] 10. The system of claim 1, comprising:

[1426] (Claim 3)

[1427] The visual aid device worn by the user is a wearable device equipped with a camera and a communication module;

[1428] A means for encrypting and transmitting / receiving the video data using a security communication protocol;

[1429] 10. The system of claim 1, comprising: [Explanation of symbols]

[1430] 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. means for acquiring video data from a visual aid device worn by a user; means for encrypting the video data and transmitting the data to a cloud server; means for analyzing the received video data on the cloud server; means for generating visual aid information based on the analysis results; means for re-encrypting the generated visual aid information and returning it to the user; means for displaying the returned visual aid information in the user's field of view; A system including:

2. means for the cloud server to analyze video data using an AI model; a means for generating obstacle locations and route guidance information based on the analysis results; The system of claim 1 , comprising:

3. The visual aid device worn by the user is a wearable device equipped with a camera; A means for encrypting and transmitting / receiving the video data using a secure communication protocol; The system of claim 1 , comprising:

4. a means for displaying the generated visual aid information superimposed on the user's field of vision; The system of claim 1 , comprising:

Images