system

Abstract

Provide a system. A means for acquiring image data using a camera worn by a user; A means for acquiring location information; means for transmitting the image data and the position information to a server via a communication network; means for analyzing the image data and the location information in the server; means for generating feedback to a user based on the analysis results in the server; means for transmitting the feedback to the user via the communication network and outputting it as a voice; A system including:

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] It is extremely difficult for visually impaired people to grasp their surroundings and risks in real time. Currently, guide dogs and white canes are common means of support, but the number of guide dogs is limited, and white canes can only detect a limited range. Furthermore, relying on others creates a psychological barrier, making it difficult for them to act independently. The present invention aims to solve these problems and provide a support system that enables visually impaired people to act independently and safely without the intervention of sighted people. [Means for solving the problem]

[0005] The present invention includes a means for acquiring image data using a camera worn by a user, a means for acquiring location information, and a means for transmitting the image data and the location information to a server via a communication network. The server has a means for analyzing the image data and the location information and a means for generating feedback to the user based on the analysis results. The system further includes a means for transmitting the feedback to the user via the communication network and outputting it as audio. Such a system allows visually impaired people to obtain information about their surrounding environment in real time and act safely.

[0006] "User" refers to a blind or otherwise at-risk individual who uses the system.

[0007] "Worn camera" refers to a photographic device that can be worn by a user and that continuously captures images of its surroundings.

[0008] "Image data" refers to data that digitally represents visual information acquired by a mounted camera.

[0009] "Location Information" refers to the longitude and latitude data of a user's current location obtained using GPS or other location measurement technology.

[0010] "Communication network" refers to a network infrastructure for transmitting and receiving digital data, and in this invention specifically refers to 5G communication.

[0011] "Server" refers to a computer system for receiving transmitted image data and location information, analyzing it, and generating feedback.

[0012] "Means of analysis" refers to a method of processing image data and location information using generative AI models and image analysis algorithms within the server to identify information about the surrounding environment.

[0013] "Feedback" refers to instructions or warnings in natural language that are generated by the server based on the analysis results and presented to the user.

[0014] "Audio output means" refers to a device such as a speaker or earpiece for transmitting the generated feedback to the user as an audio message. [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] The purpose of this invention is to provide a real-time support system for visually impaired people and other users at risk to act independently and safely. This system consists of the following main components:

[0037] 1. Acquiring images and location information

[0038] The system uses a wearable camera and GPS module worn by the user to acquire image data of the surroundings and the user's location. The wearable camera is attached at a position close to the user's eye level and continuously captures real-time visual information while walking. The GPS module measures the user's current location with high accuracy.

[0039] 2. Sending images and location information to the server

[0040] The device transmits the acquired image data and location information to the server in real time. 5G communication enables high-speed, low-latency data transfer, allowing the server to constantly provide up-to-date environmental information, even while the user is on the move.

[0041] 3. Analysis of Environmental Data

[0042] The server analyzes the surrounding environment using generative AI models and image analysis algorithms based on the received image data and location information. For example, the image analysis algorithm identifies objects such as intersections, traffic lights, pedestrians, and vehicles, and then pinpoints their locations based on the location information. Furthermore, the server complements the analysis results with high-precision map information from Mapbox.

[0043] 4. Generating risk and environmental information

[0044] Based on the analysis results, the server generates feedback in natural language to help users act safely. The feedback includes real-time risk information such as the status of traffic lights at intersections, approaching vehicles, and pedestrian movements. The focus is on providing feedback that allows users to respond quickly and appropriately.

[0045] 5. Submitting Feedback

[0046] The server then sends the generated feedback to the device via 5G, allowing users to receive the information they need with low latency.

[0047] 6. Real-time feedback

[0048] The device then provides the received feedback to the user via an audio output device (speaker or earpiece). Real-time audio guidance allows even visually impaired users to instantly understand the current situation and take appropriate action. The device can also provide additional notification features, such as vibration feedback, if needed.

[0049] Specific examples

[0050] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information using GPS. This information is sent to the server via 5G communication. The server identifies the traffic light using image analysis and determines the current traffic light status by referring to map information from Mapbox. It then generates instructions such as "The light is red, so stop and wait" and sends them to the device. The device then communicates this to the user via voice.

[0051] Similarly, if a user is walking down a sidewalk and is about to come across a staircase, the device will acquire image and location information, and the server will detect the presence of the staircase. The server will then generate a warning such as "There are stairs 10 meters ahead. Please proceed with caution" and notify the user via the device.

[0052] This system enables visually impaired people to understand their surroundings in real time and act safely. Furthermore, by improving the accuracy of analysis and the quality of feedback on the server, we can expect to further improve the user experience.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The device uses a wearable camera to capture images of the surroundings every second, and also uses a GPS module to obtain its current location (latitude and longitude).

[0056] Step 2:

[0057] The device then converts the captured image data and location information into a data packet and sends it to the server using 5G communications.

[0058] Step 3:

[0059] The server inputs the received image data and location information into an AI model for analysis, which uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.).

[0060] Step 4:

[0061] The server then calls a map API (e.g., Mapbox) to obtain detailed geographic information based on the location information, such as intersections, sidewalks, and buildings at the user's current location.

[0062] Step 5:

[0063] The server combines the image analysis results with map information and generates the feedback the user needs to act safely, such as "The traffic light is red. Please stop before the crosswalk."

[0064] Step 6:

[0065] The server converts the generated feedback into data packets and sends them back to the device using 5G communications.

[0066] Step 7:

[0067] The device converts the received feedback into audio messages and provides them to the user in real time through a speaker or earpiece, along with vibration feedback if necessary.

[0068] Example 1

[0069] 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."

[0070] For visually impaired and other at-risk users to act independently and safely, they need to acquire information about their surroundings in real time and receive appropriate feedback. However, conventional systems often experience delays in acquiring and analyzing this information and generating and providing feedback, which can prevent users from responding appropriately. This can result in safety issues. Furthermore, previous technologies have not yet realized the provision of real-time feedback using generative AI models.

[0071] 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.

[0072] In this invention, the server includes means for analyzing image data and location information, means for generating feedback to the user using a generative AI model, and means for transmitting the feedback to the user via a communication network and outputting it as audio, thereby enabling visually impaired and other at-risk users to understand their surroundings in real time and take appropriate action.

[0073] "User" refers to the individual end user of the system, particularly those who are visually impaired or otherwise at risk.

[0074] A "camera" refers to a device worn by a user that captures image data of the surroundings in real time.

[0075] "Image data" refers to digital data of visual information captured using a camera.

[0076] "Location Information" means geographic information obtained using a GPS module or other location tracking technology to identify a User's current location.

[0077] "Communications Network" means the network infrastructure used to transmit and receive data in real time, and specifically includes fifth generation mobile communications systems (5G).

[0078] "Server" refers to a sophisticated computer system for analyzing acquired data and generating and transmitting feedback.

[0079] "Image analysis" refers to algorithms and techniques used to identify specific objects or situations based on acquired image data.

[0080] A "generative AI model" refers to an artificial intelligence model that performs natural language processing and advanced data analysis, and is specifically used to generate feedback for users.

[0081] "Feedback" refers to instructions or information that the server generates based on the analysis results and provides to the user.

[0082] "Audio output" refers to an audio notification method for conveying feedback to the user, including devices such as speakers and earpieces.

[0083] This invention is a support system for visually impaired and other at-risk users to move independently and safely. The system uses a wearable camera, a GPS module, a generative AI model, image analysis algorithms, and advanced communication network technology.

[0084] The basic hardware configuration involves the user wearing a wearable camera and a GPS module. The wearable camera is attached close to the user's viewpoint and captures visual information of the surroundings in real time. The GPS module is responsible for measuring the user's current location with high accuracy.

[0085] Data Acquisition

[0086] As the user moves, the wearable camera continuously captures image data, and the GPS module acquires location information, which is then immediately sent to the device and transmitted to a server via 5G communication.

[0087] Data analysis

[0088] The server analyzes the environment using generative AI models and image analysis algorithms based on the received image data and location information. Specifically, it uses object detection algorithms (e.g., YOLO - You Only Look Once) to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then complements the analysis results with high-precision map information from Mapbox.

[0089] Generate feedback

[0090] The server generates feedback for the user based on the analysis results. A generative AI model (e.g., GPT-4 (registered trademark)) creates feedback content in natural language based on the analysis results. The following is an example of a prompt sentence:

[0091] "There is a red light at the intersection. Please create a description to instruct the user to stop."

[0092] By entering such a prompt, appropriate feedback is generated, such as "The light is red, so stop and wait."

[0093] Sending and outputting feedback

[0094] The generated feedback is transmitted to the device via 5G communication, and the device provides the received feedback to the user via an audio output device (speaker or earpiece), and can also have additional notification features such as vibration feedback.

[0095] Specific examples

[0096] For example, when a user approaches an intersection, the device captures an image and obtains GPS location information. This information is sent in real time to a server, which then uses image analysis to determine the traffic light status and uses a generative AI model to generate an instruction such as "The light is red, so stop and wait." This instruction is then sent to the device and communicated to the user via a voice output device. Similarly, if a user is walking down a sidewalk and comes across stairs, the device will use image analysis and location information to detect their presence and generate a warning saying, "There are stairs 10 meters ahead. Proceed with caution."

[0097] The above is a specific embodiment of the present invention. This system enables the user to act independently and safely.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The user puts on the wearable camera and GPS module and starts the system.

[0101] Input: User's visual information (image data) and current location information

[0102] Output: Real-time image data and location information

[0103] How it works: The wearable camera captures images at a rate of 30 frames per second, and the GPS module updates the user's location every second.

[0104] Step 2:

[0105] The device transmits the acquired image data and location information to the server in real time.

[0106] Input: Real-time image data and location information

[0107] Output: Data packet containing compressed image data and location information

[0108] Specific operation: The terminal compresses and packets the acquired data via the 5G communication module and sends it to the server. At this time, data priority is set and packet retransmission control is performed to minimize data transmission delays.

[0109] Step 3:

[0110] The server applies a generated AI model and image analysis algorithm based on the received image data and location information to analyze the surrounding environment.

[0111] Input: Compressed image data and location information

[0112] Output: Analyzed environmental information (object position, type, etc.)

[0113] How it works: The server decodes the received data and uses object detection algorithms to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then uses Mapbox's API to obtain high-precision map information and complete the location of objects and situations.

[0114] Step 4:

[0115] The server uses a generative AI model based on the analysis results to generate feedback for the user.

[0116] Input: Parsed environment information

[0117] Output: Feedback in natural language

[0118] Specific behavior: A prompt sentence (e.g., "There is a red light at the intersection. Please write an explanatory sentence instructing the user to stop") is input into the generative AI model, and appropriate feedback sentences are generated based on the analysis results. For example, specific behavioral instructions such as "The light is red, so stop and wait" are generated.

[0119] Step 5:

[0120] The server sends the generated feedback to the terminal via 5G communication.

[0121] Input: Feedback in natural language

[0122] Output: Feedback data sent to the device

[0123] Specific operation: The server encodes the generated feedback and uses a communication protocol optimized for Quality of Service (QoS) to transfer data with low latency.

[0124] Step 6:

[0125] The terminal provides the received feedback to the user through an audio output device.

[0126] Input: Feedback data sent to the device

[0127] Output: Vocalized feedback and optional vibration feedback

[0128] Specific operation: The device sends text data to a speech synthesis engine, which converts it into natural-sounding speech. The synthesized instructions are then played back through the user's earpiece or speaker. If necessary, a dedicated vibration device will be activated to provide a physical notification to the user.

[0129] (Application example 1)

[0130] 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."

[0131] At-risk users, such as the visually impaired and elderly, have difficulty independently navigating physical stores safely and smoothly and finding the products they are looking for. Conventional navigation systems often require visual information, making them difficult for visually impaired people to use. In addition, store layouts and product placement change frequently, making it difficult to respond to changes using static maps alone. Therefore, there is a need for a system that provides real-time environmental information to support users.

[0132] 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.

[0133] In this invention, the server includes means for acquiring image data of the store using a camera worn by the user, means for acquiring location information, means for transmitting the image data and the location information to the server via a communication network, means for analyzing the image data and the location information in the server, means for identifying the user's current location and surrounding objects in the store based on the analysis results in the server, and means for generating guidance to the user's destination in real time based on the identified objects, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people and elderly people to move independently and safely through a physical store and find the products they are looking for.

[0134] "Users" are people at risk, such as visually impaired people or elderly people, who use the system.

[0135] A "camera" is a device that is worn at a position close to the user's line of sight and that captures image data of the surroundings.

[0136] "Location information" refers to data on the user's current geographical location obtained using a module such as a GPS.

[0137] "Communications network" refers to a network that serves as infrastructure for transferring data at high speed and with low latency, and includes the fifth-generation mobile communications system (5G).

[0138] A "server" is a computing device that receives and analyzes image data and location information.

[0139] "Analysis" refers to the process of identifying and identifying the surrounding environment using a generative AI model and image analysis algorithms based on the received image data and location information.

[0140] "Feedback" is guidance information that helps the user safely reach their destination based on the results of analysis by the server.

[0141] "Audio" is the output form of feedback provided to the user, allowing the user to understand the situation in real time.

[0142] "In-store navigation" is a support system that helps users navigate safely within a physical store and reach their desired products or areas.

[0143] "Objects" are elements that are involved in the user's movement and reaching their destination, such as shelves, products, and obstacles present in the store.

[0144] This invention is a system that enables at-risk users, such as the visually impaired and elderly, to navigate independently in physical stores and safely find the products they are looking for. The system consists of a wearable camera worn by the user, a GPS module, 5G communications, a server, a generative AI model, Mapbox API, and a voice output device.

[0145] First, the user wears a wearable camera. This camera is attached at a position close to the user's eye level and captures image data of the store in real time. Furthermore, by using a GPS module, the user's current location is acquired with high accuracy.

[0146] The device then transmits the acquired image data and location information to a server via 5G communication. The high speed and low latency characteristics of 5G communication enable the server to always receive the latest data, even when the user is on the move.

[0147] The server analyzes the received image data and location information using a generative AI model and image analysis algorithm. Specifically, the image analysis algorithm identifies shelves, products, obstacles, etc. in the store, and then references map information from Mapbox to complement the analysis results. This allows the user's current location and surrounding objects to be identified.

[0148] Based on the analysis results, the server generates guidance information to help the user safely reach their destination. This guidance information is generated in real time in natural language and includes specific instructions such as, "There is a fruit corner 10 meters ahead on the left."

[0149] The generated feedback is then sent back to the device via 5G communication, and the device then provides the received feedback to the user via an audio output device, such as an earpiece or speaker, allowing the user to understand the situation in real time and take appropriate action.

[0150] As a concrete example, consider the case where a user is in a supermarket. The user is wearing a wearable camera and walking around the store. As they do so, the camera captures images of products on the shelves, and GPS acquires their location information. This data is sent to a server via 5G communication. Based on image analysis and location information, the server generates guidance such as "There is a fruit section 10 meters ahead on the left," sends this to the device, and notifies the user via voice.

[0151] An example of a prompt might be:

[0152] "Design a system that identifies the surrounding environment and generates directions to help the user reach their destination."

[0153] "Create prompts for your AI model that generate feedback to help visually impaired people safely find individual items in a store."

[0154] This will enable visually impaired people and the elderly to enjoy shopping in physical stores with peace of mind.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] A user puts on a wearable camera and starts moving around the store. The camera continuously captures image data of the surroundings, and the GPS module acquires the user's current location. The inputs are images from the camera and location information from the GPS, and these data are collected by the device. The output is a set of image data and location information.

[0158] Step 2:

[0159] The device sends the collected image data and location information to the server via 5G communication. Using a communication protocol, the data is transferred to the server quickly and with low latency. The input here is the image data and location information obtained in step 1, and the output is the data transferred to the server.

[0160] Step 3:

[0161] The server uses a generative AI model and image analysis algorithm to analyze the received image data and location information. Specifically, the image analysis algorithm identifies objects such as shelves, products, and obstacles in the store, and complements the analysis results by integrating GPS location information and Mapbox map information. The input here is image data and location information, and the output is the object identification results and complemented location information.

[0162] Step 4:

[0163] Based on the analysis results, the server generates feedback to help the user safely reach their destination. The generative AI model generates real-time guidance information in natural language. For example, it includes specific guidance such as "There is a fruit corner 10 meters ahead on the left." The input here is the analysis result from Step 3, and the output is the feedback text.

[0164] Step 5:

[0165] The generated feedback is sent to the device via 5G communication. The communication protocol ensures that the feedback reaches the user quickly. The input here is the feedback text, and the output is the feedback text transmitted to the device.

[0166] Step 6:

[0167] The device provides the received feedback to the user through an audio output device, using a text-to-speech (TTS) engine to convert the text into speech and play it through an earpiece or speaker, where the input is the feedback text and the output is a natural language voice prompt.

[0168] Step 7:

[0169] Users receive audio feedback and follow the guidance to navigate the store. By following specific guidance, they can safely reach products and areas. The input here is audio feedback, and the output is the user's movement behavior.

[0170] 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.

[0171] This invention relates to a real-time assistance system that enables users at risk, such as the visually impaired, to act independently and safely. The system is basically composed of a camera worn by the user, a GPS module, and an emotion engine, and by transmitting these data to a server via 5G communication, it provides feedback according to the surrounding environment and the user's emotional state.

[0172] 1. Acquiring images and location information

[0173] The user wears a wearable camera, which captures image data every second. At the same time, the GPS module acquires the user's current location information. This allows visual and location information to be collected continuously while the user is moving.

[0174] 2. Sending images, location information, and emotion data to the server

[0175] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to determine the user's emotional state.

[0176] 3. Analysis of environmental and emotional data

[0177] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[0178] 4. Generating risk and environmental information

[0179] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as, "Don't worry. Cross when the light turns green."

[0180] 5. Submitting Feedback

[0181] The server then sends the generated feedback to the device via 5G, allowing users to receive the necessary information in real time.

[0182] 6. Real-time feedback

[0183] The device converts the received feedback into a voice message and provides it to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[0184] Specific examples

[0185] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[0186] The server uses image analysis to identify traffic lights, and then references map information from Mapbox to determine the current traffic light status. It then generates instructions, such as "The light is red, so please stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[0187] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotion data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[0188] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0189] The processing flow will be explained below.

[0190] Step 1:

[0191] A user walks around wearing a wearable camera. The camera captures images every second, and the GPS module obtains the user's current location.

[0192] Step 2:

[0193] The device converts the captured image data and location information into data packets, using an emotion engine to analyze the user's voice tone, facial expressions, and heart rate.

[0194] Step 3:

[0195] The device then sends the captured image data, location information, and emotion data detected by the emotion engine to a server via 5G communication.

[0196] Step 4:

[0197] The server analyzes the received image data using an image analysis algorithm (e.g., YOLOv5) to identify objects such as traffic lights, vehicles, and pedestrians. It also calls a map API (e.g., Mapbox) to obtain geographic information based on the location information.

[0198] Step 5:

[0199] The server combines image analysis results with geographic information to generate feedback to guide users in safe behavior, with tone and content adjusted based on emotional data.

[0200] Step 6:

[0201] The server converts the generated feedback into data packets and sends them to the device via 5G communication.

[0202] Step 7:

[0203] The device converts the received feedback into an audio message and provides it to the user in real time through a speaker or earpiece, optionally with vibration feedback.

[0204] Specific examples

[0205] For example, consider a situation where a user approaches an intersection. In this case, the device captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[0206] The server uses image analysis to identify traffic lights, and then refers to map information to determine the current traffic light status. It then generates instructions, such as "The light is red, so stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[0207] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotional data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[0208] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0209] Example 2

[0210] 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."

[0211] Currently, it is difficult for users who are at risk, such as those with visual impairments, to act independently and safely. Specifically, it is difficult for them to accurately grasp information about their surroundings and take appropriate action. Furthermore, the user's emotional state often influences their behavior, and there is a need for support systems that take this into consideration.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0213] In this invention, the server includes means for acquiring image data using a camera worn by a user, means for acquiring location information, means for detecting emotional data, means for transmitting the image data, the location information, and the emotional data to the server via a communication network, means for analyzing the image data, the location information, and the emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people to grasp information about their surrounding environment and risks in real time and receive appropriate feedback according to their emotional state.

[0214] "User" refers to an individual who uses the system to receive assistance, such as a visually impaired person.

[0215] "Camera" refers to a wearable device worn by a user to capture image data.

[0216] "Location information" refers to data indicating the user's current location obtained using a GPS module or the like.

[0217] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing biometric data such as the user's voice tone, facial expression, and heart rate.

[0218] "Communications network" refers to the network infrastructure for transmitting and receiving data, and specifically includes fifth generation mobile communications systems (5G).

[0219] "Server" refers to a computer system that analyzes the received data and generates and sends feedback to the user.

[0220] "Feedback" refers to messages containing notifications and instructions for users to act safely based on the analysis results.

[0221] "Analysis" refers to the process of processing received image data, location information, and emotion data to assess the user's current state and environment.

[0222] This invention relates to a real-time support system that enables users at risk, such as the visually impaired, to move independently and safely. The system is basically composed of a camera worn by the user, a GPS module that acquires location information, and an emotion engine that detects emotional data. By transmitting this data to a server via a communication network, the system provides feedback according to the surrounding environment and the user's emotional state.

[0223] Image and location capture

[0224] The user wears a wearable camera, which captures image data every second, while the GPS module acquires the user's current location information, allowing visual and location information to be collected continuously while the user is moving.

[0225] Sending images, location information, and emotion data to the server

[0226] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state.

[0227] Analysis of environmental and emotional data

[0228] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[0229] Generating risk and environmental information

[0230] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as "Don't worry. Cross the street when the light turns green."

[0231] Send Feedback

[0232] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[0233] Real-time feedback

[0234] The device converts the received feedback into voice messages and provides them to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[0235] Specific examples

[0236] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[0237] The server identifies the traffic light through image analysis and determines the current traffic light status by referring to map information. It then generates instructions such as "The light is red, so please stop and wait" in a tone that helps the user regain their composure. The device then conveys this to the user through voice.

[0238] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device will capture images, location information, and emotional data, which the server will analyze. The server will then generate instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notify the user via the device.

[0239] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0240] Prompt Sentence Examples

[0241] Example 1: "Please explain the flow of an assistance system that helps visually impaired people cross intersections safely."

[0242] Example 2: "Please explain with specific examples how a real-time feedback system using an emotion engine can support users."

[0243] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0244] Step 1: Acquire images and location information

[0245] The user wears a camera and a GPS module. The camera captures image data every second, and the GPS module obtains the user's current location.

[0246] Input: Camera shutter speed, GPS satellite signal

[0247] Output: Captured image data, current latitude and longitude information

[0248] How it works: The camera takes a still image every second, and the GPS module simultaneously acquires the latitude and longitude of the current location, which is then stored on the device.

[0249] Step 2: Packetize and transmit the data

[0250] The device packetizes the acquired image data, location information, and emotion data obtained from the emotion engine, and transmits them to a server using 5G communications.

[0251] Input: image data, location information, emotion data

[0252] Output: Packetized data sent to the server

[0253] How it works: The device breaks down each piece of data into small packets to ensure accurate delivery to the server, and the packetized data is then immediately sent to the server via 5G communication.

[0254] Step 3: Analyze environmental and emotional data

[0255] The server analyzes the received image data, location information, and emotion data. It uses image analysis algorithms such as YOLOv5 to identify objects, while simultaneously obtaining geographic information based on the location information and evaluating the emotion data.

[0256] Input: Image data, location information, and emotion data sent to the server

[0257] Output: Analyzed object information, geographic information, emotion evaluation

[0258] Specific operation: The server runs the YOLOv5 algorithm, analyzes image data, and detects objects such as traffic lights, cars, and pedestrians. It also calls a map API to obtain geographic information based on the location information. Emotion data evaluates the user's emotional state based on biometric information.

[0259] Step 4: Generate risk and environmental information

[0260] The server generates feedback based on the analysis results to help users behave safely, and the tone and content of the feedback are adjusted according to the user's emotional state.

[0261] Input: Analyzed object information, geographic information, emotion evaluation

[0262] Output: Feedback message

[0263] Specific operation: The server comprehensively evaluates the analysis results and creates a feedback message such as "The traffic light is red. Please stop and wait." It also determines the tone of the feedback based on the emotional state.

[0264] Step 5: Submit your feedback

[0265] The server re-packetizes the generated feedback and transmits it to the terminal using 5G communication.

[0266] Input: Feedback message

[0267] Output: Feedback message sent to the terminal

[0268] Specific operation: The server divides the generated feedback message into small packets and sends them to the terminal.

[0269] Step 6: Real-time feedback

[0270] The device converts the received feedback into audio messages and provides them to the user through a speaker or earpiece, and optionally uses vibration feedback.

[0271] Input: Feedback message received

[0272] Output: Audio and vibration feedback provided to the user

[0273] Specific operation: The device's speech synthesis engine converts the feedback message into voice and conveys it to the user through the speaker or earphones. In some cases, it may also activate the vibration function to alert the user.

[0274] (Application example 2)

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

[0276] In the work environment within a factory, there is a need for a system that can grasp visual information and the emotional state of workers in real time to support them in working safely and efficiently. Current systems have difficulty providing appropriate feedback in real time according to the risks and emotional state of workers during work, which has led to problems with industrial accidents and reduced work efficiency. In particular, a major issue is the inability to respond quickly to the approach of dangerous objects or emotional stress.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring image data using a camera worn by the user, means for acquiring location information, means for transmitting the image data, the location information, and emotional data to the server via a communication network, means for analyzing the image data, the location information, and emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This makes it possible to grasp visual information and emotional states in the work environment in real time and provide appropriate feedback according to the emotional state.

[0278] "User" refers to an individual who uses the system of the present invention, particularly a worker who performs work in a factory.

[0279] A "camera" is a device worn by a user that captures image data in real time. It is attached to a wearable device.

[0280] "Image data" refers to visual information acquired through cameras, including the environment and risk factors within the factory.

[0281] "Location information" is data that indicates the user's current location. It is acquired using a GPS module.

[0282] "Emotion data" is data that indicates the user's emotional state and is generated by the emotion engine. It is obtained by analyzing biometric data.

[0283] "Communication network" refers to the network infrastructure used to transmit data to the server. Specifically, it uses the fifth-generation mobile communication system (5G).

[0284] The "server" is a computer system that analyzes the transmitted image data, location information, and emotion data and generates feedback.

[0285] "Analysis means" refers to the function of analyzing the data received by the server, including image analysis and emotion analysis.

[0286] "Feedback" refers to instructions or information generated based on the analysis results, and provided to the user in the form of voice messages or other formats.

[0287] "Audio output means" refers to a device that converts feedback into audio format and provides it to the user, such as a speaker in smart glasses.

[0288] A "prompt" is predefined text used by a generative AI model to generate feedback.

[0289] The present invention relates to a real-time support system for factory workers to perform their work safely and efficiently. This system acquires image data, location information, and emotion data in real time and provides appropriate feedback based on the analysis results. Specifically, it includes the following components:

[0290] 1. Acquiring images and location information

[0291] The user wears a wearable device (e.g., smart glasses) and uses a camera to capture image data every second. The GPS module also captures the user's current location information. This allows visual and location information to be collected continuously as the user moves around the factory.

[0292] 2. Acquiring Emotion Data

[0293] The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state, which is also captured simultaneously.

[0294] 3. Sending data to the server

[0295] The device (such as smart glasses) packetizes the acquired image data, location information, and emotion data and transmits them to a server using 5G communication.

[0296] 4. Data Analysis

[0297] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (e.g., dangerous objects, mobile devices, etc.), obtains geographic information based on the location information, and evaluates the user's current emotional state based on the emotion data.

[0298] 5. Generate feedback

[0299] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. When generating feedback using a generative AI model, pre-defined prompt sentences are used.

[0300] 6. Submitting Feedback

[0301] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[0302] 7. Real-time feedback output

[0303] The device converts the received feedback into voice messages and provides them to the user through the smart glasses' speakers or earpiece, and also provides vibration feedback if necessary. The content and tone of the feedback are adjusted according to the user's emotional state, providing more appropriate assistance.

[0304] Specific examples

[0305] For example, consider a situation where a user is working near a forklift in a factory. In this case, the device (smart glasses) captures images and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[0306] The server identifies the forklift through image analysis and generates instructions such as "A forklift is approaching. Please be careful" in a tone that helps the user regain their composure. The terminal then conveys this information to the user via voice.

[0307] Similarly, if the user is fatigued, the device will acquire images, location information, and emotional data, which will be analyzed by the server. The server will then generate instructions such as "Please take a short rest" in a tone that matches the user's emotional state and notify the user via the device.

[0308] Prompt Sentence Examples

[0309] “YOLOv5: Identify and classify objects in an image of a factory setting.

[0310] Model: TENSORFLOW(R) emotion model to evaluate emotional state based on input biophysical data.

[0311] Scenario:

[0312] Object detection result: {'dangerous_item': True}

[0313] Emotional state: 'stressed'

[0314] Generate appropriate feedback for the above scenario considering both object detection and emotional state.

[0315] Output example: 'Dangerous object approaching. Be careful. Take a short break to relax.'

[0316] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0317] Step 1:

[0318] The user wears a wearable device (smart glasses), and the camera captures images every second. At the same time, the GPS module acquires location information. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to generate emotion data. The inputs are camera images, location information, and biometric data, and the outputs are image data, location information, and emotion data.

[0319] Step 2:

[0320] The device packetizes the acquired image data, location information, and emotion data and transmits them to the server using 5G communication. The input is packetized data, and the output is packets transmitted via 5G communication. The specific operation involves packetizing the data and transmitting the data according to the communication protocol.

[0321] Step 3:

[0322] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (YOLOv5) to identify objects in the image (such as dangerous objects or moving equipment) and obtains geographic information based on the location information. It evaluates the user's current emotional state based on the emotion data. The input is the received image data, location information, and emotion data, and the output is the analysis result.

[0323] Step 4:

[0324] Based on the analysis results, the server generates feedback to help the user act safely. When generating feedback using a generative AI model, a predefined prompt sentence is used. The input is the analysis result and the prompt sentence, and the output is a feedback message. Specifically, the server supplies data to the generative AI model and generates appropriate feedback.

[0325] Step 5:

[0326] The server sends the generated feedback to the terminal via 5G communication. The input is the feedback message, and the output is the feedback sent via 5G communication. The specific operation involves packetizing the feedback message and transmitting the data according to the communication protocol.

[0327] Step 6:

[0328] The device converts the received feedback into an audio message and provides it to the user through the smart glasses' speaker or earpiece. It also uses vibration feedback if necessary. The input is the received feedback message, and the output is audio or vibration feedback. Specifically, the device generates audio data and provides feedback through the device.

[0329] 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.

[0330] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0331] 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.

[0332] [Second embodiment]

[0333] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0334] 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.

[0335] 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).

[0336] 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.

[0337] 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.

[0338] 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).

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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."

[0345] The purpose of this invention is to provide a real-time support system for visually impaired people and other users at risk to act independently and safely. This system consists of the following main components:

[0346] 1. Acquiring images and location information

[0347] The system uses a wearable camera and GPS module worn by the user to acquire image data of the surroundings and the user's location. The wearable camera is attached at a position close to the user's eye level and continuously captures real-time visual information while walking. The GPS module measures the user's current location with high accuracy.

[0348] 2. Sending images and location information to the server

[0349] The device transmits the acquired image data and location information to the server in real time. 5G communication enables high-speed, low-latency data transfer, allowing the server to constantly provide up-to-date environmental information, even while the user is on the move.

[0350] 3. Analysis of Environmental Data

[0351] The server analyzes the surrounding environment using generative AI models and image analysis algorithms based on the received image data and location information. For example, the image analysis algorithm identifies objects such as intersections, traffic lights, pedestrians, and vehicles, and then pinpoints their locations based on the location information. Furthermore, the server complements the analysis results with high-precision map information from Mapbox.

[0352] 4. Generating risk and environmental information

[0353] Based on the analysis results, the server generates feedback in natural language to help users act safely. The feedback includes real-time risk information such as the status of traffic lights at intersections, approaching vehicles, and pedestrian movements. The focus is on providing feedback that allows users to respond quickly and appropriately.

[0354] 5. Submitting Feedback

[0355] The server then sends the generated feedback to the device via 5G, allowing users to receive the information they need with low latency.

[0356] 6. Real-time feedback

[0357] The device then provides the received feedback to the user via an audio output device (speaker or earpiece). Real-time audio guidance allows even visually impaired users to instantly understand the current situation and take appropriate action. The device can also provide additional notification features, such as vibration feedback, if needed.

[0358] Specific examples

[0359] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information using GPS. This information is sent to the server via 5G communication. The server identifies the traffic light using image analysis and determines the current traffic light status by referring to map information from Mapbox. It then generates instructions such as "The light is red, so stop and wait" and sends them to the device. The device then communicates this to the user via voice.

[0360] Similarly, if a user is walking down a sidewalk and is about to come across a staircase, the device will acquire image and location information, and the server will detect the presence of the staircase. The server will then generate a warning such as "There are stairs 10 meters ahead. Please proceed with caution" and notify the user via the device.

[0361] This system enables visually impaired people to understand their surroundings in real time and act safely. Furthermore, by improving the accuracy of analysis and the quality of feedback on the server, we can expect to further improve the user experience.

[0362] The processing flow will be explained below.

[0363] Step 1:

[0364] The device uses a wearable camera to capture images of the surroundings every second, and also uses a GPS module to obtain its current location (latitude and longitude).

[0365] Step 2:

[0366] The device then converts the captured image data and location information into a data packet and sends it to the server using 5G communications.

[0367] Step 3:

[0368] The server inputs the received image data and location information into an AI model for analysis, which uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.).

[0369] Step 4:

[0370] The server then calls a map API (e.g., Mapbox) to obtain detailed geographic information based on the location information, such as intersections, sidewalks, and buildings at the user's current location.

[0371] Step 5:

[0372] The server combines the image analysis results with map information and generates the feedback the user needs to act safely, such as "The traffic light is red. Please stop before the crosswalk."

[0373] Step 6:

[0374] The server converts the generated feedback into data packets and sends them back to the device using 5G communications.

[0375] Step 7:

[0376] The device converts the received feedback into audio messages and provides them to the user in real time through a speaker or earpiece, along with vibration feedback if necessary.

[0377] Example 1

[0378] 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."

[0379] For visually impaired and other at-risk users to act independently and safely, they need to acquire information about their surroundings in real time and receive appropriate feedback. However, conventional systems often experience delays in acquiring and analyzing this information and generating and providing feedback, which can prevent users from responding appropriately. This can result in safety issues. Furthermore, previous technologies have not yet realized the provision of real-time feedback using generative AI models.

[0380] 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.

[0381] In this invention, the server includes means for analyzing image data and location information, means for generating feedback to the user using a generative AI model, and means for transmitting the feedback to the user via a communication network and outputting it as audio, thereby enabling visually impaired and other at-risk users to understand their surroundings in real time and take appropriate action.

[0382] "User" refers to the individual end user of the system, particularly those who are visually impaired or otherwise at risk.

[0383] A "camera" refers to a device worn by a user that captures image data of the surroundings in real time.

[0384] "Image data" refers to digital data of visual information captured using a camera.

[0385] "Location Information" means geographic information obtained using a GPS module or other location tracking technology to identify a User's current location.

[0386] "Communications Network" means the network infrastructure used to transmit and receive data in real time, and specifically includes fifth generation mobile communications systems (5G).

[0387] "Server" refers to a sophisticated computer system for analyzing acquired data and generating and transmitting feedback.

[0388] "Image analysis" refers to algorithms and techniques used to identify specific objects or situations based on acquired image data.

[0389] A "generative AI model" refers to an artificial intelligence model that performs natural language processing and advanced data analysis, and is specifically used to generate feedback for users.

[0390] "Feedback" refers to instructions or information that the server generates based on the analysis results and provides to the user.

[0391] "Audio output" refers to an audio notification method for conveying feedback to the user, including devices such as speakers and earpieces.

[0392] This invention is a support system for visually impaired and other at-risk users to move independently and safely. The system uses a wearable camera, a GPS module, a generative AI model, image analysis algorithms, and advanced communication network technology.

[0393] The basic hardware configuration involves the user wearing a wearable camera and a GPS module. The wearable camera is attached close to the user's viewpoint and captures visual information of the surroundings in real time. The GPS module is responsible for measuring the user's current location with high accuracy.

[0394] Data Acquisition

[0395] As the user moves, the wearable camera continuously captures image data, and the GPS module acquires location information, which is then immediately sent to the device and transmitted to a server via 5G communication.

[0396] Data analysis

[0397] The server analyzes the environment using generative AI models and image analysis algorithms based on the received image data and location information. Specifically, it uses object detection algorithms (e.g., YOLO - You Only Look Once) to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then complements the analysis results with high-precision map information from Mapbox.

[0398] Generate feedback

[0399] The server generates feedback for the user based on the analysis results. A generative AI model (e.g., GPT-4) creates feedback content in natural language based on the analysis results. The following is an example of a prompt:

[0400] "There is a red light at the intersection. Please create a description to instruct the user to stop."

[0401] By entering such a prompt, appropriate feedback is generated, such as "The light is red, so stop and wait."

[0402] Sending and outputting feedback

[0403] The generated feedback is transmitted to the device via 5G communication, and the device provides the received feedback to the user via an audio output device (speaker or earpiece), and can also have additional notification features such as vibration feedback.

[0404] Specific examples

[0405] For example, when a user approaches an intersection, the device captures an image and obtains GPS location information. This information is sent in real time to a server, which then uses image analysis to determine the traffic light status and uses a generative AI model to generate an instruction such as "The light is red, so stop and wait." This instruction is then sent to the device and communicated to the user via a voice output device. Similarly, if a user is walking down a sidewalk and comes across stairs, the device will use image analysis and location information to detect their presence and generate a warning saying, "There are stairs 10 meters ahead. Proceed with caution."

[0406] The above is a specific embodiment of the present invention. This system enables the user to act independently and safely.

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

[0408] Step 1:

[0409] The user puts on the wearable camera and GPS module and starts the system.

[0410] Input: User's visual information (image data) and current location information

[0411] Output: Real-time image data and location information

[0412] How it works: The wearable camera captures images at a rate of 30 frames per second, and the GPS module updates the user's location every second.

[0413] Step 2:

[0414] The device transmits the acquired image data and location information to the server in real time.

[0415] Input: Real-time image data and location information

[0416] Output: Data packet containing compressed image data and location information

[0417] Specific operation: The terminal compresses and packets the acquired data via the 5G communication module and sends it to the server. At this time, data priority is set and packet retransmission control is performed to minimize data transmission delays.

[0418] Step 3:

[0419] The server applies a generated AI model and image analysis algorithm based on the received image data and location information to analyze the surrounding environment.

[0420] Input: Compressed image data and location information

[0421] Output: Analyzed environmental information (object position, type, etc.)

[0422] How it works: The server decodes the received data and uses object detection algorithms to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then uses Mapbox's API to obtain high-precision map information and complete the location of objects and situations.

[0423] Step 4:

[0424] The server uses a generative AI model based on the analysis results to generate feedback for the user.

[0425] Input: Parsed environment information

[0426] Output: Feedback in natural language

[0427] Specific behavior: A prompt sentence (e.g., "There is a red light at the intersection. Please write an explanatory sentence instructing the user to stop") is input into the generative AI model, and appropriate feedback sentences are generated based on the analysis results. For example, specific behavioral instructions such as "The light is red, so stop and wait" are generated.

[0428] Step 5:

[0429] The server sends the generated feedback to the terminal via 5G communication.

[0430] Input: Feedback in natural language

[0431] Output: Feedback data sent to the device

[0432] Specific operation: The server encodes the generated feedback and uses a communication protocol optimized for Quality of Service (QoS) to transfer data with low latency.

[0433] Step 6:

[0434] The terminal provides the received feedback to the user through an audio output device.

[0435] Input: Feedback data sent to the device

[0436] Output: Vocalized feedback and optional vibration feedback

[0437] Specific operation: The device sends text data to a speech synthesis engine, which converts it into natural-sounding speech. The synthesized instructions are then played back through the user's earpiece or speaker. If necessary, a dedicated vibration device will be activated to provide a physical notification to the user.

[0438] (Application example 1)

[0439] 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."

[0440] At-risk users, such as the visually impaired and elderly, have difficulty independently navigating physical stores safely and smoothly and finding the products they are looking for. Conventional navigation systems often require visual information, making them difficult for visually impaired people to use. In addition, store layouts and product placement change frequently, making it difficult to respond to changes using static maps alone. Therefore, there is a need for a system that provides real-time environmental information to support users.

[0441] 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.

[0442] In this invention, the server includes means for acquiring image data of the store using a camera worn by the user, means for acquiring location information, means for transmitting the image data and the location information to the server via a communication network, means for analyzing the image data and the location information in the server, means for identifying the user's current location and surrounding objects in the store based on the analysis results in the server, and means for generating guidance to the user's destination in real time based on the identified objects, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people and elderly people to move independently and safely through a physical store and find the products they are looking for.

[0443] "Users" are people at risk, such as visually impaired people or elderly people, who use the system.

[0444] A "camera" is a device that is worn at a position close to the user's line of sight and that captures image data of the surroundings.

[0445] "Location information" refers to data on the user's current geographical location obtained using a module such as a GPS.

[0446] "Communications network" refers to a network that serves as infrastructure for transferring data at high speed and with low latency, and includes the fifth-generation mobile communications system (5G).

[0447] A "server" is a computing device that receives and analyzes image data and location information.

[0448] "Analysis" refers to the process of identifying and identifying the surrounding environment using a generative AI model and image analysis algorithms based on the received image data and location information.

[0449] "Feedback" is guidance information that helps the user safely reach their destination based on the results of analysis by the server.

[0450] "Audio" is the output form of feedback provided to the user, allowing the user to understand the situation in real time.

[0451] "In-store navigation" is a support system that helps users navigate safely within a physical store and reach their desired products or areas.

[0452] "Objects" are elements that are involved in the user's movement and reaching their destination, such as shelves, products, and obstacles present in the store.

[0453] This invention is a system that enables at-risk users, such as the visually impaired and elderly, to navigate independently in physical stores and safely find the products they are looking for. The system consists of a wearable camera worn by the user, a GPS module, 5G communications, a server, a generative AI model, Mapbox API, and a voice output device.

[0454] First, the user wears a wearable camera. This camera is attached at a position close to the user's eye level and captures image data of the store in real time. Furthermore, by using a GPS module, the user's current location is acquired with high accuracy.

[0455] The device then transmits the acquired image data and location information to a server via 5G communication. The high speed and low latency characteristics of 5G communication enable the server to always receive the latest data, even when the user is on the move.

[0456] The server analyzes the received image data and location information using a generative AI model and image analysis algorithm. Specifically, the image analysis algorithm identifies shelves, products, obstacles, etc. in the store, and then references map information from Mapbox to complement the analysis results. This allows the user's current location and surrounding objects to be identified.

[0457] Based on the analysis results, the server generates guidance information to help the user safely reach their destination. This guidance information is generated in real time in natural language and includes specific instructions such as, "There is a fruit corner 10 meters ahead on the left."

[0458] The generated feedback is then sent back to the device via 5G communication, and the device then provides the received feedback to the user via an audio output device, such as an earpiece or speaker, allowing the user to understand the situation in real time and take appropriate action.

[0459] As a concrete example, consider the case where a user is in a supermarket. The user is wearing a wearable camera and walking around the store. As they do so, the camera captures images of products on the shelves, and GPS acquires their location information. This data is sent to a server via 5G communication. Based on image analysis and location information, the server generates guidance such as "There is a fruit section 10 meters ahead on the left," sends this to the device, and notifies the user via voice.

[0460] An example of a prompt might be:

[0461] "Design a system that identifies the surrounding environment and generates directions to help the user reach their destination."

[0462] "Create prompts for your AI model that generate feedback to help visually impaired people safely find individual items in a store."

[0463] This will enable visually impaired people and the elderly to enjoy shopping in physical stores with peace of mind.

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

[0465] Step 1:

[0466] A user puts on a wearable camera and starts moving around the store. The camera continuously captures image data of the surroundings, and the GPS module acquires the user's current location. The inputs are images from the camera and location information from the GPS, and these data are collected by the device. The output is a set of image data and location information.

[0467] Step 2:

[0468] The device sends the collected image data and location information to the server via 5G communication. Using a communication protocol, the data is transferred to the server quickly and with low latency. The input here is the image data and location information obtained in step 1, and the output is the data transferred to the server.

[0469] Step 3:

[0470] The server uses a generative AI model and image analysis algorithm to analyze the received image data and location information. Specifically, the image analysis algorithm identifies objects such as shelves, products, and obstacles in the store, and complements the analysis results by integrating GPS location information and Mapbox map information. The input here is image data and location information, and the output is the object identification results and complemented location information.

[0471] Step 4:

[0472] Based on the analysis results, the server generates feedback to help the user safely reach their destination. The generative AI model generates real-time guidance information in natural language. For example, it includes specific guidance such as "There is a fruit corner 10 meters ahead on the left." The input here is the analysis result from Step 3, and the output is the feedback text.

[0473] Step 5:

[0474] The generated feedback is sent to the device via 5G communication. The communication protocol ensures that the feedback reaches the user quickly. The input here is the feedback text, and the output is the feedback text transmitted to the device.

[0475] Step 6:

[0476] The device provides the received feedback to the user through an audio output device, using a text-to-speech (TTS) engine to convert the text into speech and play it through an earpiece or speaker, where the input is the feedback text and the output is a natural language voice prompt.

[0477] Step 7:

[0478] Users receive audio feedback and follow the guidance to navigate the store. By following specific guidance, they can safely reach products and areas. The input here is audio feedback, and the output is the user's movement behavior.

[0479] 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.

[0480] This invention relates to a real-time assistance system that enables users at risk, such as the visually impaired, to act independently and safely. The system is basically composed of a camera worn by the user, a GPS module, and an emotion engine, and by transmitting these data to a server via 5G communication, it provides feedback according to the surrounding environment and the user's emotional state.

[0481] 1. Acquiring images and location information

[0482] The user wears a wearable camera, which captures image data every second. At the same time, the GPS module acquires the user's current location information. This allows visual and location information to be collected continuously while the user is moving.

[0483] 2. Sending images, location information, and emotion data to the server

[0484] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to determine the user's emotional state.

[0485] 3. Analysis of environmental and emotional data

[0486] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[0487] 4. Generating risk and environmental information

[0488] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as, "Don't worry. Cross when the light turns green."

[0489] 5. Submitting Feedback

[0490] The server then sends the generated feedback to the device via 5G, allowing users to receive the necessary information in real time.

[0491] 6. Real-time feedback

[0492] The device converts the received feedback into a voice message and provides it to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[0493] Specific examples

[0494] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[0495] The server uses image analysis to identify traffic lights, and then references map information from Mapbox to determine the current traffic light status. It then generates instructions, such as "The light is red, so please stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[0496] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotion data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[0497] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0498] The processing flow will be explained below.

[0499] Step 1:

[0500] A user walks around wearing a wearable camera. The camera captures images every second, and the GPS module obtains the user's current location.

[0501] Step 2:

[0502] The device converts the captured image data and location information into data packets, using an emotion engine to analyze the user's voice tone, facial expressions, and heart rate.

[0503] Step 3:

[0504] The device then sends the captured image data, location information, and emotion data detected by the emotion engine to a server via 5G communication.

[0505] Step 4:

[0506] The server analyzes the received image data using an image analysis algorithm (e.g., YOLOv5) to identify objects such as traffic lights, vehicles, and pedestrians. It also calls a map API (e.g., Mapbox) to obtain geographic information based on the location information.

[0507] Step 5:

[0508] The server combines image analysis results with geographic information to generate feedback to guide users in safe behavior, with tone and content adjusted based on emotional data.

[0509] Step 6:

[0510] The server converts the generated feedback into data packets and sends them to the device via 5G communication.

[0511] Step 7:

[0512] The device converts the received feedback into an audio message and provides it to the user in real time through a speaker or earpiece, optionally with vibration feedback.

[0513] Specific examples

[0514] For example, consider a situation where a user approaches an intersection. In this case, the device captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[0515] The server uses image analysis to identify traffic lights, and then refers to map information to determine the current traffic light status. It then generates instructions, such as "The light is red, so stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[0516] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotional data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[0517] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0518] Example 2

[0519] 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."

[0520] Currently, it is difficult for users who are at risk, such as those with visual impairments, to act independently and safely. Specifically, it is difficult for them to accurately grasp information about their surroundings and take appropriate action. Furthermore, the user's emotional state often influences their behavior, and there is a need for support systems that take this into consideration.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0522] In this invention, the server includes means for acquiring image data using a camera worn by a user, means for acquiring location information, means for detecting emotional data, means for transmitting the image data, the location information, and the emotional data to the server via a communication network, means for analyzing the image data, the location information, and the emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people to grasp information about their surrounding environment and risks in real time and receive appropriate feedback according to their emotional state.

[0523] "User" refers to an individual who uses the system to receive assistance, such as a visually impaired person.

[0524] "Camera" refers to a wearable device worn by a user to capture image data.

[0525] "Location information" refers to data indicating the user's current location obtained using a GPS module or the like.

[0526] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing biometric data such as the user's voice tone, facial expression, and heart rate.

[0527] "Communications network" refers to the network infrastructure for transmitting and receiving data, and specifically includes fifth generation mobile communications systems (5G).

[0528] "Server" refers to a computer system that analyzes the received data and generates and sends feedback to the user.

[0529] "Feedback" refers to messages containing notifications and instructions for users to act safely based on the analysis results.

[0530] "Analysis" refers to the process of processing received image data, location information, and emotion data to assess the user's current state and environment.

[0531] This invention relates to a real-time support system that enables users at risk, such as the visually impaired, to move independently and safely. The system is basically composed of a camera worn by the user, a GPS module that acquires location information, and an emotion engine that detects emotional data. By transmitting this data to a server via a communication network, the system provides feedback according to the surrounding environment and the user's emotional state.

[0532] Image and location capture

[0533] The user wears a wearable camera, which captures image data every second, while the GPS module acquires the user's current location information, allowing visual and location information to be collected continuously while the user is moving.

[0534] Sending images, location information, and emotion data to the server

[0535] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state.

[0536] Analysis of environmental and emotional data

[0537] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[0538] Generating risk and environmental information

[0539] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as "Don't worry. Cross the street when the light turns green."

[0540] Send Feedback

[0541] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[0542] Real-time feedback

[0543] The device converts the received feedback into voice messages and provides them to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[0544] Specific examples

[0545] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[0546] The server identifies the traffic light through image analysis and determines the current traffic light status by referring to map information. It then generates instructions such as "The light is red, so please stop and wait" in a tone that helps the user regain their composure. The device then conveys this to the user through voice.

[0547] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device will capture images, location information, and emotional data, which the server will analyze. The server will then generate instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notify the user via the device.

[0548] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0549] Prompt Sentence Examples

[0550] Example 1: "Please explain the flow of an assistance system that helps visually impaired people cross intersections safely."

[0551] Example 2: "Please explain with specific examples how a real-time feedback system using an emotion engine can support users."

[0552] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0553] Step 1: Acquire images and location information

[0554] The user wears a camera and a GPS module. The camera captures image data every second, and the GPS module obtains the user's current location.

[0555] Input: Camera shutter speed, GPS satellite signal

[0556] Output: Captured image data, current latitude and longitude information

[0557] How it works: The camera takes a still image every second, and the GPS module simultaneously acquires the latitude and longitude of the current location, which is then stored on the device.

[0558] Step 2: Packetize and transmit the data

[0559] The device packetizes the acquired image data, location information, and emotion data obtained from the emotion engine, and transmits them to a server using 5G communications.

[0560] Input: image data, location information, emotion data

[0561] Output: Packetized data sent to the server

[0562] How it works: The device breaks down each piece of data into small packets to ensure accurate delivery to the server, and the packetized data is then immediately sent to the server via 5G communication.

[0563] Step 3: Analyze environmental and emotional data

[0564] The server analyzes the received image data, location information, and emotion data. It uses image analysis algorithms such as YOLOv5 to identify objects, while simultaneously obtaining geographic information based on the location information and evaluating the emotion data.

[0565] Input: Image data, location information, and emotion data sent to the server

[0566] Output: Analyzed object information, geographic information, emotion evaluation

[0567] Specific operation: The server runs the YOLOv5 algorithm, analyzes image data, and detects objects such as traffic lights, cars, and pedestrians. It also calls a map API to obtain geographic information based on the location information. Emotion data evaluates the user's emotional state based on biometric information.

[0568] Step 4: Generate risk and environmental information

[0569] The server generates feedback based on the analysis results to help users behave safely, and the tone and content of the feedback are adjusted according to the user's emotional state.

[0570] Input: Analyzed object information, geographic information, emotion evaluation

[0571] Output: Feedback message

[0572] Specific operation: The server comprehensively evaluates the analysis results and creates a feedback message such as "The traffic light is red. Please stop and wait." It also determines the tone of the feedback based on the emotional state.

[0573] Step 5: Submit your feedback

[0574] The server re-packetizes the generated feedback and transmits it to the terminal using 5G communication.

[0575] Input: Feedback message

[0576] Output: Feedback message sent to the terminal

[0577] Specific operation: The server divides the generated feedback message into small packets and sends them to the terminal.

[0578] Step 6: Real-time feedback

[0579] The device converts the received feedback into audio messages and provides them to the user through a speaker or earpiece, and optionally uses vibration feedback.

[0580] Input: Feedback message received

[0581] Output: Audio and vibration feedback provided to the user

[0582] Specific operation: The device's speech synthesis engine converts the feedback message into voice and conveys it to the user through the speaker or earphones. In some cases, it may also activate the vibration function to alert the user.

[0583] (Application example 2)

[0584] 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."

[0585] In the work environment within a factory, there is a need for a system that can grasp visual information and the emotional state of workers in real time to support them in working safely and efficiently. Current systems have difficulty providing appropriate feedback in real time according to the risks and emotional state of workers during work, which has led to problems with industrial accidents and reduced work efficiency. In particular, a major issue is the inability to respond quickly to the approach of dangerous objects or emotional stress.

[0586] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring image data using a camera worn by the user, means for acquiring location information, means for transmitting the image data, the location information, and emotional data to the server via a communication network, means for analyzing the image data, the location information, and emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This makes it possible to grasp visual information and emotional states in the work environment in real time and provide appropriate feedback according to the emotional state.

[0587] "User" refers to an individual who uses the system of the present invention, particularly a worker who performs work in a factory.

[0588] A "camera" is a device worn by a user that captures image data in real time. It is attached to a wearable device.

[0589] "Image data" refers to visual information acquired through cameras, including the environment and risk factors within the factory.

[0590] "Location information" is data that indicates the user's current location. It is acquired using a GPS module.

[0591] "Emotion data" is data that indicates the user's emotional state and is generated by the emotion engine. It is obtained by analyzing biometric data.

[0592] "Communication network" refers to the network infrastructure used to transmit data to the server. Specifically, it uses the fifth-generation mobile communication system (5G).

[0593] The "server" is a computer system that analyzes the transmitted image data, location information, and emotion data and generates feedback.

[0594] "Analysis means" refers to the function of analyzing the data received by the server, including image analysis and emotion analysis.

[0595] "Feedback" refers to instructions or information generated based on the analysis results, and provided to the user in the form of voice messages or other formats.

[0596] "Audio output means" refers to a device that converts feedback into audio format and provides it to the user, such as a speaker in smart glasses.

[0597] A "prompt" is predefined text used by a generative AI model to generate feedback.

[0598] The present invention relates to a real-time support system for factory workers to perform their work safely and efficiently. This system acquires image data, location information, and emotion data in real time and provides appropriate feedback based on the analysis results. Specifically, it includes the following components:

[0599] 1. Acquiring images and location information

[0600] The user wears a wearable device (e.g., smart glasses) and uses a camera to capture image data every second. The GPS module also captures the user's current location information. This allows visual and location information to be collected continuously as the user moves around the factory.

[0601] 2. Acquiring Emotion Data

[0602] The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state, which is also captured simultaneously.

[0603] 3. Sending data to the server

[0604] The device (such as smart glasses) packetizes the acquired image data, location information, and emotion data and transmits them to a server using 5G communication.

[0605] 4. Data Analysis

[0606] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (e.g., dangerous objects, mobile devices, etc.), obtains geographic information based on the location information, and evaluates the user's current emotional state based on the emotion data.

[0607] 5. Generate feedback

[0608] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. When generating feedback using a generative AI model, pre-defined prompt sentences are used.

[0609] 6. Submitting Feedback

[0610] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[0611] 7. Real-time feedback output

[0612] The device converts the received feedback into voice messages and provides them to the user through the smart glasses' speakers or earpiece, and also provides vibration feedback if necessary. The content and tone of the feedback are adjusted according to the user's emotional state, providing more appropriate assistance.

[0613] Specific examples

[0614] For example, consider a situation where a user is working near a forklift in a factory. In this case, the device (smart glasses) captures images and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[0615] The server identifies the forklift through image analysis and generates instructions such as "A forklift is approaching. Please be careful" in a tone that helps the user regain their composure. The terminal then conveys this information to the user via voice.

[0616] Similarly, if the user is fatigued, the device will acquire images, location information, and emotional data, which will be analyzed by the server. The server will then generate instructions such as "Please take a short rest" in a tone that matches the user's emotional state and notify the user via the device.

[0617] Prompt Sentence Examples

[0618] “YOLOv5: Identify and classify objects in an image of a factory setting.

[0619] Model: TensorFlow emotion model to evaluate emotional state based on input biophysical data.

[0620] Scenario:

[0621] Object detection result: {'dangerous_item': True}

[0622] Emotional state: 'stressed'

[0623] Generate appropriate feedback for the above scenario considering both object detection and emotional state.

[0624] Output example: 'Dangerous object approaching. Be careful. Take a short break to relax.'

[0625] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0626] Step 1:

[0627] The user wears a wearable device (smart glasses), and the camera captures images every second. At the same time, the GPS module acquires location information. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to generate emotion data. The inputs are camera images, location information, and biometric data, and the outputs are image data, location information, and emotion data.

[0628] Step 2:

[0629] The device packetizes the acquired image data, location information, and emotion data and transmits them to the server using 5G communication. The input is packetized data, and the output is packets transmitted via 5G communication. The specific operation involves packetizing the data and transmitting the data according to the communication protocol.

[0630] Step 3:

[0631] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (YOLOv5) to identify objects in the image (such as dangerous objects or moving equipment) and obtains geographic information based on the location information. It evaluates the user's current emotional state based on the emotion data. The input is the received image data, location information, and emotion data, and the output is the analysis result.

[0632] Step 4:

[0633] Based on the analysis results, the server generates feedback to help the user act safely. When generating feedback using a generative AI model, a predefined prompt sentence is used. The input is the analysis result and the prompt sentence, and the output is a feedback message. Specifically, the server supplies data to the generative AI model and generates appropriate feedback.

[0634] Step 5:

[0635] The server sends the generated feedback to the terminal via 5G communication. The input is the feedback message, and the output is the feedback sent via 5G communication. The specific operation involves packetizing the feedback message and transmitting the data according to the communication protocol.

[0636] Step 6:

[0637] The device converts the received feedback into an audio message and provides it to the user through the smart glasses' speaker or earpiece. It also uses vibration feedback if necessary. The input is the received feedback message, and the output is audio or vibration feedback. Specifically, the device generates audio data and provides feedback through the device.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] [Third embodiment]

[0642] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0643] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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."

[0654] The purpose of this invention is to provide a real-time support system for visually impaired people and other users at risk to act independently and safely. This system consists of the following main components:

[0655] 1. Acquiring images and location information

[0656] The system uses a wearable camera and GPS module worn by the user to acquire image data of the surroundings and the user's location. The wearable camera is attached at a position close to the user's eye level and continuously captures real-time visual information while walking. The GPS module measures the user's current location with high accuracy.

[0657] 2. Sending images and location information to the server

[0658] The device transmits the acquired image data and location information to the server in real time. 5G communication enables high-speed, low-latency data transfer, allowing the server to constantly provide up-to-date environmental information, even while the user is on the move.

[0659] 3. Analysis of Environmental Data

[0660] The server analyzes the surrounding environment using generative AI models and image analysis algorithms based on the received image data and location information. For example, the image analysis algorithm identifies objects such as intersections, traffic lights, pedestrians, and vehicles, and then pinpoints their locations based on the location information. Furthermore, the server complements the analysis results with high-precision map information from Mapbox.

[0661] 4. Generating risk and environmental information

[0662] Based on the analysis results, the server generates feedback in natural language to help users act safely. The feedback includes real-time risk information such as the status of traffic lights at intersections, approaching vehicles, and pedestrian movements. The focus is on providing feedback that allows users to respond quickly and appropriately.

[0663] 5. Submitting Feedback

[0664] The server then sends the generated feedback to the device via 5G, allowing users to receive the information they need with low latency.

[0665] 6. Real-time feedback

[0666] The device then provides the received feedback to the user via an audio output device (speaker or earpiece). Real-time audio guidance allows even visually impaired users to instantly understand the current situation and take appropriate action. The device can also provide additional notification features, such as vibration feedback, if needed.

[0667] Specific examples

[0668] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information using GPS. This information is sent to the server via 5G communication. The server identifies the traffic light using image analysis and determines the current traffic light status by referring to map information from Mapbox. It then generates instructions such as "The light is red, so stop and wait" and sends them to the device. The device then communicates this to the user via voice.

[0669] Similarly, if a user is walking down a sidewalk and is about to come across a staircase, the device will acquire image and location information, and the server will detect the presence of the staircase. The server will then generate a warning such as "There are stairs 10 meters ahead. Please proceed with caution" and notify the user via the device.

[0670] This system enables visually impaired people to understand their surroundings in real time and act safely. Furthermore, by improving the accuracy of analysis and the quality of feedback on the server, we can expect to further improve the user experience.

[0671] The processing flow will be explained below.

[0672] Step 1:

[0673] The device uses a wearable camera to capture images of the surroundings every second, and also uses a GPS module to obtain its current location (latitude and longitude).

[0674] Step 2:

[0675] The device then converts the captured image data and location information into a data packet and sends it to the server using 5G communications.

[0676] Step 3:

[0677] The server inputs the received image data and location information into an AI model for analysis, which uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.).

[0678] Step 4:

[0679] The server then calls a map API (e.g., Mapbox) to obtain detailed geographic information based on the location information, such as intersections, sidewalks, and buildings at the user's current location.

[0680] Step 5:

[0681] The server combines the image analysis results with map information and generates the feedback the user needs to act safely, such as "The traffic light is red. Please stop before the crosswalk."

[0682] Step 6:

[0683] The server converts the generated feedback into data packets and sends them back to the device using 5G communications.

[0684] Step 7:

[0685] The device converts the received feedback into audio messages and provides them to the user in real time through a speaker or earpiece, along with vibration feedback if necessary.

[0686] Example 1

[0687] 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."

[0688] For visually impaired and other at-risk users to act independently and safely, they need to acquire information about their surroundings in real time and receive appropriate feedback. However, conventional systems often experience delays in acquiring and analyzing this information and generating and providing feedback, which can prevent users from responding appropriately. This can result in safety issues. Furthermore, previous technologies have not yet realized the provision of real-time feedback using generative AI models.

[0689] 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.

[0690] In this invention, the server includes means for analyzing image data and location information, means for generating feedback to the user using a generative AI model, and means for transmitting the feedback to the user via a communication network and outputting it as audio, thereby enabling visually impaired and other at-risk users to understand their surroundings in real time and take appropriate action.

[0691] "User" refers to the individual end user of the system, particularly those who are visually impaired or otherwise at risk.

[0692] A "camera" refers to a device worn by a user that captures image data of the surroundings in real time.

[0693] "Image data" refers to digital data of visual information captured using a camera.

[0694] "Location Information" means geographic information obtained using a GPS module or other location tracking technology to identify a User's current location.

[0695] "Communications Network" means the network infrastructure used to transmit and receive data in real time, and specifically includes fifth generation mobile communications systems (5G).

[0696] "Server" refers to a sophisticated computer system for analyzing acquired data and generating and transmitting feedback.

[0697] "Image analysis" refers to algorithms and techniques used to identify specific objects or situations based on acquired image data.

[0698] A "generative AI model" refers to an artificial intelligence model that performs natural language processing and advanced data analysis, and is specifically used to generate feedback for users.

[0699] "Feedback" refers to instructions or information that the server generates based on the analysis results and provides to the user.

[0700] "Audio output" refers to an audio notification method for conveying feedback to the user, including devices such as speakers and earpieces.

[0701] This invention is a support system for visually impaired and other at-risk users to move independently and safely. The system uses a wearable camera, a GPS module, a generative AI model, image analysis algorithms, and advanced communication network technology.

[0702] The basic hardware configuration involves the user wearing a wearable camera and a GPS module. The wearable camera is attached close to the user's viewpoint and captures visual information of the surroundings in real time. The GPS module is responsible for measuring the user's current location with high accuracy.

[0703] Data Acquisition

[0704] As the user moves, the wearable camera continuously captures image data, and the GPS module acquires location information, which is then immediately sent to the device and transmitted to a server via 5G communication.

[0705] Data analysis

[0706] The server analyzes the environment using generative AI models and image analysis algorithms based on the received image data and location information. Specifically, it uses object detection algorithms (e.g., YOLO - You Only Look Once) to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then complements the analysis results with high-precision map information from Mapbox.

[0707] Generate feedback

[0708] The server generates feedback for the user based on the analysis results. A generative AI model (e.g., GPT-4) creates feedback content in natural language based on the analysis results. The following is an example of a prompt:

[0709] "There is a red light at the intersection. Please create a description to instruct the user to stop."

[0710] By entering such a prompt, appropriate feedback is generated, such as "The light is red, so stop and wait."

[0711] Sending and outputting feedback

[0712] The generated feedback is transmitted to the device via 5G communication, and the device provides the received feedback to the user via an audio output device (speaker or earpiece), and can also have additional notification features such as vibration feedback.

[0713] Specific examples

[0714] For example, when a user approaches an intersection, the device captures an image and obtains GPS location information. This information is sent in real time to a server, which then uses image analysis to determine the traffic light status and uses a generative AI model to generate an instruction such as "The light is red, so stop and wait." This instruction is then sent to the device and communicated to the user via a voice output device. Similarly, if a user is walking down a sidewalk and comes across stairs, the device will use image analysis and location information to detect their presence and generate a warning saying, "There are stairs 10 meters ahead. Proceed with caution."

[0715] The above is a specific embodiment of the present invention. This system enables the user to act independently and safely.

[0716] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0717] Step 1:

[0718] The user puts on the wearable camera and GPS module and starts the system.

[0719] Input: User's visual information (image data) and current location information

[0720] Output: Real-time image data and location information

[0721] How it works: The wearable camera captures images at a rate of 30 frames per second, and the GPS module updates the user's location every second.

[0722] Step 2:

[0723] The device transmits the acquired image data and location information to the server in real time.

[0724] Input: Real-time image data and location information

[0725] Output: Data packet containing compressed image data and location information

[0726] Specific operation: The terminal compresses and packets the acquired data via the 5G communication module and sends it to the server. At this time, data priority is set and packet retransmission control is performed to minimize data transmission delays.

[0727] Step 3:

[0728] The server applies a generated AI model and image analysis algorithm based on the received image data and location information to analyze the surrounding environment.

[0729] Input: Compressed image data and location information

[0730] Output: Analyzed environmental information (object position, type, etc.)

[0731] How it works: The server decodes the received data and uses object detection algorithms to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then uses Mapbox's API to obtain high-precision map information and complete the location of objects and situations.

[0732] Step 4:

[0733] The server uses a generative AI model based on the analysis results to generate feedback for the user.

[0734] Input: Parsed environment information

[0735] Output: Feedback in natural language

[0736] Specific behavior: A prompt sentence (e.g., "There is a red light at the intersection. Please write an explanatory sentence instructing the user to stop") is input into the generative AI model, and appropriate feedback sentences are generated based on the analysis results. For example, specific behavioral instructions such as "The light is red, so stop and wait" are generated.

[0737] Step 5:

[0738] The server sends the generated feedback to the terminal via 5G communication.

[0739] Input: Feedback in natural language

[0740] Output: Feedback data sent to the device

[0741] Specific operation: The server encodes the generated feedback and uses a communication protocol optimized for Quality of Service (QoS) to transfer data with low latency.

[0742] Step 6:

[0743] The terminal provides the received feedback to the user through an audio output device.

[0744] Input: Feedback data sent to the device

[0745] Output: Vocalized feedback and optional vibration feedback

[0746] Specific operation: The device sends text data to a speech synthesis engine, which converts it into natural-sounding speech. The synthesized instructions are then played back through the user's earpiece or speaker. If necessary, a dedicated vibration device will be activated to provide a physical notification to the user.

[0747] (Application example 1)

[0748] 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."

[0749] At-risk users, such as the visually impaired and elderly, have difficulty independently navigating physical stores safely and smoothly and finding the products they are looking for. Conventional navigation systems often require visual information, making them difficult for visually impaired people to use. In addition, store layouts and product placement change frequently, making it difficult to respond to changes using static maps alone. Therefore, there is a need for a system that provides real-time environmental information to support users.

[0750] 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.

[0751] In this invention, the server includes means for acquiring image data of the store using a camera worn by the user, means for acquiring location information, means for transmitting the image data and the location information to the server via a communication network, means for analyzing the image data and the location information in the server, means for identifying the user's current location and surrounding objects in the store based on the analysis results in the server, and means for generating guidance to the user's destination in real time based on the identified objects, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people and elderly people to move independently and safely through a physical store and find the products they are looking for.

[0752] "Users" are people at risk, such as visually impaired people or elderly people, who use the system.

[0753] A "camera" is a device that is worn at a position close to the user's line of sight and that captures image data of the surroundings.

[0754] "Location information" refers to data on the user's current geographical location obtained using a module such as a GPS.

[0755] "Communications network" refers to a network that serves as infrastructure for transferring data at high speed and with low latency, and includes the fifth-generation mobile communications system (5G).

[0756] A "server" is a computing device that receives and analyzes image data and location information.

[0757] "Analysis" refers to the process of identifying and identifying the surrounding environment using a generative AI model and image analysis algorithms based on the received image data and location information.

[0758] "Feedback" is guidance information that helps the user safely reach their destination based on the results of analysis by the server.

[0759] "Audio" is the output form of feedback provided to the user, allowing the user to understand the situation in real time.

[0760] "In-store navigation" is a support system that helps users navigate safely within a physical store and reach their desired products or areas.

[0761] "Objects" are elements that are involved in the user's movement and reaching their destination, such as shelves, products, and obstacles present in the store.

[0762] This invention is a system that enables at-risk users, such as the visually impaired and elderly, to navigate independently in physical stores and safely find the products they are looking for. The system consists of a wearable camera worn by the user, a GPS module, 5G communications, a server, a generative AI model, Mapbox API, and a voice output device.

[0763] First, the user wears a wearable camera. This camera is attached at a position close to the user's eye level and captures image data of the store in real time. Furthermore, by using a GPS module, the user's current location is acquired with high accuracy.

[0764] The device then transmits the acquired image data and location information to a server via 5G communication. The high speed and low latency characteristics of 5G communication enable the server to always receive the latest data, even when the user is on the move.

[0765] The server analyzes the received image data and location information using a generative AI model and image analysis algorithm. Specifically, the image analysis algorithm identifies shelves, products, obstacles, etc. in the store, and then references map information from Mapbox to complement the analysis results. This allows the user's current location and surrounding objects to be identified.

[0766] Based on the analysis results, the server generates guidance information to help the user safely reach their destination. This guidance information is generated in real time in natural language and includes specific instructions such as, "There is a fruit corner 10 meters ahead on the left."

[0767] The generated feedback is then sent back to the device via 5G communication, and the device then provides the received feedback to the user via an audio output device, such as an earpiece or speaker, allowing the user to understand the situation in real time and take appropriate action.

[0768] As a concrete example, consider the case where a user is in a supermarket. The user is wearing a wearable camera and walking around the store. As they do so, the camera captures images of products on the shelves, and GPS acquires their location information. This data is sent to a server via 5G communication. Based on image analysis and location information, the server generates guidance such as "There is a fruit section 10 meters ahead on the left," sends this to the device, and notifies the user via voice.

[0769] An example of a prompt might be:

[0770] "Design a system that identifies the surrounding environment and generates directions to help the user reach their destination."

[0771] "Create prompts for your AI model that generate feedback to help visually impaired people safely find individual items in a store."

[0772] This will enable visually impaired people and the elderly to enjoy shopping in physical stores with peace of mind.

[0773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0774] Step 1:

[0775] A user puts on a wearable camera and starts moving around the store. The camera continuously captures image data of the surroundings, and the GPS module acquires the user's current location. The inputs are images from the camera and location information from the GPS, and these data are collected by the device. The output is a set of image data and location information.

[0776] Step 2:

[0777] The device sends the collected image data and location information to the server via 5G communication. Using a communication protocol, the data is transferred to the server quickly and with low latency. The input here is the image data and location information obtained in step 1, and the output is the data transferred to the server.

[0778] Step 3:

[0779] The server uses a generative AI model and image analysis algorithm to analyze the received image data and location information. Specifically, the image analysis algorithm identifies objects such as shelves, products, and obstacles in the store, and complements the analysis results by integrating GPS location information and Mapbox map information. The input here is image data and location information, and the output is the object identification results and complemented location information.

[0780] Step 4:

[0781] Based on the analysis results, the server generates feedback to help the user safely reach their destination. The generative AI model generates real-time guidance information in natural language. For example, it includes specific guidance such as "There is a fruit corner 10 meters ahead on the left." The input here is the analysis result from Step 3, and the output is the feedback text.

[0782] Step 5:

[0783] The generated feedback is sent to the device via 5G communication. The communication protocol ensures that the feedback reaches the user quickly. The input here is the feedback text, and the output is the feedback text transmitted to the device.

[0784] Step 6:

[0785] The device provides the received feedback to the user through an audio output device, using a text-to-speech (TTS) engine to convert the text into speech and play it through an earpiece or speaker, where the input is the feedback text and the output is a natural language voice prompt.

[0786] Step 7:

[0787] Users receive audio feedback and follow the guidance to navigate the store. By following specific guidance, they can safely reach products and areas. The input here is audio feedback, and the output is the user's movement behavior.

[0788] 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.

[0789] This invention relates to a real-time assistance system that enables users at risk, such as the visually impaired, to act independently and safely. The system is basically composed of a camera worn by the user, a GPS module, and an emotion engine, and by transmitting these data to a server via 5G communication, it provides feedback according to the surrounding environment and the user's emotional state.

[0790] 1. Acquiring images and location information

[0791] The user wears a wearable camera, which captures image data every second. At the same time, the GPS module acquires the user's current location information. This allows visual and location information to be collected continuously while the user is moving.

[0792] 2. Sending images, location information, and emotion data to the server

[0793] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to determine the user's emotional state.

[0794] 3. Analysis of environmental and emotional data

[0795] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[0796] 4. Generating risk and environmental information

[0797] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as, "Don't worry. Cross when the light turns green."

[0798] 5. Submitting Feedback

[0799] The server then sends the generated feedback to the device via 5G, allowing users to receive the necessary information in real time.

[0800] 6. Real-time feedback

[0801] The device converts the received feedback into a voice message and provides it to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[0802] Specific examples

[0803] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[0804] The server uses image analysis to identify traffic lights, and then references map information from Mapbox to determine the current traffic light status. It then generates instructions, such as "The light is red, so please stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[0805] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotion data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[0806] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] A user walks around wearing a wearable camera. The camera captures images every second, and the GPS module obtains the user's current location.

[0810] Step 2:

[0811] The device converts the captured image data and location information into data packets, using an emotion engine to analyze the user's voice tone, facial expressions, and heart rate.

[0812] Step 3:

[0813] The device then sends the captured image data, location information, and emotion data detected by the emotion engine to a server via 5G communication.

[0814] Step 4:

[0815] The server analyzes the received image data using an image analysis algorithm (e.g., YOLOv5) to identify objects such as traffic lights, vehicles, and pedestrians. It also calls a map API (e.g., Mapbox) to obtain geographic information based on the location information.

[0816] Step 5:

[0817] The server combines image analysis results with geographic information to generate feedback to guide users in safe behavior, with tone and content adjusted based on emotional data.

[0818] Step 6:

[0819] The server converts the generated feedback into data packets and sends them to the device via 5G communication.

[0820] Step 7:

[0821] The device converts the received feedback into an audio message and provides it to the user in real time through a speaker or earpiece, optionally with vibration feedback.

[0822] Specific examples

[0823] For example, consider a situation where a user approaches an intersection. In this case, the device captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[0824] The server uses image analysis to identify traffic lights, and then refers to map information to determine the current traffic light status. It then generates instructions, such as "The light is red, so stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[0825] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotional data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[0826] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0827] Example 2

[0828] 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."

[0829] Currently, it is difficult for users who are at risk, such as those with visual impairments, to act independently and safely. Specifically, it is difficult for them to accurately grasp information about their surroundings and take appropriate action. Furthermore, the user's emotional state often influences their behavior, and there is a need for support systems that take this into consideration.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0831] In this invention, the server includes means for acquiring image data using a camera worn by a user, means for acquiring location information, means for detecting emotional data, means for transmitting the image data, the location information, and the emotional data to the server via a communication network, means for analyzing the image data, the location information, and the emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people to grasp information about their surrounding environment and risks in real time and receive appropriate feedback according to their emotional state.

[0832] "User" refers to an individual who uses the system to receive assistance, such as a visually impaired person.

[0833] "Camera" refers to a wearable device worn by a user to capture image data.

[0834] "Location information" refers to data indicating the user's current location obtained using a GPS module or the like.

[0835] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing biometric data such as the user's voice tone, facial expression, and heart rate.

[0836] "Communications network" refers to the network infrastructure for transmitting and receiving data, and specifically includes fifth generation mobile communications systems (5G).

[0837] "Server" refers to a computer system that analyzes the received data and generates and sends feedback to the user.

[0838] "Feedback" refers to messages containing notifications and instructions for users to act safely based on the analysis results.

[0839] "Analysis" refers to the process of processing received image data, location information, and emotion data to assess the user's current state and environment.

[0840] This invention relates to a real-time support system that enables users at risk, such as the visually impaired, to move independently and safely. The system is basically composed of a camera worn by the user, a GPS module that acquires location information, and an emotion engine that detects emotional data. By transmitting this data to a server via a communication network, the system provides feedback according to the surrounding environment and the user's emotional state.

[0841] Image and location capture

[0842] The user wears a wearable camera, which captures image data every second, while the GPS module acquires the user's current location information, allowing visual and location information to be collected continuously while the user is moving.

[0843] Sending images, location information, and emotion data to the server

[0844] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state.

[0845] Analysis of environmental and emotional data

[0846] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[0847] Generating risk and environmental information

[0848] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as "Don't worry. Cross the street when the light turns green."

[0849] Send Feedback

[0850] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[0851] Real-time feedback

[0852] The device converts the received feedback into voice messages and provides them to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[0853] Specific examples

[0854] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[0855] The server identifies the traffic light through image analysis and determines the current traffic light status by referring to map information. It then generates instructions such as "The light is red, so please stop and wait" in a tone that helps the user regain their composure. The device then conveys this to the user through voice.

[0856] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device will capture images, location information, and emotional data, which the server will analyze. The server will then generate instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notify the user via the device.

[0857] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[0858] Prompt Sentence Examples

[0859] Example 1: "Please explain the flow of an assistance system that helps visually impaired people cross intersections safely."

[0860] Example 2: "Please explain with specific examples how a real-time feedback system using an emotion engine can support users."

[0861] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0862] Step 1: Acquire images and location information

[0863] The user wears a camera and a GPS module. The camera captures image data every second, and the GPS module obtains the user's current location.

[0864] Input: Camera shutter speed, GPS satellite signal

[0865] Output: Captured image data, current latitude and longitude information

[0866] How it works: The camera takes a still image every second, and the GPS module simultaneously acquires the latitude and longitude of the current location, which is then stored on the device.

[0867] Step 2: Packetize and transmit the data

[0868] The device packetizes the acquired image data, location information, and emotion data obtained from the emotion engine, and transmits them to a server using 5G communications.

[0869] Input: image data, location information, emotion data

[0870] Output: Packetized data sent to the server

[0871] How it works: The device breaks down each piece of data into small packets to ensure accurate delivery to the server, and the packetized data is then immediately sent to the server via 5G communication.

[0872] Step 3: Analyze environmental and emotional data

[0873] The server analyzes the received image data, location information, and emotion data. It uses image analysis algorithms such as YOLOv5 to identify objects, while simultaneously obtaining geographic information based on the location information and evaluating the emotion data.

[0874] Input: Image data, location information, and emotion data sent to the server

[0875] Output: Analyzed object information, geographic information, emotion evaluation

[0876] Specific operation: The server runs the YOLOv5 algorithm, analyzes image data, and detects objects such as traffic lights, cars, and pedestrians. It also calls a map API to obtain geographic information based on the location information. Emotion data evaluates the user's emotional state based on biometric information.

[0877] Step 4: Generate risk and environmental information

[0878] The server generates feedback based on the analysis results to help users behave safely, and the tone and content of the feedback are adjusted according to the user's emotional state.

[0879] Input: Analyzed object information, geographic information, emotion evaluation

[0880] Output: Feedback message

[0881] Specific operation: The server comprehensively evaluates the analysis results and creates a feedback message such as "The traffic light is red. Please stop and wait." It also determines the tone of the feedback based on the emotional state.

[0882] Step 5: Submit your feedback

[0883] The server re-packetizes the generated feedback and transmits it to the terminal using 5G communication.

[0884] Input: Feedback message

[0885] Output: Feedback message sent to the terminal

[0886] Specific operation: The server divides the generated feedback message into small packets and sends them to the terminal.

[0887] Step 6: Real-time feedback

[0888] The device converts the received feedback into audio messages and provides them to the user through a speaker or earpiece, and optionally uses vibration feedback.

[0889] Input: Feedback message received

[0890] Output: Audio and vibration feedback provided to the user

[0891] Specific operation: The device's speech synthesis engine converts the feedback message into voice and conveys it to the user through the speaker or earphones. In some cases, it may also activate the vibration function to alert the user.

[0892] (Application example 2)

[0893] 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."

[0894] In the work environment within a factory, there is a need for a system that can grasp visual information and the emotional state of workers in real time to support them in working safely and efficiently. Current systems have difficulty providing appropriate feedback in real time according to the risks and emotional state of workers during work, which has led to problems with industrial accidents and reduced work efficiency. In particular, a major issue is the inability to respond quickly to the approach of dangerous objects or emotional stress.

[0895] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring image data using a camera worn by the user, means for acquiring location information, means for transmitting the image data, the location information, and emotional data to the server via a communication network, means for analyzing the image data, the location information, and emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This makes it possible to grasp visual information and emotional states in the work environment in real time and provide appropriate feedback according to the emotional state.

[0896] "User" refers to an individual who uses the system of the present invention, particularly a worker who performs work in a factory.

[0897] A "camera" is a device worn by a user that captures image data in real time. It is attached to a wearable device.

[0898] "Image data" refers to visual information acquired through cameras, including the environment and risk factors within the factory.

[0899] "Location information" is data that indicates the user's current location. It is acquired using a GPS module.

[0900] "Emotion data" is data that indicates the user's emotional state and is generated by the emotion engine. It is obtained by analyzing biometric data.

[0901] "Communication network" refers to the network infrastructure used to transmit data to the server. Specifically, it uses the fifth-generation mobile communication system (5G).

[0902] The "server" is a computer system that analyzes the transmitted image data, location information, and emotion data and generates feedback.

[0903] "Analysis means" refers to the function of analyzing the data received by the server, including image analysis and emotion analysis.

[0904] "Feedback" refers to instructions or information generated based on the analysis results, and provided to the user in the form of voice messages or other formats.

[0905] "Audio output means" refers to a device that converts feedback into audio format and provides it to the user, such as a speaker in smart glasses.

[0906] A "prompt" is predefined text used by a generative AI model to generate feedback.

[0907] The present invention relates to a real-time support system for factory workers to perform their work safely and efficiently. This system acquires image data, location information, and emotion data in real time and provides appropriate feedback based on the analysis results. Specifically, it includes the following components:

[0908] 1. Acquiring images and location information

[0909] The user wears a wearable device (e.g., smart glasses) and uses a camera to capture image data every second. The GPS module also captures the user's current location information. This allows visual and location information to be collected continuously as the user moves around the factory.

[0910] 2. Acquiring Emotion Data

[0911] The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state, which is also captured simultaneously.

[0912] 3. Sending data to the server

[0913] The device (such as smart glasses) packetizes the acquired image data, location information, and emotion data and transmits them to a server using 5G communication.

[0914] 4. Data Analysis

[0915] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (e.g., dangerous objects, mobile devices, etc.), obtains geographic information based on the location information, and evaluates the user's current emotional state based on the emotion data.

[0916] 5. Generate feedback

[0917] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. When generating feedback using a generative AI model, pre-defined prompt sentences are used.

[0918] 6. Submitting Feedback

[0919] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[0920] 7. Real-time feedback output

[0921] The device converts the received feedback into voice messages and provides them to the user through the smart glasses' speakers or earpiece, and also provides vibration feedback if necessary. The content and tone of the feedback are adjusted according to the user's emotional state, providing more appropriate assistance.

[0922] Specific examples

[0923] For example, consider a situation where a user is working near a forklift in a factory. In this case, the device (smart glasses) captures images and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[0924] The server identifies the forklift through image analysis and generates instructions such as "A forklift is approaching. Please be careful" in a tone that helps the user regain their composure. The terminal then conveys this information to the user via voice.

[0925] Similarly, if the user is fatigued, the device will acquire images, location information, and emotional data, which will be analyzed by the server. The server will then generate instructions such as "Please take a short rest" in a tone that matches the user's emotional state and notify the user via the device.

[0926] Prompt Sentence Examples

[0927] “YOLOv5: Identify and classify objects in an image of a factory setting.

[0928] Model: TensorFlow emotion model to evaluate emotional state based on input biophysical data.

[0929] Scenario:

[0930] Object detection result: {'dangerous_item': True}

[0931] Emotional state: 'stressed'

[0932] Generate appropriate feedback for the above scenario considering both object detection and emotional state.

[0933] Output example: 'Dangerous object approaching. Be careful. Take a short break to relax.'

[0934] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0935] Step 1:

[0936] The user wears a wearable device (smart glasses), and the camera captures images every second. At the same time, the GPS module acquires location information. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to generate emotion data. The inputs are camera images, location information, and biometric data, and the outputs are image data, location information, and emotion data.

[0937] Step 2:

[0938] The device packetizes the acquired image data, location information, and emotion data and transmits them to the server using 5G communication. The input is packetized data, and the output is packets transmitted via 5G communication. The specific operation involves packetizing the data and transmitting the data according to the communication protocol.

[0939] Step 3:

[0940] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (YOLOv5) to identify objects in the image (such as dangerous objects or moving equipment) and obtains geographic information based on the location information. It evaluates the user's current emotional state based on the emotion data. The input is the received image data, location information, and emotion data, and the output is the analysis result.

[0941] Step 4:

[0942] Based on the analysis results, the server generates feedback to help the user act safely. When generating feedback using a generative AI model, a predefined prompt sentence is used. The input is the analysis result and the prompt sentence, and the output is a feedback message. Specifically, the server supplies data to the generative AI model and generates appropriate feedback.

[0943] Step 5:

[0944] The server sends the generated feedback to the terminal via 5G communication. The input is the feedback message, and the output is the feedback sent via 5G communication. The specific operation involves packetizing the feedback message and transmitting the data according to the communication protocol.

[0945] Step 6:

[0946] The device converts the received feedback into an audio message and provides it to the user through the smart glasses' speaker or earpiece. It also uses vibration feedback if necessary. The input is the received feedback message, and the output is audio or vibration feedback. Specifically, the device generates audio data and provides feedback through the device.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] [Fourth embodiment]

[0951] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0952] 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.

[0953] 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).

[0954] 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.

[0955] 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.

[0956] 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).

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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."

[0964] The purpose of this invention is to provide a real-time support system for visually impaired people and other users at risk to act independently and safely. This system consists of the following main components:

[0965] 1. Acquiring images and location information

[0966] The system uses a wearable camera and GPS module worn by the user to acquire image data of the surroundings and the user's location. The wearable camera is attached at a position close to the user's eye level and continuously captures real-time visual information while walking. The GPS module measures the user's current location with high accuracy.

[0967] 2. Sending images and location information to the server

[0968] The device transmits the acquired image data and location information to the server in real time. 5G communication enables high-speed, low-latency data transfer, allowing the server to constantly provide up-to-date environmental information, even while the user is on the move.

[0969] 3. Analysis of Environmental Data

[0970] The server analyzes the surrounding environment using generative AI models and image analysis algorithms based on the received image data and location information. For example, the image analysis algorithm identifies objects such as intersections, traffic lights, pedestrians, and vehicles, and then pinpoints their locations based on the location information. Furthermore, the server complements the analysis results with high-precision map information from Mapbox.

[0971] 4. Generating risk and environmental information

[0972] Based on the analysis results, the server generates feedback in natural language to help users act safely. The feedback includes real-time risk information such as the status of traffic lights at intersections, approaching vehicles, and pedestrian movements. The focus is on providing feedback that allows users to respond quickly and appropriately.

[0973] 5. Submitting Feedback

[0974] The server then sends the generated feedback to the device via 5G, allowing users to receive the information they need with low latency.

[0975] 6. Real-time feedback

[0976] The device then provides the received feedback to the user via an audio output device (speaker or earpiece). Real-time audio guidance allows even visually impaired users to instantly understand the current situation and take appropriate action. The device can also provide additional notification features, such as vibration feedback, if needed.

[0977] Specific examples

[0978] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information using GPS. This information is sent to the server via 5G communication. The server identifies the traffic light using image analysis and determines the current traffic light status by referring to map information from Mapbox. It then generates instructions such as "The light is red, so stop and wait" and sends them to the device. The device then communicates this to the user via voice.

[0979] Similarly, if a user is walking down a sidewalk and is about to come across a staircase, the device will acquire image and location information, and the server will detect the presence of the staircase. The server will then generate a warning such as "There are stairs 10 meters ahead. Please proceed with caution" and notify the user via the device.

[0980] This system enables visually impaired people to understand their surroundings in real time and act safely. Furthermore, by improving the accuracy of analysis and the quality of feedback on the server, we can expect to further improve the user experience.

[0981] The processing flow will be explained below.

[0982] Step 1:

[0983] The device uses a wearable camera to capture images of the surroundings every second, and also uses a GPS module to obtain its current location (latitude and longitude).

[0984] Step 2:

[0985] The device then converts the captured image data and location information into a data packet and sends it to the server using 5G communications.

[0986] Step 3:

[0987] The server inputs the received image data and location information into an AI model for analysis, which uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.).

[0988] Step 4:

[0989] The server then calls a map API (e.g., Mapbox) to obtain detailed geographic information based on the location information, such as intersections, sidewalks, and buildings at the user's current location.

[0990] Step 5:

[0991] The server combines the image analysis results with map information and generates the feedback the user needs to act safely, such as "The traffic light is red. Please stop before the crosswalk."

[0992] Step 6:

[0993] The server converts the generated feedback into data packets and sends them back to the device using 5G communications.

[0994] Step 7:

[0995] The device converts the received feedback into audio messages and provides them to the user in real time through a speaker or earpiece, along with vibration feedback if necessary.

[0996] Example 1

[0997] 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."

[0998] For visually impaired and other at-risk users to act independently and safely, they need to acquire information about their surroundings in real time and receive appropriate feedback. However, conventional systems often experience delays in acquiring and analyzing this information and generating and providing feedback, which can prevent users from responding appropriately. This can result in safety issues. Furthermore, previous technologies have not yet realized the provision of real-time feedback using generative AI models.

[0999] 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.

[1000] In this invention, the server includes means for analyzing image data and location information, means for generating feedback to the user using a generative AI model, and means for transmitting the feedback to the user via a communication network and outputting it as audio, thereby enabling visually impaired and other at-risk users to understand their surroundings in real time and take appropriate action.

[1001] "User" refers to the individual end user of the system, particularly those who are visually impaired or otherwise at risk.

[1002] A "camera" refers to a device worn by a user that captures image data of the surroundings in real time.

[1003] "Image data" refers to digital data of visual information captured using a camera.

[1004] "Location Information" means geographic information obtained using a GPS module or other location tracking technology to identify a User's current location.

[1005] "Communications Network" means the network infrastructure used to transmit and receive data in real time, and specifically includes fifth generation mobile communications systems (5G).

[1006] "Server" refers to a sophisticated computer system for analyzing acquired data and generating and transmitting feedback.

[1007] "Image analysis" refers to algorithms and techniques used to identify specific objects or situations based on acquired image data.

[1008] A "generative AI model" refers to an artificial intelligence model that performs natural language processing and advanced data analysis, and is specifically used to generate feedback for users.

[1009] "Feedback" refers to instructions or information that the server generates based on the analysis results and provides to the user.

[1010] "Audio output" refers to an audio notification method for conveying feedback to the user, including devices such as speakers and earpieces.

[1011] This invention is a support system for visually impaired and other at-risk users to move independently and safely. The system uses a wearable camera, a GPS module, a generative AI model, image analysis algorithms, and advanced communication network technology.

[1012] The basic hardware configuration involves the user wearing a wearable camera and a GPS module. The wearable camera is attached close to the user's viewpoint and captures visual information of the surroundings in real time. The GPS module is responsible for measuring the user's current location with high accuracy.

[1013] Data Acquisition

[1014] As the user moves, the wearable camera continuously captures image data, and the GPS module acquires location information, which is then immediately sent to the device and transmitted to a server via 5G communication.

[1015] Data analysis

[1016] The server analyzes the environment using generative AI models and image analysis algorithms based on the received image data and location information. Specifically, it uses object detection algorithms (e.g., YOLO - You Only Look Once) to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then complements the analysis results with high-precision map information from Mapbox.

[1017] Generate feedback

[1018] The server generates feedback for the user based on the analysis results. A generative AI model (e.g., GPT-4) creates feedback content in natural language based on the analysis results. The following is an example of a prompt:

[1019] "There is a red light at the intersection. Please create a description to instruct the user to stop."

[1020] By entering such a prompt, appropriate feedback is generated, such as "The light is red, so stop and wait."

[1021] Sending and outputting feedback

[1022] The generated feedback is transmitted to the device via 5G communication, and the device provides the received feedback to the user via an audio output device (speaker or earpiece), and can also have additional notification features such as vibration feedback.

[1023] Specific examples

[1024] For example, when a user approaches an intersection, the device captures an image and obtains GPS location information. This information is sent in real time to a server, which then uses image analysis to determine the traffic light status and uses a generative AI model to generate an instruction such as "The light is red, so stop and wait." This instruction is then sent to the device and communicated to the user via a voice output device. Similarly, if a user is walking down a sidewalk and comes across stairs, the device will use image analysis and location information to detect their presence and generate a warning saying, "There are stairs 10 meters ahead. Proceed with caution."

[1025] The above is a specific embodiment of the present invention. This system enables the user to act independently and safely.

[1026] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1027] Step 1:

[1028] The user puts on the wearable camera and GPS module and starts the system.

[1029] Input: User's visual information (image data) and current location information

[1030] Output: Real-time image data and location information

[1031] How it works: The wearable camera captures images at a rate of 30 frames per second, and the GPS module updates the user's location every second.

[1032] Step 2:

[1033] The device transmits the acquired image data and location information to the server in real time.

[1034] Input: Real-time image data and location information

[1035] Output: Data packet containing compressed image data and location information

[1036] Specific operation: The terminal compresses and packets the acquired data via the 5G communication module and sends it to the server. At this time, data priority is set and packet retransmission control is performed to minimize data transmission delays.

[1037] Step 3:

[1038] The server applies a generated AI model and image analysis algorithm based on the received image data and location information to analyze the surrounding environment.

[1039] Input: Compressed image data and location information

[1040] Output: Analyzed environmental information (object position, type, etc.)

[1041] How it works: The server decodes the received data and uses object detection algorithms to identify objects such as intersections, traffic lights, pedestrians, and vehicles. It then uses Mapbox's API to obtain high-precision map information and complete the location of objects and situations.

[1042] Step 4:

[1043] The server uses a generative AI model based on the analysis results to generate feedback for the user.

[1044] Input: Parsed environment information

[1045] Output: Feedback in natural language

[1046] Specific behavior: A prompt sentence (e.g., "There is a red light at the intersection. Please write an explanatory sentence instructing the user to stop") is input into the generative AI model, and appropriate feedback sentences are generated based on the analysis results. For example, specific behavioral instructions such as "The light is red, so stop and wait" are generated.

[1047] Step 5:

[1048] The server sends the generated feedback to the terminal via 5G communication.

[1049] Input: Feedback in natural language

[1050] Output: Feedback data sent to the device

[1051] Specific operation: The server encodes the generated feedback and uses a communication protocol optimized for Quality of Service (QoS) to transfer data with low latency.

[1052] Step 6:

[1053] The terminal provides the received feedback to the user through an audio output device.

[1054] Input: Feedback data sent to the device

[1055] Output: Vocalized feedback and optional vibration feedback

[1056] Specific operation: The device sends text data to a speech synthesis engine, which converts it into natural-sounding speech. The synthesized instructions are then played back through the user's earpiece or speaker. If necessary, a dedicated vibration device will be activated to provide a physical notification to the user.

[1057] (Application example 1)

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

[1059] At-risk users, such as the visually impaired and elderly, have difficulty independently navigating physical stores safely and smoothly and finding the products they are looking for. Conventional navigation systems often require visual information, making them difficult for visually impaired people to use. In addition, store layouts and product placement change frequently, making it difficult to respond to changes using static maps alone. Therefore, there is a need for a system that provides real-time environmental information to support users.

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

[1061] In this invention, the server includes means for acquiring image data of the store using a camera worn by the user, means for acquiring location information, means for transmitting the image data and the location information to the server via a communication network, means for analyzing the image data and the location information in the server, means for identifying the user's current location and surrounding objects in the store based on the analysis results in the server, and means for generating guidance to the user's destination in real time based on the identified objects, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people and elderly people to move independently and safely through a physical store and find the products they are looking for.

[1062] "Users" are people at risk, such as visually impaired people or elderly people, who use the system.

[1063] A "camera" is a device that is worn at a position close to the user's line of sight and that captures image data of the surroundings.

[1064] "Location information" refers to data on the user's current geographical location obtained using a module such as a GPS.

[1065] "Communications network" refers to a network that serves as infrastructure for transferring data at high speed and with low latency, and includes the fifth-generation mobile communications system (5G).

[1066] A "server" is a computing device that receives and analyzes image data and location information.

[1067] "Analysis" refers to the process of identifying and identifying the surrounding environment using a generative AI model and image analysis algorithms based on the received image data and location information.

[1068] "Feedback" is guidance information that helps the user safely reach their destination based on the results of analysis by the server.

[1069] "Audio" is the output form of feedback provided to the user, allowing the user to understand the situation in real time.

[1070] "In-store navigation" is a support system that helps users navigate safely within a physical store and reach their desired products or areas.

[1071] "Objects" are elements that are involved in the user's movement and reaching their destination, such as shelves, products, and obstacles present in the store.

[1072] This invention is a system that enables at-risk users, such as the visually impaired and elderly, to navigate independently in physical stores and safely find the products they are looking for. The system consists of a wearable camera worn by the user, a GPS module, 5G communications, a server, a generative AI model, Mapbox API, and a voice output device.

[1073] First, the user wears a wearable camera. This camera is attached at a position close to the user's eye level and captures image data of the store in real time. Furthermore, by using a GPS module, the user's current location is acquired with high accuracy.

[1074] The device then transmits the acquired image data and location information to a server via 5G communication. The high speed and low latency characteristics of 5G communication enable the server to always receive the latest data, even when the user is on the move.

[1075] The server analyzes the received image data and location information using a generative AI model and image analysis algorithm. Specifically, the image analysis algorithm identifies shelves, products, obstacles, etc. in the store, and then references map information from Mapbox to complement the analysis results. This allows the user's current location and surrounding objects to be identified.

[1076] Based on the analysis results, the server generates guidance information to help the user safely reach their destination. This guidance information is generated in real time in natural language and includes specific instructions such as, "There is a fruit corner 10 meters ahead on the left."

[1077] The generated feedback is then sent back to the device via 5G communication, and the device then provides the received feedback to the user via an audio output device, such as an earpiece or speaker, allowing the user to understand the situation in real time and take appropriate action.

[1078] As a concrete example, consider the case where a user is in a supermarket. The user is wearing a wearable camera and walking around the store. As they do so, the camera captures images of products on the shelves, and GPS acquires their location information. This data is sent to a server via 5G communication. Based on image analysis and location information, the server generates guidance such as "There is a fruit section 10 meters ahead on the left," sends this to the device, and notifies the user via voice.

[1079] An example of a prompt might be:

[1080] "Design a system that identifies the surrounding environment and generates directions to help the user reach their destination."

[1081] "Create prompts for your AI model that generate feedback to help visually impaired people safely find individual items in a store."

[1082] This will enable visually impaired people and the elderly to enjoy shopping in physical stores with peace of mind.

[1083] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1084] Step 1:

[1085] A user puts on a wearable camera and starts moving around the store. The camera continuously captures image data of the surroundings, and the GPS module acquires the user's current location. The inputs are images from the camera and location information from the GPS, and these data are collected by the device. The output is a set of image data and location information.

[1086] Step 2:

[1087] The device sends the collected image data and location information to the server via 5G communication. Using a communication protocol, the data is transferred to the server quickly and with low latency. The input here is the image data and location information obtained in step 1, and the output is the data transferred to the server.

[1088] Step 3:

[1089] The server uses a generative AI model and image analysis algorithm to analyze the received image data and location information. Specifically, the image analysis algorithm identifies objects such as shelves, products, and obstacles in the store, and complements the analysis results by integrating GPS location information and Mapbox map information. The input here is image data and location information, and the output is the object identification results and complemented location information.

[1090] Step 4:

[1091] Based on the analysis results, the server generates feedback to help the user safely reach their destination. The generative AI model generates real-time guidance information in natural language. For example, it includes specific guidance such as "There is a fruit corner 10 meters ahead on the left." The input here is the analysis result from Step 3, and the output is the feedback text.

[1092] Step 5:

[1093] The generated feedback is sent to the device via 5G communication. The communication protocol ensures that the feedback reaches the user quickly. The input here is the feedback text, and the output is the feedback text transmitted to the device.

[1094] Step 6:

[1095] The device provides the received feedback to the user through an audio output device, using a text-to-speech (TTS) engine to convert the text into speech and play it through an earpiece or speaker, where the input is the feedback text and the output is a natural language voice prompt.

[1096] Step 7:

[1097] Users receive audio feedback and follow the guidance to navigate the store. By following specific guidance, they can safely reach products and areas. The input here is audio feedback, and the output is the user's movement behavior.

[1098] 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.

[1099] This invention relates to a real-time assistance system that enables users at risk, such as the visually impaired, to act independently and safely. The system is basically composed of a camera worn by the user, a GPS module, and an emotion engine, and by transmitting these data to a server via 5G communication, it provides feedback according to the surrounding environment and the user's emotional state.

[1100] 1. Acquiring images and location information

[1101] The user wears a wearable camera, which captures image data every second. At the same time, the GPS module acquires the user's current location information. This allows visual and location information to be collected continuously while the user is moving.

[1102] 2. Sending images, location information, and emotion data to the server

[1103] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to determine the user's emotional state.

[1104] 3. Analysis of environmental and emotional data

[1105] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[1106] 4. Generating risk and environmental information

[1107] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as, "Don't worry. Cross when the light turns green."

[1108] 5. Submitting Feedback

[1109] The server then sends the generated feedback to the device via 5G, allowing users to receive the necessary information in real time.

[1110] 6. Real-time feedback

[1111] The device converts the received feedback into a voice message and provides it to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[1112] Specific examples

[1113] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[1114] The server uses image analysis to identify traffic lights, and then references map information from Mapbox to determine the current traffic light status. It then generates instructions, such as "The light is red, so please stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[1115] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotion data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[1116] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[1117] The processing flow will be explained below.

[1118] Step 1:

[1119] A user walks around wearing a wearable camera. The camera captures images every second, and the GPS module obtains the user's current location.

[1120] Step 2:

[1121] The device converts the captured image data and location information into data packets, using an emotion engine to analyze the user's voice tone, facial expressions, and heart rate.

[1122] Step 3:

[1123] The device then sends the captured image data, location information, and emotion data detected by the emotion engine to a server via 5G communication.

[1124] Step 4:

[1125] The server analyzes the received image data using an image analysis algorithm (e.g., YOLOv5) to identify objects such as traffic lights, vehicles, and pedestrians. It also calls a map API (e.g., Mapbox) to obtain geographic information based on the location information.

[1126] Step 5:

[1127] The server combines image analysis results with geographic information to generate feedback to guide users in safe behavior, with tone and content adjusted based on emotional data.

[1128] Step 6:

[1129] The server converts the generated feedback into data packets and sends them to the device via 5G communication.

[1130] Step 7:

[1131] The device converts the received feedback into an audio message and provides it to the user in real time through a speaker or earpiece, optionally with vibration feedback.

[1132] Specific examples

[1133] For example, consider a situation where a user approaches an intersection. In this case, the device captures an image and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then transmitted to the server via 5G communication.

[1134] The server uses image analysis to identify traffic lights, and then refers to map information to determine the current traffic light status. It then generates instructions, such as "The light is red, so stop and wait," in a tone that helps the user regain their composure. The device then relays this information to the user via voice.

[1135] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device captures the image, location information, and emotional data, which are then analyzed by the server. The server then generates instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notifies the user via the device.

[1136] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[1137] Example 2

[1138] 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."

[1139] Currently, it is difficult for users who are at risk, such as those with visual impairments, to act independently and safely. Specifically, it is difficult for them to accurately grasp information about their surroundings and take appropriate action. Furthermore, the user's emotional state often influences their behavior, and there is a need for support systems that take this into consideration.

[1140] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1141] In this invention, the server includes means for acquiring image data using a camera worn by a user, means for acquiring location information, means for detecting emotional data, means for transmitting the image data, the location information, and the emotional data to the server via a communication network, means for analyzing the image data, the location information, and the emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This enables visually impaired people to grasp information about their surrounding environment and risks in real time and receive appropriate feedback according to their emotional state.

[1142] "User" refers to an individual who uses the system to receive assistance, such as a visually impaired person.

[1143] "Camera" refers to a wearable device worn by a user to capture image data.

[1144] "Location information" refers to data indicating the user's current location obtained using a GPS module or the like.

[1145] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing biometric data such as the user's voice tone, facial expression, and heart rate.

[1146] "Communications network" refers to the network infrastructure for transmitting and receiving data, and specifically includes fifth generation mobile communications systems (5G).

[1147] "Server" refers to a computer system that analyzes the received data and generates and sends feedback to the user.

[1148] "Feedback" refers to messages containing notifications and instructions for users to act safely based on the analysis results.

[1149] "Analysis" refers to the process of processing received image data, location information, and emotion data to assess the user's current state and environment.

[1150] This invention relates to a real-time support system that enables users at risk, such as the visually impaired, to move independently and safely. The system is basically composed of a camera worn by the user, a GPS module that acquires location information, and an emotion engine that detects emotional data. By transmitting this data to a server via a communication network, the system provides feedback according to the surrounding environment and the user's emotional state.

[1151] Image and location capture

[1152] The user wears a wearable camera, which captures image data every second, while the GPS module acquires the user's current location information, allowing visual and location information to be collected continuously while the user is moving.

[1153] Sending images, location information, and emotion data to the server

[1154] The device then packetizes the acquired image data, location information, and the user's emotional data detected by the emotion engine and transmits them to a server via 5G communications. The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state.

[1155] Analysis of environmental and emotional data

[1156] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (traffic lights, vehicles, pedestrians, etc.) and obtains geographic information based on the location information. It also evaluates the user's current emotional state based on the emotion data.

[1157] Generating risk and environmental information

[1158] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. For example, if the user is feeling nervous, the server generates calming feedback such as "Don't worry. Cross the street when the light turns green."

[1159] Send Feedback

[1160] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[1161] Real-time feedback

[1162] The device converts the received feedback into voice messages and provides them to the user through a speaker or earpiece, and optionally with vibration feedback. The content and tone of the feedback are tailored to the user's emotional state, providing more appropriate assistance.

[1163] Specific examples

[1164] For example, consider a situation where a user approaches an intersection. In this case, the device (wearable camera) captures an image and obtains location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[1165] The server identifies the traffic light through image analysis and determines the current traffic light status by referring to map information. It then generates instructions such as "The light is red, so please stop and wait" in a tone that helps the user regain their composure. The device then conveys this to the user through voice.

[1166] Similarly, if a user encounters an obstacle while walking on a sidewalk, the device will capture images, location information, and emotional data, which the server will analyze. The server will then generate instructions such as "There is an obstacle ahead. Please move to the right to avoid it" in a tone that matches the user's emotional state and notify the user via the device.

[1167] This system allows visually impaired people to grasp information about their surroundings and risks in real time, and receive appropriate feedback based on their emotional state, enabling them to act safely and independently.

[1168] Prompt Sentence Examples

[1169] Example 1: "Please explain the flow of an assistance system that helps visually impaired people cross intersections safely."

[1170] Example 2: "Please explain with specific examples how a real-time feedback system using an emotion engine can support users."

[1171] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1172] Step 1: Acquire images and location information

[1173] The user wears a camera and a GPS module. The camera captures image data every second, and the GPS module obtains the user's current location.

[1174] Input: Camera shutter speed, GPS satellite signal

[1175] Output: Captured image data, current latitude and longitude information

[1176] How it works: The camera takes a still image every second, and the GPS module simultaneously acquires the latitude and longitude of the current location, which is then stored on the device.

[1177] Step 2: Packetize and transmit the data

[1178] The device packetizes the acquired image data, location information, and emotion data obtained from the emotion engine, and transmits them to a server using 5G communications.

[1179] Input: image data, location information, emotion data

[1180] Output: Packetized data sent to the server

[1181] How it works: The device breaks down each piece of data into small packets to ensure accurate delivery to the server, and the packetized data is then immediately sent to the server via 5G communication.

[1182] Step 3: Analyze environmental and emotional data

[1183] The server analyzes the received image data, location information, and emotion data. It uses image analysis algorithms such as YOLOv5 to identify objects, while simultaneously obtaining geographic information based on the location information and evaluating the emotion data.

[1184] Input: Image data, location information, and emotion data sent to the server

[1185] Output: Analyzed object information, geographic information, emotion evaluation

[1186] Specific operation: The server runs the YOLOv5 algorithm, analyzes image data, and detects objects such as traffic lights, cars, and pedestrians. It also calls a map API to obtain geographic information based on the location information. Emotion data evaluates the user's emotional state based on biometric information.

[1187] Step 4: Generate risk and environmental information

[1188] The server generates feedback based on the analysis results to help users behave safely, and the tone and content of the feedback are adjusted according to the user's emotional state.

[1189] Input: Analyzed object information, geographic information, emotion evaluation

[1190] Output: Feedback message

[1191] Specific operation: The server comprehensively evaluates the analysis results and creates a feedback message such as "The traffic light is red. Please stop and wait." It also determines the tone of the feedback based on the emotional state.

[1192] Step 5: Submit your feedback

[1193] The server re-packetizes the generated feedback and transmits it to the terminal using 5G communication.

[1194] Input: Feedback message

[1195] Output: Feedback message sent to the terminal

[1196] Specific operation: The server divides the generated feedback message into small packets and sends them to the terminal.

[1197] Step 6: Real-time feedback

[1198] The device converts the received feedback into audio messages and provides them to the user through a speaker or earpiece, and optionally uses vibration feedback.

[1199] Input: Feedback message received

[1200] Output: Audio and vibration feedback provided to the user

[1201] Specific operation: The device's speech synthesis engine converts the feedback message into voice and conveys it to the user through the speaker or earphones. In some cases, it may also activate the vibration function to alert the user.

[1202] (Application example 2)

[1203] 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."

[1204] In the work environment within a factory, there is a need for a system that can grasp visual information and the emotional state of workers in real time to support them in working safely and efficiently. Current systems have difficulty providing appropriate feedback in real time according to the risks and emotional state of workers during work, which has led to problems with industrial accidents and reduced work efficiency. In particular, a major issue is the inability to respond quickly to the approach of dangerous objects or emotional stress.

[1205] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring image data using a camera worn by the user, means for acquiring location information, means for transmitting the image data, the location information, and emotional data to the server via a communication network, means for analyzing the image data, the location information, and emotional data in the server, means for generating feedback to the user based on the analysis results in the server, and means for transmitting the feedback to the user via the communication network and outputting it as audio. This makes it possible to grasp visual information and emotional states in the work environment in real time and provide appropriate feedback according to the emotional state.

[1206] "User" refers to an individual who uses the system of the present invention, particularly a worker who performs work in a factory.

[1207] A "camera" is a device worn by a user that captures image data in real time. It is attached to a wearable device.

[1208] "Image data" refers to visual information acquired through cameras, including the environment and risk factors within the factory.

[1209] "Location information" is data that indicates the user's current location. It is acquired using a GPS module.

[1210] "Emotion data" is data that indicates the user's emotional state and is generated by the emotion engine. It is obtained by analyzing biometric data.

[1211] "Communication network" refers to the network infrastructure used to transmit data to the server. Specifically, it uses the fifth-generation mobile communication system (5G).

[1212] The "server" is a computer system that analyzes the transmitted image data, location information, and emotion data and generates feedback.

[1213] "Analysis means" refers to the function of analyzing the data received by the server, including image analysis and emotion analysis.

[1214] "Feedback" refers to instructions or information generated based on the analysis results, and provided to the user in the form of voice messages or other formats.

[1215] "Audio output means" refers to a device that converts feedback into audio format and provides it to the user, such as a speaker in smart glasses.

[1216] A "prompt" is predefined text used by a generative AI model to generate feedback.

[1217] The present invention relates to a real-time support system for factory workers to perform their work safely and efficiently. This system acquires image data, location information, and emotion data in real time and provides appropriate feedback based on the analysis results. Specifically, it includes the following components:

[1218] 1. Acquiring images and location information

[1219] The user wears a wearable device (e.g., smart glasses) and uses a camera to capture image data every second. The GPS module also captures the user's current location information. This allows visual and location information to be collected continuously as the user moves around the factory.

[1220] 2. Acquiring Emotion Data

[1221] The emotion engine analyzes biometric data such as voice tone, facial expressions, and heart rate to determine the user's emotional state, which is also captured simultaneously.

[1222] 3. Sending data to the server

[1223] The device (such as smart glasses) packetizes the acquired image data, location information, and emotion data and transmits them to a server using 5G communication.

[1224] 4. Data Analysis

[1225] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (e.g., YOLOv5) to identify objects in the image (e.g., dangerous objects, mobile devices, etc.), obtains geographic information based on the location information, and evaluates the user's current emotional state based on the emotion data.

[1226] 5. Generate feedback

[1227] Based on the analysis results, the server generates feedback to help the user act safely. The tone and content of this feedback are adjusted according to the user's emotional state. When generating feedback using a generative AI model, pre-defined prompt sentences are used.

[1228] 6. Submitting Feedback

[1229] The server then sends the generated feedback to the device via 5G communication, allowing users to receive the necessary information in real time.

[1230] 7. Real-time feedback output

[1231] The device converts the received feedback into voice messages and provides them to the user through the smart glasses' speakers or earpiece, and also provides vibration feedback if necessary. The content and tone of the feedback are adjusted according to the user's emotional state, providing more appropriate assistance.

[1232] Specific examples

[1233] For example, consider a situation where a user is working near a forklift in a factory. In this case, the device (smart glasses) captures images and acquires location information via GPS. At the same time, the emotion engine analyzes the user's voice tone and heart rate to detect whether the user is in a state of tension. This information is then sent to the server via 5G communication.

[1234] The server identifies the forklift through image analysis and generates instructions such as "A forklift is approaching. Please be careful" in a tone that helps the user regain their composure. The terminal then conveys this information to the user via voice.

[1235] Similarly, if the user is fatigued, the device will acquire images, location information, and emotional data, which will be analyzed by the server. The server will then generate instructions such as "Please take a short rest" in a tone that matches the user's emotional state and notify the user via the device.

[1236] Prompt Sentence Examples

[1237] “YOLOv5: Identify and classify objects in an image of a factory setting.

[1238] Model: TensorFlow emotion model to evaluate emotional state based on input biophysical data.

[1239] Scenario:

[1240] Object detection result: {'dangerous_item': True}

[1241] Emotional state: 'stressed'

[1242] Generate appropriate feedback for the above scenario considering both object detection and emotional state.

[1243] Output example: 'Dangerous object approaching. Be careful. Take a short break to relax.'

[1244] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1245] Step 1:

[1246] The user wears a wearable device (smart glasses), and the camera captures images every second. At the same time, the GPS module acquires location information. The emotion engine analyzes biometric data such as voice tone, facial expression, and heart rate to generate emotion data. The inputs are camera images, location information, and biometric data, and the outputs are image data, location information, and emotion data.

[1247] Step 2:

[1248] The device packetizes the acquired image data, location information, and emotion data and transmits them to the server using 5G communication. The input is packetized data, and the output is packets transmitted via 5G communication. The specific operation involves packetizing the data and transmitting the data according to the communication protocol.

[1249] Step 3:

[1250] The server analyzes the received image data, location information, and emotion data. It uses an image analysis algorithm (YOLOv5) to identify objects in the image (such as dangerous objects or moving equipment) and obtains geographic information based on the location information. It evaluates the user's current emotional state based on the emotion data. The input is the received image data, location information, and emotion data, and the output is the analysis result.

[1251] Step 4:

[1252] Based on the analysis results, the server generates feedback to help the user act safely. When generating feedback using a generative AI model, a predefined prompt sentence is used. The input is the analysis result and the prompt sentence, and the output is a feedback message. Specifically, the server supplies data to the generative AI model and generates appropriate feedback.

[1253] Step 5:

[1254] The server sends the generated feedback to the terminal via 5G communication. The input is the feedback message, and the output is the feedback sent via 5G communication. The specific operation involves packetizing the feedback message and transmitting the data according to the communication protocol.

[1255] Step 6:

[1256] The device converts the received feedback into an audio message and provides it to the user through the smart glasses' speaker or earpiece. It also uses vibration feedback if necessary. The input is the received feedback message, and the output is audio or vibration feedback. Specifically, the device generates audio data and provides feedback through the device.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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).

[1264] 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.

[1265] 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."

[1266] 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.

[1267] 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).

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] The following is further disclosed regarding the above embodiment.

[1279] (Claim 1)

[1280] means for acquiring image data using a camera worn by a user;

[1281] A means for acquiring location information;

[1282] means for transmitting the image data and the position information to a server via a communication network;

[1283] means for analyzing the image data and the location information in the server;

[1284] means for generating feedback to a user based on the analysis results in the server;

[1285] means for transmitting the feedback to the user via the communication network and outputting it as a voice;

[1286] A system including:

[1287] (Claim 2)

[1288] 10. The system of claim 1, wherein the camera is attached to a wearable device.

[1289] (Claim 3)

[1290] 2. The system of claim 1, wherein the communication network is a fifth generation mobile communication system (5G).

[1291] "Example 1"

[1292] (Claim 1)

[1293] means for acquiring image data using a camera worn by a user;

[1294] A means for acquiring location information;

[1295] means for transmitting the image data and the position information to a server via a communication network;

[1296] means for analyzing the image data and the location information in the server;

[1297] A means for generating feedback to a user using a generative AI model based on the analysis results in the server;

[1298] means for transmitting the feedback to the user via the communication network and outputting it as a voice;

[1299] A system including:

[1300] (Claim 2)

[1301] 10. The system of claim 1, wherein the camera is attached to a wearable device.

[1302] (Claim 3)

[1303] 2. The system of claim 1, wherein the communication network is a fifth generation mobile communication system (5G).

[1304] "Application Example 1"

[1305] (Claim 1)

[1306] means for acquiring image data using a camera worn by a user;

[1307] A means for acquiring location information;

[1308] means for transmitting the image data and the position information to a server via a communication network;

[1309] means for analyzing the image data and the location information in the server;

[1310] means for generating feedback to a user based on the analysis results in the server;

[1311] means for transmitting the feedback to the user via the communication network and outputting it as a voice;

[1312] means for identifying a user's current location and surrounding objects to assist in-store navigation;

[1313] means for generating and providing feedback to a user's destination based on the identified object;

[1314] A system including:

[1315] (Claim 2)

[1316] 10. The system of claim 1, wherein the camera is attached to a wearable device.

[1317] (Claim 3)

[1318] 2. The system of claim 1, wherein the communication network is a fifth generation mobile communication system (5G).

[1319] "Example 2: Combining Emotion Engines"

[1320] (Claim 1)

[1321] means for acquiring image data using a camera worn by a user;

[1322] A means for acquiring location information;

[1323] means for detecting emotion data;

[1324] means for transmitting the image data, the position information, and the emotion data to a server via a communication network;

[1325] means for analyzing the image data, the location information, and the emotion data in the server;

[1326] means for generating feedback to a user based on the analysis results in the server;

[1327] means for transmitting the feedback to the user via the communication network and outputting it as a voice;

[1328] A system including:

[1329] (Claim 2)

[1330] 10. The system of claim 1, wherein the camera is attached to a wearable device.

[1331] (Claim 3)

[1332] 2. The system of claim 1, wherein the communication network is a fifth generation mobile communication system (5G).

[1333] "Application example 2 when combining emotion engines"

[1334] (Claim 1)

[1335] means for acquiring image data using a camera worn by a user;

[1336] A means for acquiring location information;

[1337] means for transmitting the image data, the position information, and the emotion data to a server via a communication network;

[1338] means for analyzing the image data, the location information, and the emotion data in the server;

[1339] means for generating feedback to a user based on the analysis results in the server;

[1340] means for transmitting the feedback to the user via the communication network and outputting it as a voice;

[1341] A system including:

[1342] (Claim 2)

[1343] 10. The system of claim 1, wherein the camera is attached to a wearable device and the feedback is provided as an audio message with a tone adjusted according to the user's emotional state.

[1344] (Claim 3)

[1345] 2. The system of claim 1, wherein the communication network is a fifth generation mobile communication system (5G) and the feedback is generated using a generative AI model based on a predefined prompt sentence. [Explanation of symbols]

[1346] 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 image data using a camera worn by a user; A means for acquiring location information; means for transmitting the image data and the position information to a server via a communication network; means for analyzing the image data and the location information in the server; means for generating feedback to a user based on the analysis results in the server; means for transmitting the feedback to the user via the communication network and outputting it as a voice; A system including:

2. The system of claim 1 , wherein the camera is attached to a wearable device.

3. The system according to claim 1, wherein the communication network is a fifth generation mobile communication system.

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