system

Abstract

We provide technology to assist elderly people and visually impaired people with reduced visual and motor skills in their daily mobility and driving. The device includes: a means for acquiring image data from a camera attached to a head-mounted device; a means for transmitting the acquired image data and position data to a server; A system including: a server that analyzes the transmitted image data and location data to recognize the surrounding situation; a server that generates audio data or visual data to notify the user based on the analysis results; and a server that provides the generated data to the user.

Description

[Technical Field]

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

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

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

[0004] There is a need for technology to assist elderly people and visually impaired people with reduced visual and motor skills in their daily travels and driving. However, current high-performance driving assistance systems are expensive and not easily accessible to the general public. Furthermore, these systems require dedicated vehicles and expensive devices, which limits their cost and range of use. Furthermore, it is difficult for visually impaired people and elderly people to receive real-time audio and visual information about the status of simple traffic lights and crosswalks, so there is a need for assistance to travel and drive safely. [Means for solving the problem]

[0005] The present invention provides a system that acquires image data from a camera attached to a head-mounted device and transmits the data to a server via a terminal such as a smartphone. The server analyzes the received image data and location data to recognize the surrounding situation. Based on the results of this analysis, audio or visual feedback can be provided to the user, notifying them of traffic light status, crosswalk status, and other information in real time. This system can assist elderly people and visually impaired people with reduced visual or motor skills to travel and drive safely. Furthermore, since data is sent and received using wireless communication, it offers high convenience while keeping costs down.

[0006] A "head-mounted device" is a device that is worn on the user's head and is equipped with a camera, display, sensors, etc.

[0007] A "camera" is a photographing device for acquiring image data, and in the present invention, is attached to a head-mounted device to capture images of the surroundings in real time.

[0008] "Image data" is digital data containing visual information captured by a camera.

[0009] "Location Data" means data that includes geographic location information obtained using technology such as GPS.

[0010] A "server" is a computer system that receives data sent from terminals on a network and performs analytical processing.

[0011] "Analysis" is the process of detecting objects such as traffic lights and crosswalks based on received image data and location data, and understanding that information.

[0012] "Audio Data" means data that includes digital audio files for audibly communicating information to a user.

[0013] "Visual data" is data that includes images and text intended for display to provide visual information to a user.

[0014] "Notification" refers to the act of conveying information to users based on analysis results.

[0015] "Wireless communication" is a communication method that uses wireless technologies such as Bluetooth and Wi-Fi to send and receive data. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[0038] System Configuration

[0039] head-mounted devices

[0040] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[0041] Smartphones and other devices

[0042] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0043] server

[0044] The server receives and analyzes image and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then converted into audio and visual data and sent back to the device.

[0045] Program processing

[0046] Data collection and transmission

[0047] When a user activates the system, the device acquires image data of the area in front of it from the head-mounted device's camera. The device also uses GPS to collect data on its current location. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0048] Data analysis

[0049] The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel. The analysis results are organized as important information to assist safe travel and driving.

[0050] Information Generation and Notification

[0051] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data. The generated information is sent to the device, which provides it to the user in real time.

[0052] Specific examples

[0053] Traffic light notifications while driving

[0054] 1. A user is wearing a head-mounted device while driving a car.

[0055] 2. The device acquires forward image data and GPS location information from the camera and sends them to the server.

[0056] 3. The server performs image analysis and detects that the traffic light ahead is red.

[0057] 4. The server generates a voice message saying "The traffic light ahead is red" and sends it to the device.

[0058] 5. The device plays audio messages to the user and projects information on a heads-up display if visual data is needed.

[0059] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. Furthermore, by using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[0063] Step 2:

[0064] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[0065] Step 3:

[0066] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[0067] Step 4:

[0068] The device combines the image data and GPS location data acquired from the camera into a single data packet, which also includes a timestamp.

[0069] Step 5:

[0070] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[0071] Step 6:

[0072] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[0073] Step 7:

[0074] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[0075] Step 8:

[0076] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[0077] Step 9:

[0078] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, it generates a voice message saying, "The traffic light ahead is red."

[0079] Step 10:

[0080] The server sends the generated audio and visual data to the device, which is also updated in real time.

[0081] Step 11:

[0082] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[0083] Step 12:

[0084] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[0085] Step 13:

[0086] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[0087] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely.

[0088] Example 1

[0089] 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."

[0090] Conventional mobility and driving assistance systems for the elderly and visually impaired, who have reduced visual and motor skills, have problems with lack of accuracy in recognizing environmental conditions and real-time performance. Furthermore, insufficient information is provided to users, resulting in situations where safety cannot be fully ensured. In particular, the lack of accuracy in recognizing important objects such as traffic lights and pedestrians can potentially hinder safe mobility and driving.

[0091] 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.

[0092] In this invention, the server includes means for analyzing the transmitted image data and location data to recognize the surrounding situation, means for generating audio data or visual data for notifying the user based on the analysis results using a generative AI model, and means for providing the generated data to the user, thereby enabling the user to receive highly accurate information in real time and to travel and drive safely.

[0093] A "head-mounted device" is a device that is worn on the user's head and is equipped with cameras and sensors to acquire visual information about the area in front of them.

[0094] The "camera" is a photographing device for capturing image data in front of the vehicle, and is attached to the head-mounted device.

[0095] A "terminal" is an electronic device that communicates with the head-mounted device and transmits image data and location data to a server.

[0096] "Location data" refers to data indicating the user's current geographical location, and is obtained using a GPS function or the like.

[0097] "Server" means a computer system that analyzes the transmitted image data and location data, and uses a generative AI model to generate audio or visual data based on the analysis results and provide it to the user.

[0098] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to perform image analysis and generate audio data.

[0099] "Voice data" refers to voice information generated to notify the user, and is generated based on the analysis results.

[0100] "Visual data" refers to visual information generated to notify the user and displayed on the display of a head-mounted device.

[0101] A "data packet" is a unit of data for transmitting a group of multiple pieces of information, such as image data and position data.

[0102] "Wireless communication" is a communication method that sends and receives data using wireless technologies such as Bluetooth and Wi-Fi.

[0103] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[0104] System Configuration

[0105] head-mounted devices

[0106] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[0107] Smartphones and other devices

[0108] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0109] server

[0110] The server receives and analyzes image and location data sent from the device. It is equipped with advanced image recognition algorithms and generative AI models (e.g., Tensorflow® and PyTorch) to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then formatted as audio and visual data and sent back to the device.

[0111] Specific examples of programs

[0112] 1. When a user activates the system, the camera on the head-mounted device captures real-time image data of the area in front of the user. At the same time, the GPS function of the device is used to collect data on the user's current location.

[0113] 2. The device converts this data into data packets via a dedicated app and sends them to a server via the Internet.

[0114] 3. The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel.

[0115] 4. The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data.

[0116] 5. A generative AI model (e.g., Google® Text-to-Speech API) is used to convert the analysis results into natural-sounding speech, which is then sent to the device and played back to the user.

[0117] 6. Through voice guidance, users can accurately grasp the situation ahead and travel and drive safely.

[0118] Prompt Sentence Examples

[0119] "Please tell me about a system that enables visually impaired people to move around safely using a head-mounted device."

[0120] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. By using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1:

[0123] When the user activates the system, the camera and various sensors on the head-mounted device are activated. The camera continuously captures image data in front of the user's field of view. The input is real-time image data and position data obtained from the camera and sensors, and the output is these raw data sets.

[0124] Step 2:

[0125] The device acquires forward image data from the head-mounted device's camera and simultaneously acquires current location data from the built-in GPS sensor. These data are converted into data packets by a dedicated app installed on the device. The input is image data from the camera and location data from the GPS, and the output is a data packet.

[0126] Step 3:

[0127] The terminal sends the generated data packets to a server via the Internet. The communication is secure using the HTTPS protocol. The input is the formatted data packet, and the output is the packet data sent to the server.

[0128] Step 4:

[0129] The server analyzes the received data packets. It opens the packets containing image data and location data and performs analysis using image recognition algorithms and generative AI models (such as TensorFlow or PyTorch). The input is the data packets, and the output is the analysis results, which are recognition data for traffic lights, signs, pedestrians, etc.

[0130] Step 5:

[0131] The server generates a voice message and visual information based on the analysis results. It converts the analysis results into speech using a generative AI model (e.g., Google Text-to-Speech API). The input is the recognition data, and the output is a voice message and visual data.

[0132] Step 6:

[0133] The server sends the generated voice message and visual data to the terminal. The input is the generated voice message and visual data, and the output is the information sent to the terminal.

[0134] Step 7:

[0135] The terminal plays the received voice message and projects visual information on the head-up display. The input is the voice message and visual data sent from the server, and the output is real-time assistance information provided to the user.

[0136] Step 8:

[0137] The user moves and drives safely based on the audio guidance and visual information provided by the device. The input is the information provided by the device, and the output is the user's safe behavior.

[0138] In this way, the present invention provides users with highly accurate information in real time, assisting those with impaired visual and motor skills and supporting safe travel and driving.

[0139] (Application example 1)

[0140] 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."

[0141] Conventional systems to support elderly people and visually impaired people with reduced visual or motor skills often fail to ensure sufficient safety when traveling or driving. In particular, they have difficulty recognizing the dynamic surrounding environment (traffic light status, pedestrian movement, the presence of obstacles, etc.) in real time and providing appropriate information to the user. Therefore, new assistance systems that solve these problems are needed so that people who require assistance with vision or motor skills can travel and drive safely and with peace of mind.

[0142] 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.

[0143] In this invention, the server includes a means for detecting traffic lights, pedestrians, and obstacles and generating information to support safe driving, a means for analyzing transmitted image data and position data to recognize the surrounding situation, and a means for generating audio data or visual data to notify the user based on the analysis results, thereby making it possible to provide information necessary for users with impaired vision or motor skills to travel and drive safely in real time.

[0144] A "head-mounted device" is a device that can be used by a user by wearing it on their head, and is equipped with a camera and various sensors.

[0145] A "camera" is a device that acquires image data and is capable of constantly capturing images of a specific area.

[0146] "Image data" is digital data containing visual information obtained from a camera.

[0147] "Location data" is data including location information such as GPS, and indicates the user's current location.

[0148] A "server" is a computer system that receives data over a network and analyzes and processes it.

[0149] "Surroundings" refers to environmental information present around the user, such as traffic lights, signs, pedestrians, and other objects.

[0150] "Analysis" refers to processing the acquired image and location data to extract important information.

[0151] "Audio data" is digital data for notifying the analysis results by voice.

[0152] "Visual data" is digital data that visually notifies the results of analysis.

[0153] A "traffic light" is an optical signaling device installed to control traffic.

[0154] "Pedestrian" refers to a person walking on a road or sidewalk.

[0155] An "obstacle" refers to an object that may impede the user's movement.

[0156] "Wireless communication" is a technology that transmits and receives data via radio waves, infrared rays, etc., without using physical connections such as cables.

[0157] "Information to support safe driving" refers to information that includes instructions and warnings necessary for users with impaired vision or motor skills to drive safely.

[0158] The present invention provides a support system for elderly people and visually impaired people with reduced vision and motor skills to safely move around and drive. This system is composed of a head-mounted device, a terminal, and a server. Specific embodiments of the system are described in detail below.

[0159] System Configuration

[0160] head-mounted devices

[0161] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. The device also incorporates an acceleration sensor and gyroscope to detect the user's movements.

[0162] Terminal

[0163] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time. It also has a built-in GPS module that can determine the current location.

[0164] server

[0165] The server receives and analyzes image data and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, pedestrians, and obstacles. The detected information is converted into audio and visual data and sent back to the device. The server uses software libraries such as TensorFlow and OpenCV for processing.

[0166] How it works

[0167] When a user activates the system, the device acquires image data of the road ahead from the head-mounted device's camera. The device also simultaneously collects current location data using its GPS function. This data is then converted into a single data packet via a dedicated app and sent to a server via the Internet. The server analyzes the received data packet and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the exact location of the detected objects and the user's direction of travel are confirmed. The analysis results are then organized as important information to assist safe travel and driving. The server then generates audio guidance and visual information based on the analysis results. The generated information is sent to the device, which then provides this information to the user in real time. For example, if a traffic light is red, a voice message stating "The traffic light ahead is red" is generated. Furthermore, if a pedestrian is standing at a crosswalk, that information is also notified to the user via audio and visual data.

[0168] Specific examples

[0169] Suppose a user activates this system while in an autonomous vehicle. While driving, a traffic light appears ahead and its status is confirmed by a camera. The server performs image analysis and detects that the traffic light is red. At that time, a voice message stating "The traffic light ahead is red" is notified to the user. A similar notification is also given when a pedestrian approaches a crosswalk. In this way, it is possible to compensate for declines in visual and motor skills and support safe driving.

[0170] Prompt Sentence Examples

[0171] Examples of prompt sentences include:

[0172] "Please create an algorithm that determines the status of traffic lights ahead in real time and notifies visually impaired people by voice."

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] When a user starts the system, the terminal acquires image data from the camera of the head-mounted device. This process captures live video from the camera and generates image data. The input is raw data from the camera and the output is the captured image data.

[0176] Step 2:

[0177] The device uses GPS to obtain location data, which allows the user to determine their current location. The input is a GPS signal, and the output is the current latitude and longitude.

[0178] Step 3:

[0179] The device formats the acquired image data and location data into a single data packet and sends it to a server via the Internet. In this process, the image data and location data are compiled into a format such as JSON and sent to the server using an HTTP POST request. The inputs are the image data and location data, and the output is the sent data packet.

[0180] Step 4:

[0181] The server receives data packets sent from the device and analyzes the image data and location data. First, the server decodes the data packets to separate the image data and location data. Next, the server analyzes the image data using advanced image recognition algorithms and machine learning models (e.g., TensorFlow, OpenCV) to detect traffic lights, signs, pedestrians, obstacles, etc. The input is image data and location data, and the output is the analysis results.

[0182] Step 5:

[0183] The server generates audio or visual data based on the analysis results to support safe driving. For example, if the analysis indicates that the traffic light is red, it generates an audio message saying, "The traffic light ahead is red." The input is the analysis result, and the output is the generated audio or visual data.

[0184] Step 6:

[0185] The server sends the generated audio or visual data to the terminal. In this process, the generated data is sent to the terminal via the Internet. The input is the audio or visual data, and the output is the sent data.

[0186] Step 7:

[0187] The terminal provides the user with audio or visual data received from the server, for example by playing the audio data or displaying the visual data on the display of the head-mounted device. The input is the audio or visual data from the server, and the output is the information provided to the user.

[0188] 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.

[0189] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[0190] System Configuration

[0191] head-mounted devices

[0192] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. In addition, a microphone and heart rate sensor may be added, making it possible to capture the user's voice and biometric data.

[0193] Smartphones and other devices

[0194] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0195] server

[0196] The server receives and analyzes image data, location data, and audio data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice and biometric data to identify emotions. The detected information and emotional state are then formatted as audio or visual data and sent back to the device.

[0197] Program processing

[0198] Data collection and transmission

[0199] When a user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0200] Data analysis

[0201] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[0202] Information Generation and Notification

[0203] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0204] Specific examples

[0205] Traffic light notifications while driving

[0206] 1. A user is wearing a head-mounted device while driving a car.

[0207] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[0208] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[0209] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[0210] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[0211] Through this process, users receive important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[0212] The processing flow will be explained below.

[0213] Step 1:

[0214] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[0215] Step 2:

[0216] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[0217] Step 3:

[0218] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[0219] Step 4:

[0220] The device uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data.

[0221] Step 5:

[0222] The device combines the image data, GPS location data, and audio and biometric data acquired from the camera into a single data packet, which also includes a timestamp.

[0223] Step 6:

[0224] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[0225] Step 7:

[0226] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[0227] Step 8:

[0228] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[0229] Step 9:

[0230] The server passes voice and biometric data to an emotion engine that analyzes voice tone and heart rate fluctuation patterns to identify the user's emotional state.

[0231] Step 10:

[0232] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[0233] Step 11:

[0234] Based on the analysis results and the user's emotional state, the server generates audio guidance and visual information that best suits the user's current state. For example, if the traffic light is red and the server identifies the user as feeling stressed, it generates a message saying, "Remain calm, the light ahead is red."

[0235] Step 12:

[0236] The server sends the generated audio and visual data to the device, which is also updated in real time.

[0237] Step 13:

[0238] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[0239] Step 14:

[0240] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[0241] Step 15:

[0242] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[0243] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely according to their emotional state.

[0244] Example 2

[0245] 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."

[0246] For elderly people and visually impaired people to travel and drive safely, they need not only to accurately grasp their surroundings but also to respond to changes in their own emotions and physical condition. However, conventional systems focus on recognizing the surroundings and do not provide feedback that adapts to the user's emotional and physical states, which does not ensure safety. Therefore, a system that provides adaptive feedback that also takes the user's emotional state into account is needed.

[0247] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and for identifying the user's emotional state, a means for generating voice data or visual data for notifying the user based on the analysis result, and a means for providing the generated data to the user. This allows the user to receive feedback according to not only the surrounding situation but also their own emotional state and physical condition, thereby realizing safe travel and driving.

[0248] A "head-mounted device" is a device worn on the user's head, equipped with a camera and various sensors, for obtaining the user's field of vision and biometric information.

[0249] A "camera" is a photographing device for acquiring image data, which is attached to a head-mounted device and positioned to cover the user's field of vision.

[0250] "Location data" is data indicating the current location of the user, obtained using a location information system such as a GPS.

[0251] "Voice data" refers to data including the user's voice and surrounding sounds acquired using a voice input device such as a microphone.

[0252] "Biometric data" refers to data indicating the user's heart rate and other biometric indicators obtained using a biometric information sensor such as a heart rate sensor.

[0253] A "server" is a computer system that provides the computational resources to analyze data, including captured image data, location data, audio data, and biometric data, to identify the user's situation and emotional state.

[0254] "Analysis" is the process of processing data and extracting meaningful information, and the system uses image recognition algorithms and sentiment analysis engines.

[0255] "Audio data or visual data" is notification data for the user that is generated based on the analysis results obtained, and is data for providing audio guidance or displaying visual information.

[0256] "Wireless communication" refers to technology that transmits data wirelessly using Bluetooth or Wi-Fi, etc.

[0257] A "wearable support" is a structure for fixing a head-mounted device to the user's head, and includes eyeglass frame types, etc.

[0258] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to move and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal, a server linked via the Internet, and the emotion engine.

[0259] head-mounted devices

[0260] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. Additional devices such as a microphone and heart rate sensor can also be added, making it possible to capture the user's voice and biometric data. This allows the user to understand the situation around them in real time.

[0261] Terminal

[0262] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time. Furthermore, the terminal uses the GPS function to acquire current location data and identify the user's movement route.

[0263] server

[0264] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. The server is equipped with advanced image recognition algorithms and machine learning models, such as TensorFlow and PyTorch, to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine uses technologies such as IBM Watson® and Azure® Cognitive Services to analyze the user's voice and biometric data to identify emotions. This is achieved by analyzing voice tone and heart rate fluctuation patterns.

[0265] Information generation and provision

[0266] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0267] Specific examples

[0268] When a user wears a head-mounted device while driving, the device acquires image data of the road ahead from the camera, GPS location information, as well as audio and heart rate information, and sends them to the server. The server performs image analysis and detects that the traffic light ahead is red. The emotion engine also identifies an increase in the user's stress level. The server generates an audio message saying, "Remain calm, the traffic light ahead is red," and sends it to the device. The device plays the audio message to the user and, if necessary, projects the information on the head-up display. Through this process, the user obtains important information about their surroundings in real time, enabling safe travel and driving according to their emotional state.

[0269] Examples of prompt statements

[0270] Here are some example prompts to input to a generative AI model:

[0271] "We have created a program that describes a system that notifies a user while driving that the traffic light ahead is red and that the user is feeling stressed. The program collects and analyzes image data, GPS location information, voice data, and heart rate information, and then provides an appropriate voice message. The processing steps are described in detail."

[0272] This allows the generative AI model to generate a detailed program for the system.

[0273] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0274] Step 1: Initialization

[0275] When the user starts the system, the terminal automatically connects to the head-mounted device via Bluetooth or Wi-Fi.

[0276] An initial data packet of the connection is sent to the server to ensure all sensors and system components are working properly. At this stage, the head-mounted device's camera, microphone, heart rate sensor, etc. are online.

[0277] Input: System startup command

[0278] Output: Device and server connection status, sensor online status

[0279] Step 2: Data collection

[0280] The device begins to acquire real-time image data of the front view from the head-mounted device's camera, which continuously captures images at frames per second (fps).

[0281] The device uses its built-in GPS to acquire location data, which allows the device to determine the user's current location and route.

[0282] The device records the user's voice data through a microphone in the head-mounted device and obtains the user's heart rate data using a heart rate sensor, which is used to analyze the user's stress level and emotional state.

[0283] Input: Camera footage, GPS data, audio data, heart rate data

[0284] Output: Formatted data packet

[0285] Step 3: Send data

[0286] The device uses a dedicated app to packetize the collected image data, location data, voice data, and biometric data, and transmits it to a server via the Internet.

[0287] A security protocol (e.g. HTTPS) is used for transmission and data is encrypted.

[0288] Input: Formatted data packet

[0289] Output: Data transfer completion notification to the server

[0290] Step 4: Data analysis

[0291] The server passes the received data packets to an analysis program, which uses image analysis algorithms to detect important objects such as traffic lights, signs, and pedestrians.

[0292] The server applies machine learning models such as TensorFlow and PyTorch to analyze image and audio data. The audio and biometric data are then analyzed by an emotion engine to identify the user's emotional state from their tone of voice and heart rate fluctuation patterns.

[0293] Input: Data packet sent to the server

[0294] Output: Analysis results (surrounding situation and emotional state)

[0295] Step 5: Information Generation

[0296] Based on the analysis, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, it generates a voice message saying, "The light ahead is red." If the emotion engine identifies an increase in the user's stress level, it creates a message such as, "Calm down, the light ahead is red."

[0297] Input: Analysis results

[0298] Output: Generated audio and visual data

[0299] Step 6: Notification

[0300] The server transmits the generated audio guide and visual information to the terminal.

[0301] The terminal plays the received audio data to the user through a speaker and, if necessary, displays visual information on the display of the head-mounted device.

[0302] Input: Generated audio and visual data

[0303] Output: User notification (audio and visual display)

[0304] This allows users to receive real-time situational awareness information and feedback based on their emotional state, enabling safe travel and driving.

[0305] (Application example 2)

[0306] 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."

[0307] Elderly people and visually impaired people with reduced visual and motor skills have difficulty traveling and driving safely. They also often experience mental strain while traveling and require support tailored to their emotional state. However, current mobility assistance systems lack the ability to recognize the user's emotional state and provide appropriate feedback. Therefore, there is a need for a system that can reduce the user's mental strain and improve the safety of travel.

[0308] 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 analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and the user's emotional state, means for generating voice data or visual data for notifying the user based on the analysis results, and means for generating voice guidance appropriate to the user's emotional state. This enables real-time feedback in line with the user's emotional state, enabling safe and secure travel and driving.

[0309] A "head-mounted device" is a device worn by a user on the head to capture visual data of the area in front of the user and the surrounding environment.

[0310] An "imaging device" is a device for acquiring image data, such as a camera, that is attached to a head-mounted device.

[0311] "Image data" is digital data containing visual information obtained from an imaging device.

[0312] "Location Data" means GPS signals and other location information data used to indicate a user's current location.

[0313] "Audio data" refers to digitized audio signals including user conversation and environmental sounds.

[0314] "Biometric data" refers to data used to measure the user's physical condition, such as heart rate and body temperature.

[0315] A "server" is a central system that communicates with multiple terminals via a network and analyzes and stores data.

[0316] "Analysis" is the act of processing data to recognize a specific situation or emotional state based on the transmitted data.

[0317] "Notification" refers to the act of providing information to the user in audio or visual form based on the analysis results.

[0318] "Audio guidance" refers to verbal instructions and advice provided to the user based on the analysis results.

[0319] "Wireless communication" is a method of sending and receiving data without using cables, using methods such as radio waves, Bluetooth, and Wi-Fi.

[0320] "Emotional state" refers to the user's current psychological state or feelings, including feelings of stress or relief.

[0321] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely. It also combines an emotion engine that recognizes the user's emotional state and provides adaptive feedback accordingly. This system is composed of an imaging device attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[0322] System configuration:

[0323] head-mounted device

[0324] Head-mounted devices are equipped with cameras and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the device. In addition, a microphone and heart rate sensor can be added, making it possible to capture the user's voice data and biometric data.

[0325] Smartphones and other devices

[0326] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0327] server

[0328] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice data and biometric data to identify emotions. The detected information and emotional state are converted into voice data or visual data and sent back to the device.

[0329] Program processing

[0330] Data collection and transmission

[0331] When the user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0332] Data analysis

[0333] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[0334] Information Generation and Notification

[0335] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0336] Specific examples

[0337] Traffic light notifications while driving

[0338] 1. A user wears a head-mounted device while driving a car.

[0339] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[0340] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[0341] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[0342] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[0343] This allows users to obtain important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[0344] Prompt sentences for generative AI models (examples)

[0345] Audio data preprocessing:

[0346] python

[0347] audio_input = preprocess_audio(audio_data)

[0348] emotion_prediction = emotion_model.predict(audio_input)

[0349] This provides users with an environment in which they can move around comfortably and safely.

[0350] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0351] Step 1: System startup and data collection

[0352] The user starts the system. The device acquires image data of the area in front of the vehicle from a camera mounted on a head-mounted device. The input data includes image data, audio data, location data, and heart rate data. This data is then collected by the device. The device then collects information on the area in front of the vehicle, the sound environment, location information, and the user's biometric information all at once.

[0353] Step 2: Organize and send data

[0354] The device assembles the collected data into a single data packet, which includes image data of the road ahead, current location information, voice data, and heart rate data. The assembled data packet is then sent to a server via the Internet. The device is responsible for aggregating and transmitting the data.

[0355] Step 3: Analyzing the image data

[0356] The server separates and decodes the image data from the received data packets. It has data packets as input and decoded image data as output. The server's image analysis algorithms detect important objects such as traffic lights, signs, and pedestrians. This analysis process uses machine learning models and image recognition algorithms.

[0357] Step 4: Analyzing audio and biometric data

[0358] The server extracts voice and heart rate data from the data packets. The voice data is analyzed by an emotion engine to identify the user's emotional state. The inputs are voice data and biometric data, and the output is the analyzed emotional state. The tone of voice and heart rate fluctuation patterns are analyzed. A generative AI model is used for this process.

[0359] Step 5: Generate notification data

[0360] The server generates information to be notified to the user based on the results of image analysis and emotion analysis. The analysis results are input, and audio or visual data for notification is generated as output. For example, if the traffic light is red and the user is feeling stressed, an audio message such as "Calm down, the light ahead is red" is generated.

[0361] Step 6: Submit and provide data

[0362] The generated notification data is sent from the server to the device. The device provides this notification data to the user in real time. The generated notification data is input, and audio messages and visual information are provided to the user as output. Information is displayed on the audio output and display of the head-mounted device.

[0363] This allows the user to accurately grasp the situation and receive optimal feedback tailored to their emotional state.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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."

[0380] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[0381] System Configuration

[0382] head-mounted devices

[0383] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[0384] Smartphones and other devices

[0385] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0386] server

[0387] The server receives and analyzes image and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then converted into audio and visual data and sent back to the device.

[0388] Program processing

[0389] Data collection and transmission

[0390] When a user activates the system, the device acquires image data of the area in front of it from the head-mounted device's camera. The device also uses GPS to collect data on its current location. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0391] Data analysis

[0392] The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel. The analysis results are organized as important information to assist safe travel and driving.

[0393] Information Generation and Notification

[0394] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data. The generated information is sent to the device, which provides it to the user in real time.

[0395] Specific examples

[0396] Traffic light notifications while driving

[0397] 1. A user is wearing a head-mounted device while driving a car.

[0398] 2. The device acquires forward image data and GPS location information from the camera and sends them to the server.

[0399] 3. The server performs image analysis and detects that the traffic light ahead is red.

[0400] 4. The server generates a voice message saying "The traffic light ahead is red" and sends it to the device.

[0401] 5. The device plays audio messages to the user and projects information on a heads-up display if visual data is needed.

[0402] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. Furthermore, by using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[0403] The processing flow will be explained below.

[0404] Step 1:

[0405] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[0406] Step 2:

[0407] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[0408] Step 3:

[0409] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[0410] Step 4:

[0411] The device combines the image data and GPS location data acquired from the camera into a single data packet, which also includes a timestamp.

[0412] Step 5:

[0413] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[0414] Step 6:

[0415] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[0416] Step 7:

[0417] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[0418] Step 8:

[0419] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[0420] Step 9:

[0421] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, it generates a voice message saying, "The traffic light ahead is red."

[0422] Step 10:

[0423] The server sends the generated audio and visual data to the device, which is also updated in real time.

[0424] Step 11:

[0425] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[0426] Step 12:

[0427] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[0428] Step 13:

[0429] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[0430] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely.

[0431] Example 1

[0432] 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."

[0433] Conventional mobility and driving assistance systems for the elderly and visually impaired, who have reduced visual and motor skills, have problems with lack of accuracy in recognizing environmental conditions and real-time performance. Furthermore, insufficient information is provided to users, resulting in situations where safety cannot be fully ensured. In particular, the lack of accuracy in recognizing important objects such as traffic lights and pedestrians can potentially hinder safe mobility and driving.

[0434] 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.

[0435] In this invention, the server includes means for analyzing the transmitted image data and location data to recognize the surrounding situation, means for generating audio data or visual data for notifying the user based on the analysis results using a generative AI model, and means for providing the generated data to the user, thereby enabling the user to receive highly accurate information in real time and to travel and drive safely.

[0436] A "head-mounted device" is a device that is worn on the user's head and is equipped with cameras and sensors to acquire visual information about the area in front of them.

[0437] The "camera" is a photographing device for capturing image data in front of the vehicle, and is attached to the head-mounted device.

[0438] A "terminal" is an electronic device that communicates with the head-mounted device and transmits image data and location data to a server.

[0439] "Location data" refers to data indicating the user's current geographical location, and is obtained using a GPS function or the like.

[0440] "Server" means a computer system that analyzes the transmitted image data and location data, and uses a generative AI model to generate audio or visual data based on the analysis results and provide it to the user.

[0441] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to perform image analysis and generate audio data.

[0442] "Voice data" refers to voice information generated to notify the user, and is generated based on the analysis results.

[0443] "Visual data" refers to visual information generated to notify the user and displayed on the display of a head-mounted device.

[0444] A "data packet" is a unit of data for transmitting a group of multiple pieces of information, such as image data and position data.

[0445] "Wireless communication" is a communication method that sends and receives data using wireless technologies such as Bluetooth and Wi-Fi.

[0446] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[0447] System Configuration

[0448] head-mounted devices

[0449] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[0450] Smartphones and other devices

[0451] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0452] server

[0453] The server receives and analyzes image and location data sent from the device. It uses advanced image recognition algorithms and generative AI models (such as TensorFlow and PyTorch) to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then formatted as audio and visual data and sent back to the device.

[0454] Specific examples of programs

[0455] 1. When a user activates the system, the camera on the head-mounted device captures real-time image data of the area in front of the user. At the same time, the GPS function of the device is used to collect data on the user's current location.

[0456] 2. The device converts this data into data packets via a dedicated app and sends them to a server via the Internet.

[0457] 3. The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel.

[0458] 4. The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data.

[0459] 5. A generative AI model (e.g., Google Text-to-Speech API) is used to convert the analysis results into natural-sounding speech, which is then sent to the device and played back to the user.

[0460] 6. Through voice guidance, users can accurately grasp the situation ahead and travel and drive safely.

[0461] Prompt Sentence Examples

[0462] "Please tell me about a system that enables visually impaired people to move around safely using a head-mounted device."

[0463] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. By using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[0464] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0465] Step 1:

[0466] When the user activates the system, the camera and various sensors on the head-mounted device are activated. The camera continuously captures image data in front of the user's field of view. The input is real-time image data and position data obtained from the camera and sensors, and the output is these raw data sets.

[0467] Step 2:

[0468] The device acquires forward image data from the head-mounted device's camera and simultaneously acquires current location data from the built-in GPS sensor. These data are converted into data packets by a dedicated app installed on the device. The input is image data from the camera and location data from the GPS, and the output is a data packet.

[0469] Step 3:

[0470] The terminal sends the generated data packets to a server via the Internet. The communication is secure using the HTTPS protocol. The input is the formatted data packet, and the output is the packet data sent to the server.

[0471] Step 4:

[0472] The server analyzes the received data packets. It opens the packets containing image data and location data and performs analysis using image recognition algorithms and generative AI models (such as TensorFlow or PyTorch). The input is the data packets, and the output is the analysis results, which are recognition data for traffic lights, signs, pedestrians, etc.

[0473] Step 5:

[0474] The server generates a voice message and visual information based on the analysis results. It converts the analysis results into speech using a generative AI model (e.g., Google Text-to-Speech API). The input is the recognition data, and the output is a voice message and visual data.

[0475] Step 6:

[0476] The server sends the generated voice message and visual data to the terminal. The input is the generated voice message and visual data, and the output is the information sent to the terminal.

[0477] Step 7:

[0478] The terminal plays the received voice message and projects visual information on the head-up display. The input is the voice message and visual data sent from the server, and the output is real-time assistance information provided to the user.

[0479] Step 8:

[0480] The user moves and drives safely based on the audio guidance and visual information provided by the device. The input is the information provided by the device, and the output is the user's safe behavior.

[0481] In this way, the present invention provides users with highly accurate information in real time, assisting those with impaired visual and motor skills and supporting safe travel and driving.

[0482] (Application example 1)

[0483] 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."

[0484] Conventional systems to support elderly people and visually impaired people with reduced visual or motor skills often fail to ensure sufficient safety when traveling or driving. In particular, they have difficulty recognizing the dynamic surrounding environment (traffic light status, pedestrian movement, the presence of obstacles, etc.) in real time and providing appropriate information to the user. Therefore, new assistance systems that solve these problems are needed so that people who require assistance with vision or motor skills can travel and drive safely and with peace of mind.

[0485] 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.

[0486] In this invention, the server includes a means for detecting traffic lights, pedestrians, and obstacles and generating information to support safe driving, a means for analyzing transmitted image data and position data to recognize the surrounding situation, and a means for generating audio data or visual data to notify the user based on the analysis results, thereby making it possible to provide information necessary for users with impaired vision or motor skills to travel and drive safely in real time.

[0487] A "head-mounted device" is a device that can be used by a user by wearing it on their head, and is equipped with a camera and various sensors.

[0488] A "camera" is a device that acquires image data and is capable of constantly capturing images of a specific area.

[0489] "Image data" is digital data containing visual information obtained from a camera.

[0490] "Location data" is data including location information such as GPS, and indicates the user's current location.

[0491] A "server" is a computer system that receives data over a network and analyzes and processes it.

[0492] "Surroundings" refers to environmental information present around the user, such as traffic lights, signs, pedestrians, and other objects.

[0493] "Analysis" refers to processing the acquired image and location data to extract important information.

[0494] "Audio data" is digital data for notifying the analysis results by voice.

[0495] "Visual data" is digital data that visually notifies the results of analysis.

[0496] A "traffic light" is an optical signaling device installed to control traffic.

[0497] "Pedestrian" refers to a person walking on a road or sidewalk.

[0498] An "obstacle" refers to an object that may impede the user's movement.

[0499] "Wireless communication" is a technology that transmits and receives data via radio waves, infrared rays, etc., without using physical connections such as cables.

[0500] "Information to support safe driving" refers to information that includes instructions and warnings necessary for users with impaired vision or motor skills to drive safely.

[0501] The present invention provides a support system for elderly people and visually impaired people with reduced vision and motor skills to safely move around and drive. This system is composed of a head-mounted device, a terminal, and a server. Specific embodiments of the system are described in detail below.

[0502] System Configuration

[0503] head-mounted devices

[0504] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. The device also incorporates an acceleration sensor and gyroscope to detect the user's movements.

[0505] Terminal

[0506] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time. It also has a built-in GPS module that can determine the current location.

[0507] server

[0508] The server receives and analyzes image data and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, pedestrians, and obstacles. The detected information is converted into audio and visual data and sent back to the device. The server uses software libraries such as TensorFlow and OpenCV for processing.

[0509] How it works

[0510] When a user activates the system, the device acquires image data of the road ahead from the head-mounted device's camera. The device also simultaneously collects current location data using its GPS function. This data is then converted into a single data packet via a dedicated app and sent to a server via the Internet. The server analyzes the received data packet and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the exact location of the detected objects and the user's direction of travel are confirmed. The analysis results are then organized as important information to assist safe travel and driving. The server then generates audio guidance and visual information based on the analysis results. The generated information is sent to the device, which then provides this information to the user in real time. For example, if a traffic light is red, a voice message stating "The traffic light ahead is red" is generated. Furthermore, if a pedestrian is standing at a crosswalk, that information is also notified to the user via audio and visual data.

[0511] Specific examples

[0512] Suppose a user activates this system while in an autonomous vehicle. While driving, a traffic light appears ahead and its status is confirmed by a camera. The server performs image analysis and detects that the traffic light is red. At that time, a voice message stating "The traffic light ahead is red" is notified to the user. A similar notification is also given when a pedestrian approaches a crosswalk. In this way, it is possible to compensate for declines in visual and motor skills and support safe driving.

[0513] Prompt Sentence Examples

[0514] Examples of prompt sentences include:

[0515] "Please create an algorithm that determines the status of traffic lights ahead in real time and notifies visually impaired people by voice."

[0516] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0517] Step 1:

[0518] When a user starts the system, the terminal acquires image data from the camera of the head-mounted device. This process captures live video from the camera and generates image data. The input is raw data from the camera and the output is the captured image data.

[0519] Step 2:

[0520] The device uses GPS to obtain location data, which allows the user to determine their current location. The input is a GPS signal, and the output is the current latitude and longitude.

[0521] Step 3:

[0522] The device formats the acquired image data and location data into a single data packet and sends it to a server via the Internet. In this process, the image data and location data are compiled into a format such as JSON and sent to the server using an HTTP POST request. The inputs are the image data and location data, and the output is the sent data packet.

[0523] Step 4:

[0524] The server receives data packets sent from the device and analyzes the image data and location data. First, the server decodes the data packets to separate the image data and location data. Next, the server analyzes the image data using advanced image recognition algorithms and machine learning models (e.g., TensorFlow, OpenCV) to detect traffic lights, signs, pedestrians, obstacles, etc. The input is image data and location data, and the output is the analysis results.

[0525] Step 5:

[0526] The server generates audio or visual data based on the analysis results to support safe driving. For example, if the analysis indicates that the traffic light is red, it generates an audio message saying, "The traffic light ahead is red." The input is the analysis result, and the output is the generated audio or visual data.

[0527] Step 6:

[0528] The server sends the generated audio or visual data to the terminal. In this process, the generated data is sent to the terminal via the Internet. The input is the audio or visual data, and the output is the sent data.

[0529] Step 7:

[0530] The terminal provides the user with audio or visual data received from the server, for example by playing the audio data or displaying the visual data on the display of the head-mounted device. The input is the audio or visual data from the server, and the output is the information provided to the user.

[0531] 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.

[0532] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[0533] System Configuration

[0534] head-mounted devices

[0535] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. In addition, a microphone and heart rate sensor may be added, making it possible to capture the user's voice and biometric data.

[0536] Smartphones and other devices

[0537] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0538] server

[0539] The server receives and analyzes image data, location data, and audio data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice and biometric data to identify emotions. The detected information and emotional state are then formatted as audio or visual data and sent back to the device.

[0540] Program processing

[0541] Data collection and transmission

[0542] When a user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0543] Data analysis

[0544] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[0545] Information Generation and Notification

[0546] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0547] Specific examples

[0548] Traffic light notifications while driving

[0549] 1. A user is wearing a head-mounted device while driving a car.

[0550] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[0551] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[0552] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[0553] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[0554] Through this process, users receive important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[0555] The processing flow will be explained below.

[0556] Step 1:

[0557] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[0558] Step 2:

[0559] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[0560] Step 3:

[0561] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[0562] Step 4:

[0563] The device uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data.

[0564] Step 5:

[0565] The device combines the image data, GPS location data, and audio and biometric data acquired from the camera into a single data packet, which also includes a timestamp.

[0566] Step 6:

[0567] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[0568] Step 7:

[0569] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[0570] Step 8:

[0571] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[0572] Step 9:

[0573] The server passes voice and biometric data to an emotion engine that analyzes voice tone and heart rate fluctuation patterns to identify the user's emotional state.

[0574] Step 10:

[0575] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[0576] Step 11:

[0577] Based on the analysis results and the user's emotional state, the server generates audio guidance and visual information that best suits the user's current state. For example, if the traffic light is red and the server identifies the user as feeling stressed, it generates a message saying, "Remain calm, the light ahead is red."

[0578] Step 12:

[0579] The server sends the generated audio and visual data to the device, which is also updated in real time.

[0580] Step 13:

[0581] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[0582] Step 14:

[0583] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[0584] Step 15:

[0585] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[0586] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely according to their emotional state.

[0587] Example 2

[0588] 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."

[0589] For elderly people and visually impaired people to travel and drive safely, they need not only to accurately grasp their surroundings but also to respond to changes in their own emotions and physical condition. However, conventional systems focus on recognizing the surroundings and do not provide feedback that adapts to the user's emotional and physical states, which does not ensure safety. Therefore, a system that provides adaptive feedback that also takes the user's emotional state into account is needed.

[0590] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and for identifying the user's emotional state, a means for generating voice data or visual data for notifying the user based on the analysis result, and a means for providing the generated data to the user. This allows the user to receive feedback according to not only the surrounding situation but also their own emotional state and physical condition, thereby realizing safe travel and driving.

[0591] A "head-mounted device" is a device worn on the user's head, equipped with a camera and various sensors, for obtaining the user's field of vision and biometric information.

[0592] A "camera" is a photographing device for acquiring image data, which is attached to a head-mounted device and positioned to cover the user's field of vision.

[0593] "Location data" is data indicating the current location of the user, obtained using a location information system such as a GPS.

[0594] "Voice data" refers to data including the user's voice and surrounding sounds acquired using a voice input device such as a microphone.

[0595] "Biometric data" refers to data indicating the user's heart rate and other biometric indicators obtained using a biometric information sensor such as a heart rate sensor.

[0596] A "server" is a computer system that provides the computational resources to analyze data, including captured image data, location data, audio data, and biometric data, to identify the user's situation and emotional state.

[0597] "Analysis" is the process of processing data and extracting meaningful information, and the system uses image recognition algorithms and sentiment analysis engines.

[0598] "Audio data or visual data" is notification data for the user that is generated based on the analysis results obtained, and is data for providing audio guidance or displaying visual information.

[0599] "Wireless communication" refers to technology that transmits data wirelessly using Bluetooth or Wi-Fi, etc.

[0600] A "wearable support" is a structure for fixing a head-mounted device to the user's head, and includes eyeglass frame types, etc.

[0601] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to move and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal, a server linked via the Internet, and the emotion engine.

[0602] head-mounted devices

[0603] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. Additional devices such as a microphone and heart rate sensor can also be added, making it possible to capture the user's voice and biometric data. This allows the user to understand the situation around them in real time.

[0604] Terminal

[0605] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time. Furthermore, the terminal uses the GPS function to acquire current location data and identify the user's movement route.

[0606] server

[0607] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. The server is equipped with advanced image recognition algorithms and machine learning models, such as TensorFlow and PyTorch, to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine uses technologies such as IBM Watson and Azure Cognitive Services to analyze the user's voice and biometric data to identify emotions. This is achieved by analyzing voice tone and heart rate fluctuation patterns.

[0608] Information generation and provision

[0609] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0610] Specific examples

[0611] When a user wears a head-mounted device while driving, the device acquires image data of the road ahead from the camera, GPS location information, as well as audio and heart rate information, and sends them to the server. The server performs image analysis and detects that the traffic light ahead is red. The emotion engine also identifies an increase in the user's stress level. The server generates an audio message saying, "Remain calm, the traffic light ahead is red," and sends it to the device. The device plays the audio message to the user and, if necessary, projects the information on the head-up display. Through this process, the user obtains important information about their surroundings in real time, enabling safe travel and driving according to their emotional state.

[0612] Examples of prompt statements

[0613] Here are some example prompts to input to a generative AI model:

[0614] "We have created a program that describes a system that notifies a user while driving that the traffic light ahead is red and that the user is feeling stressed. The program collects and analyzes image data, GPS location information, voice data, and heart rate information, and then provides an appropriate voice message. The processing steps are described in detail."

[0615] This allows the generative AI model to generate a detailed program for the system.

[0616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0617] Step 1: Initialization

[0618] When the user starts the system, the terminal automatically connects to the head-mounted device via Bluetooth or Wi-Fi.

[0619] An initial data packet of the connection is sent to the server to ensure all sensors and system components are working properly. At this stage, the head-mounted device's camera, microphone, heart rate sensor, etc. are online.

[0620] Input: System startup command

[0621] Output: Device and server connection status, sensor online status

[0622] Step 2: Data collection

[0623] The device begins to acquire real-time image data of the front view from the head-mounted device's camera, which continuously captures images at frames per second (fps).

[0624] The device uses its built-in GPS to acquire location data, which allows the device to determine the user's current location and route.

[0625] The device records the user's voice data through a microphone in the head-mounted device and obtains the user's heart rate data using a heart rate sensor, which is used to analyze the user's stress level and emotional state.

[0626] Input: Camera footage, GPS data, audio data, heart rate data

[0627] Output: Formatted data packet

[0628] Step 3: Send data

[0629] The device uses a dedicated app to packetize the collected image data, location data, voice data, and biometric data, and transmits it to a server via the Internet.

[0630] A security protocol (e.g. HTTPS) is used for transmission and data is encrypted.

[0631] Input: Formatted data packet

[0632] Output: Data transfer completion notification to the server

[0633] Step 4: Data analysis

[0634] The server passes the received data packets to an analysis program, which uses image analysis algorithms to detect important objects such as traffic lights, signs, and pedestrians.

[0635] The server applies machine learning models such as TensorFlow and PyTorch to analyze image and audio data. The audio and biometric data are then analyzed by an emotion engine to identify the user's emotional state from their tone of voice and heart rate fluctuation patterns.

[0636] Input: Data packet sent to the server

[0637] Output: Analysis results (surrounding situation and emotional state)

[0638] Step 5: Information Generation

[0639] Based on the analysis, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, it generates a voice message saying, "The light ahead is red." If the emotion engine identifies an increase in the user's stress level, it creates a message such as, "Calm down, the light ahead is red."

[0640] Input: Analysis results

[0641] Output: Generated audio and visual data

[0642] Step 6: Notification

[0643] The server transmits the generated audio guide and visual information to the terminal.

[0644] The terminal plays the received audio data to the user through a speaker and, if necessary, displays visual information on the display of the head-mounted device.

[0645] Input: Generated audio and visual data

[0646] Output: User notification (audio and visual display)

[0647] This allows users to receive real-time situational awareness information and feedback based on their emotional state, enabling safe travel and driving.

[0648] (Application example 2)

[0649] 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."

[0650] Elderly people and visually impaired people with reduced visual and motor skills have difficulty traveling and driving safely. They also often experience mental strain while traveling and require support tailored to their emotional state. However, current mobility assistance systems lack the ability to recognize the user's emotional state and provide appropriate feedback. Therefore, there is a need for a system that can reduce the user's mental strain and improve the safety of travel.

[0651] 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 analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and the user's emotional state, means for generating voice data or visual data for notifying the user based on the analysis results, and means for generating voice guidance appropriate to the user's emotional state. This enables real-time feedback in line with the user's emotional state, enabling safe and secure travel and driving.

[0652] A "head-mounted device" is a device worn by a user on the head to capture visual data of the area in front of the user and the surrounding environment.

[0653] An "imaging device" is a device for acquiring image data, such as a camera, that is attached to a head-mounted device.

[0654] "Image data" is digital data containing visual information obtained from an imaging device.

[0655] "Location Data" means GPS signals and other location information data used to indicate a user's current location.

[0656] "Audio data" refers to digitized audio signals including user conversation and environmental sounds.

[0657] "Biometric data" refers to data used to measure the user's physical condition, such as heart rate and body temperature.

[0658] A "server" is a central system that communicates with multiple terminals via a network and analyzes and stores data.

[0659] "Analysis" is the act of processing data to recognize a specific situation or emotional state based on the transmitted data.

[0660] "Notification" refers to the act of providing information to the user in audio or visual form based on the analysis results.

[0661] "Audio guidance" refers to verbal instructions and advice provided to the user based on the analysis results.

[0662] "Wireless communication" is a method of sending and receiving data without using cables, using methods such as radio waves, Bluetooth, and Wi-Fi.

[0663] "Emotional state" refers to the user's current psychological state or feelings, including feelings of stress or relief.

[0664] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely. It also combines an emotion engine that recognizes the user's emotional state and provides adaptive feedback accordingly. This system is composed of an imaging device attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[0665] System configuration:

[0666] head-mounted device

[0667] Head-mounted devices are equipped with cameras and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the device. In addition, a microphone and heart rate sensor can be added, making it possible to capture the user's voice data and biometric data.

[0668] Smartphones and other devices

[0669] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0670] server

[0671] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice data and biometric data to identify emotions. The detected information and emotional state are converted into voice data or visual data and sent back to the device.

[0672] Program processing

[0673] Data collection and transmission

[0674] When the user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0675] Data analysis

[0676] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[0677] Information Generation and Notification

[0678] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0679] Specific examples

[0680] Traffic light notifications while driving

[0681] 1. A user wears a head-mounted device while driving a car.

[0682] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[0683] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[0684] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[0685] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[0686] This allows users to obtain important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[0687] Prompt sentences for generative AI models (examples)

[0688] Audio data preprocessing:

[0689] python

[0690] audio_input = preprocess_audio(audio_data)

[0691] emotion_prediction = emotion_model.predict(audio_input)

[0692] This provides users with an environment in which they can move around comfortably and safely.

[0693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0694] Step 1: System startup and data collection

[0695] The user starts the system. The device acquires image data of the area in front of the vehicle from a camera mounted on a head-mounted device. The input data includes image data, audio data, location data, and heart rate data. This data is then collected by the device. The device then collects information on the area in front of the vehicle, the sound environment, location information, and the user's biometric information all at once.

[0696] Step 2: Organize and send data

[0697] The device assembles the collected data into a single data packet, which includes image data of the road ahead, current location information, voice data, and heart rate data. The assembled data packet is then sent to a server via the Internet. The device is responsible for aggregating and transmitting the data.

[0698] Step 3: Analyzing the image data

[0699] The server separates and decodes the image data from the received data packets. It has data packets as input and decoded image data as output. The server's image analysis algorithms detect important objects such as traffic lights, signs, and pedestrians. This analysis process uses machine learning models and image recognition algorithms.

[0700] Step 4: Analyzing audio and biometric data

[0701] The server extracts voice and heart rate data from the data packets. The voice data is analyzed by an emotion engine to identify the user's emotional state. The inputs are voice data and biometric data, and the output is the analyzed emotional state. The tone of voice and heart rate fluctuation patterns are analyzed. A generative AI model is used for this process.

[0702] Step 5: Generate notification data

[0703] The server generates information to be notified to the user based on the results of image analysis and emotion analysis. The analysis results are input, and audio or visual data for notification is generated as output. For example, if the traffic light is red and the user is feeling stressed, an audio message such as "Calm down, the light ahead is red" is generated.

[0704] Step 6: Submit and provide data

[0705] The generated notification data is sent from the server to the device. The device provides this notification data to the user in real time. The generated notification data is input, and audio messages and visual information are provided to the user as output. Information is displayed on the audio output and display of the head-mounted device.

[0706] This allows the user to accurately grasp the situation and receive optimal feedback tailored to their emotional state.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] [Third embodiment]

[0711] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0712] 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.

[0713] 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).

[0714] 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.

[0715] 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.

[0716] 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).

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[0724] System Configuration

[0725] head-mounted devices

[0726] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[0727] Smartphones and other devices

[0728] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0729] server

[0730] The server receives and analyzes image and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then converted into audio and visual data and sent back to the device.

[0731] Program processing

[0732] Data collection and transmission

[0733] When a user activates the system, the device acquires image data of the area in front of it from the head-mounted device's camera. The device also uses GPS to collect data on its current location. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0734] Data analysis

[0735] The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel. The analysis results are organized as important information to assist safe travel and driving.

[0736] Information Generation and Notification

[0737] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data. The generated information is sent to the device, which provides it to the user in real time.

[0738] Specific examples

[0739] Traffic light notifications while driving

[0740] 1. A user is wearing a head-mounted device while driving a car.

[0741] 2. The device acquires forward image data and GPS location information from the camera and sends them to the server.

[0742] 3. The server performs image analysis and detects that the traffic light ahead is red.

[0743] 4. The server generates a voice message saying "The traffic light ahead is red" and sends it to the device.

[0744] 5. The device plays audio messages to the user and projects information on a heads-up display if visual data is needed.

[0745] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. Furthermore, by using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[0746] The processing flow will be explained below.

[0747] Step 1:

[0748] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[0749] Step 2:

[0750] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[0751] Step 3:

[0752] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[0753] Step 4:

[0754] The device combines the image data and GPS location data acquired from the camera into a single data packet, which also includes a timestamp.

[0755] Step 5:

[0756] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[0757] Step 6:

[0758] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[0759] Step 7:

[0760] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[0761] Step 8:

[0762] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[0763] Step 9:

[0764] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, it generates a voice message saying, "The traffic light ahead is red."

[0765] Step 10:

[0766] The server sends the generated audio and visual data to the device, which is also updated in real time.

[0767] Step 11:

[0768] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[0769] Step 12:

[0770] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[0771] Step 13:

[0772] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[0773] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely.

[0774] Example 1

[0775] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0776] Conventional mobility and driving assistance systems for the elderly and visually impaired, who have reduced visual and motor skills, have problems with lack of accuracy in recognizing environmental conditions and real-time performance. Furthermore, insufficient information is provided to users, resulting in situations where safety cannot be fully ensured. In particular, the lack of accuracy in recognizing important objects such as traffic lights and pedestrians can potentially hinder safe mobility and driving.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0778] In this invention, the server includes means for analyzing the transmitted image data and location data to recognize the surrounding situation, means for generating audio data or visual data for notifying the user based on the analysis results using a generative AI model, and means for providing the generated data to the user, thereby enabling the user to receive highly accurate information in real time and to travel and drive safely.

[0779] A "head-mounted device" is a device that is worn on the user's head and is equipped with cameras and sensors to acquire visual information about the area in front of them.

[0780] The "camera" is a photographing device for capturing image data in front of the vehicle, and is attached to the head-mounted device.

[0781] A "terminal" is an electronic device that communicates with the head-mounted device and transmits image data and location data to a server.

[0782] "Location data" refers to data indicating the user's current geographical location, and is obtained using a GPS function or the like.

[0783] "Server" means a computer system that analyzes the transmitted image data and location data, and uses a generative AI model to generate audio or visual data based on the analysis results and provide it to the user.

[0784] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to perform image analysis and generate audio data.

[0785] "Voice data" refers to voice information generated to notify the user, and is generated based on the analysis results.

[0786] "Visual data" refers to visual information generated to notify the user and displayed on the display of a head-mounted device.

[0787] A "data packet" is a unit of data for transmitting a group of multiple pieces of information, such as image data and position data.

[0788] "Wireless communication" is a communication method that sends and receives data using wireless technologies such as Bluetooth and Wi-Fi.

[0789] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[0790] System Configuration

[0791] head-mounted devices

[0792] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[0793] Smartphones and other devices

[0794] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0795] server

[0796] The server receives and analyzes image and location data sent from the device. It uses advanced image recognition algorithms and generative AI models (such as TensorFlow and PyTorch) to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then formatted as audio and visual data and sent back to the device.

[0797] Specific examples of programs

[0798] 1. When a user activates the system, the camera on the head-mounted device captures real-time image data of the area in front of the user. At the same time, the GPS function of the device is used to collect data on the user's current location.

[0799] 2. The device converts this data into data packets via a dedicated app and sends them to a server via the Internet.

[0800] 3. The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel.

[0801] 4. The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data.

[0802] 5. A generative AI model (e.g., Google Text-to-Speech API) is used to convert the analysis results into natural-sounding speech, which is then sent to the device and played back to the user.

[0803] 6. Through voice guidance, users can accurately grasp the situation ahead and travel and drive safely.

[0804] Prompt Sentence Examples

[0805] "Please tell me about a system that enables visually impaired people to move around safely using a head-mounted device."

[0806] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. By using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[0807] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0808] Step 1:

[0809] When the user activates the system, the camera and various sensors on the head-mounted device are activated. The camera continuously captures image data in front of the user's field of view. The input is real-time image data and position data obtained from the camera and sensors, and the output is these raw data sets.

[0810] Step 2:

[0811] The device acquires forward image data from the head-mounted device's camera and simultaneously acquires current location data from the built-in GPS sensor. These data are converted into data packets by a dedicated app installed on the device. The input is image data from the camera and location data from the GPS, and the output is a data packet.

[0812] Step 3:

[0813] The terminal sends the generated data packets to a server via the Internet. The communication is secure using the HTTPS protocol. The input is the formatted data packet, and the output is the packet data sent to the server.

[0814] Step 4:

[0815] The server analyzes the received data packets. It opens the packets containing image data and location data and performs analysis using image recognition algorithms and generative AI models (such as TensorFlow or PyTorch). The input is the data packets, and the output is the analysis results, which are recognition data for traffic lights, signs, pedestrians, etc.

[0816] Step 5:

[0817] The server generates a voice message and visual information based on the analysis results. It converts the analysis results into speech using a generative AI model (e.g., Google Text-to-Speech API). The input is the recognition data, and the output is a voice message and visual data.

[0818] Step 6:

[0819] The server sends the generated voice message and visual data to the terminal. The input is the generated voice message and visual data, and the output is the information sent to the terminal.

[0820] Step 7:

[0821] The terminal plays the received voice message and projects visual information on the head-up display. The input is the voice message and visual data sent from the server, and the output is real-time assistance information provided to the user.

[0822] Step 8:

[0823] The user moves and drives safely based on the audio guidance and visual information provided by the device. The input is the information provided by the device, and the output is the user's safe behavior.

[0824] In this way, the present invention provides users with highly accurate information in real time, assisting those with impaired visual and motor skills and supporting safe travel and driving.

[0825] (Application example 1)

[0826] 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."

[0827] Conventional systems to support elderly people and visually impaired people with reduced visual or motor skills often fail to ensure sufficient safety when traveling or driving. In particular, they have difficulty recognizing the dynamic surrounding environment (traffic light status, pedestrian movement, the presence of obstacles, etc.) in real time and providing appropriate information to the user. Therefore, new assistance systems that solve these problems are needed so that people who require assistance with vision or motor skills can travel and drive safely and with peace of mind.

[0828] 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.

[0829] In this invention, the server includes a means for detecting traffic lights, pedestrians, and obstacles and generating information to support safe driving, a means for analyzing transmitted image data and position data to recognize the surrounding situation, and a means for generating audio data or visual data to notify the user based on the analysis results, thereby making it possible to provide information necessary for users with impaired vision or motor skills to travel and drive safely in real time.

[0830] A "head-mounted device" is a device that can be used by a user by wearing it on their head, and is equipped with a camera and various sensors.

[0831] A "camera" is a device that acquires image data and is capable of constantly capturing images of a specific area.

[0832] "Image data" is digital data containing visual information obtained from a camera.

[0833] "Location data" is data including location information such as GPS, and indicates the user's current location.

[0834] A "server" is a computer system that receives data over a network and analyzes and processes it.

[0835] "Surroundings" refers to environmental information present around the user, such as traffic lights, signs, pedestrians, and other objects.

[0836] "Analysis" refers to processing the acquired image and location data to extract important information.

[0837] "Audio data" is digital data for notifying the analysis results by voice.

[0838] "Visual data" is digital data that visually notifies the results of analysis.

[0839] A "traffic light" is an optical signaling device installed to control traffic.

[0840] "Pedestrian" refers to a person walking on a road or sidewalk.

[0841] An "obstacle" refers to an object that may impede the user's movement.

[0842] "Wireless communication" is a technology that transmits and receives data via radio waves, infrared rays, etc., without using physical connections such as cables.

[0843] "Information to support safe driving" refers to information that includes instructions and warnings necessary for users with impaired vision or motor skills to drive safely.

[0844] The present invention provides a support system for elderly people and visually impaired people with reduced vision and motor skills to safely move around and drive. This system is composed of a head-mounted device, a terminal, and a server. Specific embodiments of the system are described in detail below.

[0845] System Configuration

[0846] head-mounted devices

[0847] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. The device also incorporates an acceleration sensor and gyroscope to detect the user's movements.

[0848] Terminal

[0849] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time. It also has a built-in GPS module that can determine the current location.

[0850] server

[0851] The server receives and analyzes image data and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, pedestrians, and obstacles. The detected information is converted into audio and visual data and sent back to the device. The server uses software libraries such as TensorFlow and OpenCV for processing.

[0852] How it works

[0853] When a user activates the system, the device acquires image data of the road ahead from the head-mounted device's camera. The device also simultaneously collects current location data using its GPS function. This data is then converted into a single data packet via a dedicated app and sent to a server via the Internet. The server analyzes the received data packet and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the exact location of the detected objects and the user's direction of travel are confirmed. The analysis results are then organized as important information to assist safe travel and driving. The server then generates audio guidance and visual information based on the analysis results. The generated information is sent to the device, which then provides this information to the user in real time. For example, if a traffic light is red, a voice message stating "The traffic light ahead is red" is generated. Furthermore, if a pedestrian is standing at a crosswalk, that information is also notified to the user via audio and visual data.

[0854] Specific examples

[0855] Suppose a user activates this system while in an autonomous vehicle. While driving, a traffic light appears ahead and its status is confirmed by a camera. The server performs image analysis and detects that the traffic light is red. At that time, a voice message stating "The traffic light ahead is red" is notified to the user. A similar notification is also given when a pedestrian approaches a crosswalk. In this way, it is possible to compensate for declines in visual and motor skills and support safe driving.

[0856] Prompt Sentence Examples

[0857] Examples of prompt sentences include:

[0858] "Please create an algorithm that determines the status of traffic lights ahead in real time and notifies visually impaired people by voice."

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] When a user starts the system, the terminal acquires image data from the camera of the head-mounted device. This process captures live video from the camera and generates image data. The input is raw data from the camera and the output is the captured image data.

[0862] Step 2:

[0863] The device uses GPS to obtain location data, which allows the user to determine their current location. The input is a GPS signal, and the output is the current latitude and longitude.

[0864] Step 3:

[0865] The device formats the acquired image data and location data into a single data packet and sends it to a server via the Internet. In this process, the image data and location data are compiled into a format such as JSON and sent to the server using an HTTP POST request. The inputs are the image data and location data, and the output is the sent data packet.

[0866] Step 4:

[0867] The server receives data packets sent from the device and analyzes the image data and location data. First, the server decodes the data packets to separate the image data and location data. Next, the server analyzes the image data using advanced image recognition algorithms and machine learning models (e.g., TensorFlow, OpenCV) to detect traffic lights, signs, pedestrians, obstacles, etc. The input is image data and location data, and the output is the analysis results.

[0868] Step 5:

[0869] The server generates audio or visual data based on the analysis results to support safe driving. For example, if the analysis indicates that the traffic light is red, it generates an audio message saying, "The traffic light ahead is red." The input is the analysis result, and the output is the generated audio or visual data.

[0870] Step 6:

[0871] The server sends the generated audio or visual data to the terminal. In this process, the generated data is sent to the terminal via the Internet. The input is the audio or visual data, and the output is the sent data.

[0872] Step 7:

[0873] The terminal provides the user with audio or visual data received from the server, for example by playing the audio data or displaying the visual data on the display of the head-mounted device. The input is the audio or visual data from the server, and the output is the information provided to the user.

[0874] 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.

[0875] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[0876] System Configuration

[0877] head-mounted devices

[0878] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. In addition, a microphone and heart rate sensor may be added, making it possible to capture the user's voice and biometric data.

[0879] Smartphones and other devices

[0880] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[0881] server

[0882] The server receives and analyzes image data, location data, and audio data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice and biometric data to identify emotions. The detected information and emotional state are then formatted as audio or visual data and sent back to the device.

[0883] Program processing

[0884] Data collection and transmission

[0885] When a user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[0886] Data analysis

[0887] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[0888] Information Generation and Notification

[0889] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0890] Specific examples

[0891] Traffic light notifications while driving

[0892] 1. A user is wearing a head-mounted device while driving a car.

[0893] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[0894] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[0895] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[0896] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[0897] Through this process, users receive important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[0898] The processing flow will be explained below.

[0899] Step 1:

[0900] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[0901] Step 2:

[0902] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[0903] Step 3:

[0904] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[0905] Step 4:

[0906] The device uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data.

[0907] Step 5:

[0908] The device combines the image data, GPS location data, and audio and biometric data acquired from the camera into a single data packet, which also includes a timestamp.

[0909] Step 6:

[0910] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[0911] Step 7:

[0912] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[0913] Step 8:

[0914] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[0915] Step 9:

[0916] The server passes voice and biometric data to an emotion engine that analyzes voice tone and heart rate fluctuation patterns to identify the user's emotional state.

[0917] Step 10:

[0918] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[0919] Step 11:

[0920] Based on the analysis results and the user's emotional state, the server generates audio guidance and visual information that best suits the user's current state. For example, if the traffic light is red and the server identifies the user as feeling stressed, it generates a message saying, "Remain calm, the light ahead is red."

[0921] Step 12:

[0922] The server sends the generated audio and visual data to the device, which is also updated in real time.

[0923] Step 13:

[0924] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[0925] Step 14:

[0926] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[0927] Step 15:

[0928] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[0929] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely according to their emotional state.

[0930] Example 2

[0931] 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."

[0932] For elderly people and visually impaired people to travel and drive safely, they need not only to accurately grasp their surroundings but also to respond to changes in their own emotions and physical condition. However, conventional systems focus on recognizing the surroundings and do not provide feedback that adapts to the user's emotional and physical states, which does not ensure safety. Therefore, a system that provides adaptive feedback that also takes the user's emotional state into account is needed.

[0933] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and for identifying the user's emotional state, a means for generating voice data or visual data for notifying the user based on the analysis result, and a means for providing the generated data to the user. This allows the user to receive feedback according to not only the surrounding situation but also their own emotional state and physical condition, thereby realizing safe travel and driving.

[0934] A "head-mounted device" is a device worn on the user's head, equipped with a camera and various sensors, for obtaining the user's field of vision and biometric information.

[0935] A "camera" is a photographing device for acquiring image data, which is attached to a head-mounted device and positioned to cover the user's field of vision.

[0936] "Location data" is data indicating the current location of the user, obtained using a location information system such as a GPS.

[0937] "Voice data" refers to data including the user's voice and surrounding sounds acquired using a voice input device such as a microphone.

[0938] "Biometric data" refers to data indicating the user's heart rate and other biometric indicators obtained using a biometric information sensor such as a heart rate sensor.

[0939] A "server" is a computer system that provides the computational resources to analyze data, including captured image data, location data, audio data, and biometric data, to identify the user's situation and emotional state.

[0940] "Analysis" is the process of processing data and extracting meaningful information, and the system uses image recognition algorithms and sentiment analysis engines.

[0941] "Audio data or visual data" is notification data for the user that is generated based on the analysis results obtained, and is data for providing audio guidance or displaying visual information.

[0942] "Wireless communication" refers to technology that transmits data wirelessly using Bluetooth or Wi-Fi, etc.

[0943] A "wearable support" is a structure for fixing a head-mounted device to the user's head, and includes eyeglass frame types, etc.

[0944] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to move and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal, a server linked via the Internet, and the emotion engine.

[0945] head-mounted devices

[0946] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. Additional devices such as a microphone and heart rate sensor can also be added, making it possible to capture the user's voice and biometric data. This allows the user to understand the situation around them in real time.

[0947] Terminal

[0948] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time. Furthermore, the terminal uses the GPS function to acquire current location data and identify the user's movement route.

[0949] server

[0950] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. The server is equipped with advanced image recognition algorithms and machine learning models, such as TensorFlow and PyTorch, to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine uses technologies such as IBM Watson and Azure Cognitive Services to analyze the user's voice and biometric data to identify emotions. This is achieved by analyzing voice tone and heart rate fluctuation patterns.

[0951] Information generation and provision

[0952] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[0953] Specific examples

[0954] When a user wears a head-mounted device while driving, the device acquires image data of the road ahead from the camera, GPS location information, as well as audio and heart rate information, and sends them to the server. The server performs image analysis and detects that the traffic light ahead is red. The emotion engine also identifies an increase in the user's stress level. The server generates an audio message saying, "Remain calm, the traffic light ahead is red," and sends it to the device. The device plays the audio message to the user and, if necessary, projects the information on the head-up display. Through this process, the user obtains important information about their surroundings in real time, enabling safe travel and driving according to their emotional state.

[0955] Examples of prompt statements

[0956] Here are some example prompts to input to a generative AI model:

[0957] "We have created a program that describes a system that notifies a user while driving that the traffic light ahead is red and that the user is feeling stressed. The program collects and analyzes image data, GPS location information, voice data, and heart rate information, and then provides an appropriate voice message. The processing steps are described in detail."

[0958] This allows the generative AI model to generate a detailed program for the system.

[0959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0960] Step 1: Initialization

[0961] When the user starts the system, the terminal automatically connects to the head-mounted device via Bluetooth or Wi-Fi.

[0962] An initial data packet of the connection is sent to the server to ensure all sensors and system components are working properly. At this stage, the head-mounted device's camera, microphone, heart rate sensor, etc. are online.

[0963] Input: System startup command

[0964] Output: Device and server connection status, sensor online status

[0965] Step 2: Data collection

[0966] The device begins to acquire real-time image data of the front view from the head-mounted device's camera, which continuously captures images at frames per second (fps).

[0967] The device uses its built-in GPS to acquire location data, which allows the device to determine the user's current location and route.

[0968] The device records the user's voice data through a microphone in the head-mounted device and obtains the user's heart rate data using a heart rate sensor, which is used to analyze the user's stress level and emotional state.

[0969] Input: Camera footage, GPS data, audio data, heart rate data

[0970] Output: Formatted data packet

[0971] Step 3: Send data

[0972] The device uses a dedicated app to packetize the collected image data, location data, voice data, and biometric data, and transmits it to a server via the Internet.

[0973] A security protocol (e.g. HTTPS) is used for transmission and data is encrypted.

[0974] Input: Formatted data packet

[0975] Output: Data transfer completion notification to the server

[0976] Step 4: Data analysis

[0977] The server passes the received data packets to an analysis program, which uses image analysis algorithms to detect important objects such as traffic lights, signs, and pedestrians.

[0978] The server applies machine learning models such as TensorFlow and PyTorch to analyze image and audio data. The audio and biometric data are then analyzed by an emotion engine to identify the user's emotional state from their tone of voice and heart rate fluctuation patterns.

[0979] Input: Data packet sent to the server

[0980] Output: Analysis results (surrounding situation and emotional state)

[0981] Step 5: Information Generation

[0982] Based on the analysis, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, it generates a voice message saying, "The light ahead is red." If the emotion engine identifies an increase in the user's stress level, it creates a message such as, "Calm down, the light ahead is red."

[0983] Input: Analysis results

[0984] Output: Generated audio and visual data

[0985] Step 6: Notification

[0986] The server transmits the generated audio guide and visual information to the terminal.

[0987] The terminal plays the received audio data to the user through a speaker and, if necessary, displays visual information on the display of the head-mounted device.

[0988] Input: Generated audio and visual data

[0989] Output: User notification (audio and visual display)

[0990] This allows users to receive real-time situational awareness information and feedback based on their emotional state, enabling safe travel and driving.

[0991] (Application example 2)

[0992] 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."

[0993] Elderly people and visually impaired people with reduced visual and motor skills have difficulty traveling and driving safely. They also often experience mental strain while traveling and require support tailored to their emotional state. However, current mobility assistance systems lack the ability to recognize the user's emotional state and provide appropriate feedback. Therefore, there is a need for a system that can reduce the user's mental strain and improve the safety of travel.

[0994] 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 analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and the user's emotional state, means for generating voice data or visual data for notifying the user based on the analysis results, and means for generating voice guidance appropriate to the user's emotional state. This enables real-time feedback in line with the user's emotional state, enabling safe and secure travel and driving.

[0995] A "head-mounted device" is a device worn by a user on the head to capture visual data of the area in front of the user and the surrounding environment.

[0996] An "imaging device" is a device for acquiring image data, such as a camera, that is attached to a head-mounted device.

[0997] "Image data" is digital data containing visual information obtained from an imaging device.

[0998] "Location Data" means GPS signals and other location information data used to indicate a user's current location.

[0999] "Audio data" refers to digitized audio signals including user conversation and environmental sounds.

[1000] "Biometric data" refers to data used to measure the user's physical condition, such as heart rate and body temperature.

[1001] A "server" is a central system that communicates with multiple terminals via a network and analyzes and stores data.

[1002] "Analysis" is the act of processing data to recognize a specific situation or emotional state based on the transmitted data.

[1003] "Notification" refers to the act of providing information to the user in audio or visual form based on the analysis results.

[1004] "Audio guidance" refers to verbal instructions and advice provided to the user based on the analysis results.

[1005] "Wireless communication" is a method of sending and receiving data without using cables, using methods such as radio waves, Bluetooth, and Wi-Fi.

[1006] "Emotional state" refers to the user's current psychological state or feelings, including feelings of stress or relief.

[1007] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely. It also combines an emotion engine that recognizes the user's emotional state and provides adaptive feedback accordingly. This system is composed of an imaging device attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[1008] System configuration:

[1009] head-mounted device

[1010] Head-mounted devices are equipped with cameras and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the device. In addition, a microphone and heart rate sensor can be added, making it possible to capture the user's voice data and biometric data.

[1011] Smartphones and other devices

[1012] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[1013] server

[1014] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice data and biometric data to identify emotions. The detected information and emotional state are converted into voice data or visual data and sent back to the device.

[1015] Program processing

[1016] Data collection and transmission

[1017] When the user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[1018] Data analysis

[1019] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[1020] Information Generation and Notification

[1021] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[1022] Specific examples

[1023] Traffic light notifications while driving

[1024] 1. A user wears a head-mounted device while driving a car.

[1025] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[1026] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[1027] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[1028] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[1029] This allows users to obtain important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[1030] Prompt sentences for generative AI models (examples)

[1031] Audio data preprocessing:

[1032] python

[1033] audio_input = preprocess_audio(audio_data)

[1034] emotion_prediction = emotion_model.predict(audio_input)

[1035] This provides users with an environment in which they can move around comfortably and safely.

[1036] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1037] Step 1: System startup and data collection

[1038] The user starts the system. The device acquires image data of the area in front of the vehicle from a camera mounted on a head-mounted device. The input data includes image data, audio data, location data, and heart rate data. This data is then collected by the device. The device then collects information on the area in front of the vehicle, the sound environment, location information, and the user's biometric information all at once.

[1039] Step 2: Organize and send data

[1040] The device assembles the collected data into a single data packet, which includes image data of the road ahead, current location information, voice data, and heart rate data. The assembled data packet is then sent to a server via the Internet. The device is responsible for aggregating and transmitting the data.

[1041] Step 3: Analyzing the image data

[1042] The server separates and decodes the image data from the received data packets. It has data packets as input and decoded image data as output. The server's image analysis algorithms detect important objects such as traffic lights, signs, and pedestrians. This analysis process uses machine learning models and image recognition algorithms.

[1043] Step 4: Analyzing audio and biometric data

[1044] The server extracts voice and heart rate data from the data packets. The voice data is analyzed by an emotion engine to identify the user's emotional state. The inputs are voice data and biometric data, and the output is the analyzed emotional state. The tone of voice and heart rate fluctuation patterns are analyzed. A generative AI model is used for this process.

[1045] Step 5: Generate notification data

[1046] The server generates information to be notified to the user based on the results of image analysis and emotion analysis. The analysis results are input, and audio or visual data for notification is generated as output. For example, if the traffic light is red and the user is feeling stressed, an audio message such as "Calm down, the light ahead is red" is generated.

[1047] Step 6: Submit and provide data

[1048] The generated notification data is sent from the server to the device. The device provides this notification data to the user in real time. The generated notification data is input, and audio messages and visual information are provided to the user as output. Information is displayed on the audio output and display of the head-mounted device.

[1049] This allows the user to accurately grasp the situation and receive optimal feedback tailored to their emotional state.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] [Fourth embodiment]

[1054] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1055] 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.

[1056] 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).

[1057] 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.

[1058] 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.

[1059] 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).

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] 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."

[1067] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[1068] System Configuration

[1069] head-mounted devices

[1070] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[1071] Smartphones and other devices

[1072] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[1073] server

[1074] The server receives and analyzes image and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then converted into audio and visual data and sent back to the device.

[1075] Program processing

[1076] Data collection and transmission

[1077] When a user activates the system, the device acquires image data of the area in front of it from the head-mounted device's camera. The device also uses GPS to collect data on its current location. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[1078] Data analysis

[1079] The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel. The analysis results are organized as important information to assist safe travel and driving.

[1080] Information Generation and Notification

[1081] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data. The generated information is sent to the device, which provides it to the user in real time.

[1082] Specific examples

[1083] Traffic light notifications while driving

[1084] 1. A user is wearing a head-mounted device while driving a car.

[1085] 2. The device acquires forward image data and GPS location information from the camera and sends them to the server.

[1086] 3. The server performs image analysis and detects that the traffic light ahead is red.

[1087] 4. The server generates a voice message saying "The traffic light ahead is red" and sends it to the device.

[1088] 5. The device plays audio messages to the user and projects information on a heads-up display if visual data is needed.

[1089] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. Furthermore, by using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[1093] Step 2:

[1094] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[1095] Step 3:

[1096] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[1097] Step 4:

[1098] The device combines the image data and GPS location data acquired from the camera into a single data packet, which also includes a timestamp.

[1099] Step 5:

[1100] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[1101] Step 6:

[1102] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[1103] Step 7:

[1104] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[1105] Step 8:

[1106] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[1107] Step 9:

[1108] The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, it generates a voice message saying, "The traffic light ahead is red."

[1109] Step 10:

[1110] The server sends the generated audio and visual data to the device, which is also updated in real time.

[1111] Step 11:

[1112] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[1113] Step 12:

[1114] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[1115] Step 13:

[1116] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[1117] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely.

[1118] Example 1

[1119] 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."

[1120] Conventional mobility and driving assistance systems for the elderly and visually impaired, who have reduced visual and motor skills, have problems with lack of accuracy in recognizing environmental conditions and real-time performance. Furthermore, insufficient information is provided to users, resulting in situations where safety cannot be fully ensured. In particular, the lack of accuracy in recognizing important objects such as traffic lights and pedestrians can potentially hinder safe mobility and driving.

[1121] 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.

[1122] In this invention, the server includes means for analyzing the transmitted image data and location data to recognize the surrounding situation, means for generating audio data or visual data for notifying the user based on the analysis results using a generative AI model, and means for providing the generated data to the user, thereby enabling the user to receive highly accurate information in real time and to travel and drive safely.

[1123] A "head-mounted device" is a device that is worn on the user's head and is equipped with cameras and sensors to acquire visual information about the area in front of them.

[1124] The "camera" is a photographing device for capturing image data in front of the vehicle, and is attached to the head-mounted device.

[1125] A "terminal" is an electronic device that communicates with the head-mounted device and transmits image data and location data to a server.

[1126] "Location data" refers to data indicating the user's current geographical location, and is obtained using a GPS function or the like.

[1127] "Server" means a computer system that analyzes the transmitted image data and location data, and uses a generative AI model to generate audio or visual data based on the analysis results and provide it to the user.

[1128] A "generative AI model" is an algorithm or model that uses artificial intelligence technology to perform image analysis and generate audio data.

[1129] "Voice data" refers to voice information generated to notify the user, and is generated based on the analysis results.

[1130] "Visual data" refers to visual information generated to notify the user and displayed on the display of a head-mounted device.

[1131] A "data packet" is a unit of data for transmitting a group of multiple pieces of information, such as image data and position data.

[1132] "Wireless communication" is a communication method that sends and receives data using wireless technologies such as Bluetooth and Wi-Fi.

[1133] This invention is a support system for elderly people and visually impaired people with reduced vision and motor skills to move around and drive safely. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, and a server linked via the Internet.

[1134] System Configuration

[1135] head-mounted devices

[1136] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the user.

[1137] Smartphones and other devices

[1138] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[1139] server

[1140] The server receives and analyzes image and location data sent from the device. It uses advanced image recognition algorithms and generative AI models (such as TensorFlow and PyTorch) to detect important objects such as traffic lights, signs, and pedestrians. The detected information is then formatted as audio and visual data and sent back to the device.

[1141] Specific examples of programs

[1142] 1. When a user activates the system, the camera on the head-mounted device captures real-time image data of the area in front of the user. At the same time, the GPS function of the device is used to collect data on the user's current location.

[1143] 2. The device converts this data into data packets via a dedicated app and sends them to a server via the Internet.

[1144] 3. The server analyzes the received data packets and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the server confirms the exact location of the detected objects and the user's direction of travel.

[1145] 4. The server generates audio guidance and visual information based on the analysis results. For example, if the traffic light is red, a voice message saying "The traffic light ahead is red" is generated. Also, if a pedestrian is standing at a crosswalk, that information is also notified to the user as audio and visual data.

[1146] 5. A generative AI model (e.g., Google Text-to-Speech API) is used to convert the analysis results into natural-sounding speech, which is then sent to the device and played back to the user.

[1147] 6. Through voice guidance, users can accurately grasp the situation ahead and travel and drive safely.

[1148] Prompt Sentence Examples

[1149] "Please tell me about a system that enables visually impaired people to move around safely using a head-mounted device."

[1150] In this way, the present invention is an effective support system that compensates for the decline in users' visual and motor skills, enabling them to travel and drive safely. By using wireless communication, it is possible to reduce costs and provide a wide range of support to a wide range of users.

[1151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1152] Step 1:

[1153] When the user activates the system, the camera and various sensors on the head-mounted device are activated. The camera continuously captures image data in front of the user's field of view. The input is real-time image data and position data obtained from the camera and sensors, and the output is these raw data sets.

[1154] Step 2:

[1155] The device acquires forward image data from the head-mounted device's camera and simultaneously acquires current location data from the built-in GPS sensor. These data are converted into data packets by a dedicated app installed on the device. The input is image data from the camera and location data from the GPS, and the output is a data packet.

[1156] Step 3:

[1157] The terminal sends the generated data packets to a server via the Internet. The communication is secure using the HTTPS protocol. The input is the formatted data packet, and the output is the packet data sent to the server.

[1158] Step 4:

[1159] The server analyzes the received data packets. It opens the packets containing image data and location data and performs analysis using image recognition algorithms and generative AI models (such as TensorFlow or PyTorch). The input is the data packets, and the output is the analysis results, which are recognition data for traffic lights, signs, pedestrians, etc.

[1160] Step 5:

[1161] The server generates a voice message and visual information based on the analysis results. It converts the analysis results into speech using a generative AI model (e.g., Google Text-to-Speech API). The input is the recognition data, and the output is a voice message and visual data.

[1162] Step 6:

[1163] The server sends the generated voice message and visual data to the terminal. The input is the generated voice message and visual data, and the output is the information sent to the terminal.

[1164] Step 7:

[1165] The terminal plays the received voice message and projects visual information on the head-up display. The input is the voice message and visual data sent from the server, and the output is real-time assistance information provided to the user.

[1166] Step 8:

[1167] The user moves and drives safely based on the audio guidance and visual information provided by the device. The input is the information provided by the device, and the output is the user's safe behavior.

[1168] In this way, the present invention provides users with highly accurate information in real time, assisting those with impaired visual and motor skills and supporting safe travel and driving.

[1169] (Application example 1)

[1170] 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."

[1171] Conventional systems to support elderly people and visually impaired people with reduced visual or motor skills often fail to ensure sufficient safety when traveling or driving. In particular, they have difficulty recognizing the dynamic surrounding environment (traffic light status, pedestrian movement, the presence of obstacles, etc.) in real time and providing appropriate information to the user. Therefore, new assistance systems that solve these problems are needed so that people who require assistance with vision or motor skills can travel and drive safely and with peace of mind.

[1172] 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.

[1173] In this invention, the server includes a means for detecting traffic lights, pedestrians, and obstacles and generating information to support safe driving, a means for analyzing transmitted image data and position data to recognize the surrounding situation, and a means for generating audio data or visual data to notify the user based on the analysis results, thereby making it possible to provide information necessary for users with impaired vision or motor skills to travel and drive safely in real time.

[1174] A "head-mounted device" is a device that can be used by a user by wearing it on their head, and is equipped with a camera and various sensors.

[1175] A "camera" is a device that acquires image data and is capable of constantly capturing images of a specific area.

[1176] "Image data" is digital data containing visual information obtained from a camera.

[1177] "Location data" is data including location information such as GPS, and indicates the user's current location.

[1178] A "server" is a computer system that receives data over a network and analyzes and processes it.

[1179] "Surroundings" refers to environmental information present around the user, such as traffic lights, signs, pedestrians, and other objects.

[1180] "Analysis" refers to processing the acquired image and location data to extract important information.

[1181] "Audio data" is digital data for notifying the analysis results by voice.

[1182] "Visual data" is digital data that visually notifies the results of analysis.

[1183] A "traffic light" is an optical signaling device installed to control traffic.

[1184] "Pedestrian" refers to a person walking on a road or sidewalk.

[1185] An "obstacle" refers to an object that may impede the user's movement.

[1186] "Wireless communication" is a technology that transmits and receives data via radio waves, infrared rays, etc., without using physical connections such as cables.

[1187] "Information to support safe driving" refers to information that includes instructions and warnings necessary for users with impaired vision or motor skills to drive safely.

[1188] The present invention provides a support system for elderly people and visually impaired people with reduced vision and motor skills to safely move around and drive. This system is composed of a head-mounted device, a terminal, and a server. Specific embodiments of the system are described in detail below.

[1189] System Configuration

[1190] head-mounted devices

[1191] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. The device also incorporates an acceleration sensor and gyroscope to detect the user's movements.

[1192] Terminal

[1193] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera to a server. A dedicated app is installed on the terminal, which transfers and receives data in real time. It also has a built-in GPS module that can determine the current location.

[1194] server

[1195] The server receives and analyzes image data and location data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, pedestrians, and obstacles. The detected information is converted into audio and visual data and sent back to the device. The server uses software libraries such as TensorFlow and OpenCV for processing.

[1196] How it works

[1197] When a user activates the system, the device acquires image data of the road ahead from the head-mounted device's camera. The device also simultaneously collects current location data using its GPS function. This data is then converted into a single data packet via a dedicated app and sent to a server via the Internet. The server analyzes the received data packet and detects objects such as traffic lights, signs, and people from the image data. Based on the location data, the exact location of the detected objects and the user's direction of travel are confirmed. The analysis results are then organized as important information to assist safe travel and driving. The server then generates audio guidance and visual information based on the analysis results. The generated information is sent to the device, which then provides this information to the user in real time. For example, if a traffic light is red, a voice message stating "The traffic light ahead is red" is generated. Furthermore, if a pedestrian is standing at a crosswalk, that information is also notified to the user via audio and visual data.

[1198] Specific examples

[1199] Suppose a user activates this system while in an autonomous vehicle. While driving, a traffic light appears ahead and its status is confirmed by a camera. The server performs image analysis and detects that the traffic light is red. At that time, a voice message stating "The traffic light ahead is red" is notified to the user. A similar notification is also given when a pedestrian approaches a crosswalk. In this way, it is possible to compensate for declines in visual and motor skills and support safe driving.

[1200] Prompt Sentence Examples

[1201] Examples of prompt sentences include:

[1202] "Please create an algorithm that determines the status of traffic lights ahead in real time and notifies visually impaired people by voice."

[1203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1204] Step 1:

[1205] When a user starts the system, the terminal acquires image data from the camera of the head-mounted device. This process captures live video from the camera and generates image data. The input is raw data from the camera and the output is the captured image data.

[1206] Step 2:

[1207] The device uses GPS to obtain location data, which allows the user to determine their current location. The input is a GPS signal, and the output is the current latitude and longitude.

[1208] Step 3:

[1209] The device formats the acquired image data and location data into a single data packet and sends it to a server via the Internet. In this process, the image data and location data are compiled into a format such as JSON and sent to the server using an HTTP POST request. The inputs are the image data and location data, and the output is the sent data packet.

[1210] Step 4:

[1211] The server receives data packets sent from the device and analyzes the image data and location data. First, the server decodes the data packets to separate the image data and location data. Next, the server analyzes the image data using advanced image recognition algorithms and machine learning models (e.g., TensorFlow, OpenCV) to detect traffic lights, signs, pedestrians, obstacles, etc. The input is image data and location data, and the output is the analysis results.

[1212] Step 5:

[1213] The server generates audio or visual data based on the analysis results to support safe driving. For example, if the analysis indicates that the traffic light is red, it generates an audio message saying, "The traffic light ahead is red." The input is the analysis result, and the output is the generated audio or visual data.

[1214] Step 6:

[1215] The server sends the generated audio or visual data to the terminal. In this process, the generated data is sent to the terminal via the Internet. The input is the audio or visual data, and the output is the sent data.

[1216] Step 7:

[1217] The terminal provides the user with audio or visual data received from the server, for example by playing the audio data or displaying the visual data on the display of the head-mounted device. The input is the audio or visual data from the server, and the output is the information provided to the user.

[1218] 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.

[1219] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[1220] System Configuration

[1221] head-mounted devices

[1222] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. In addition, a microphone and heart rate sensor may be added, making it possible to capture the user's voice and biometric data.

[1223] Smartphones and other devices

[1224] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[1225] server

[1226] The server receives and analyzes image data, location data, and audio data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice and biometric data to identify emotions. The detected information and emotional state are then formatted as audio or visual data and sent back to the device.

[1227] Program processing

[1228] Data collection and transmission

[1229] When a user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[1230] Data analysis

[1231] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[1232] Information Generation and Notification

[1233] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[1234] Specific examples

[1235] Traffic light notifications while driving

[1236] 1. A user is wearing a head-mounted device while driving a car.

[1237] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[1238] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[1239] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[1240] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[1241] Through this process, users receive important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[1242] The processing flow will be explained below.

[1243] Step 1:

[1244] The user installs the smartphone app and wears the head-mounted device. The device and head-mounted device are paired via Bluetooth or Wi-Fi to establish a connection.

[1245] Step 2:

[1246] The user activates the system and begins moving or driving. The device begins to acquire real-time image data from the camera.

[1247] Step 3:

[1248] The device uses the GPS module to obtain data about its current location, including latitude, longitude, and altitude.

[1249] Step 4:

[1250] The device uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data.

[1251] Step 5:

[1252] The device combines the image data, GPS location data, and audio and biometric data acquired from the camera into a single data packet, which also includes a timestamp.

[1253] Step 6:

[1254] The device sends the generated data packets to a server over the Internet at regular intervals (e.g., every second).

[1255] Step 7:

[1256] The server passes the received data packets to an analysis program, which decodes the image data and applies image analysis algorithms.

[1257] Step 8:

[1258] The server's image analysis algorithms detect important objects such as traffic lights, signs, and people from the image data using machine learning models.

[1259] Step 9:

[1260] The server passes voice and biometric data to an emotion engine that analyzes voice tone and heart rate fluctuation patterns to identify the user's emotional state.

[1261] Step 10:

[1262] The server uses the GPS location data and analysis results to identify the presence of a specific object in the user's direction of travel.

[1263] Step 11:

[1264] Based on the analysis results and the user's emotional state, the server generates audio guidance and visual information that best suits the user's current state. For example, if the traffic light is red and the server identifies the user as feeling stressed, it generates a message saying, "Remain calm, the light ahead is red."

[1265] Step 12:

[1266] The server sends the generated audio and visual data to the device, which is also updated in real time.

[1267] Step 13:

[1268] The device then plays the received audio data to the user, and if equipped with a head-up display, projects the visual data.

[1269] Step 14:

[1270] If the user wants additional information, they can issue a voice command to the device, which then recognizes the user's voice command, converts it into text data, and sends it to the server.

[1271] Step 15:

[1272] The server performs additional data analysis based on the user's request, regenerates the necessary information, and sends it to the device, which then notifies the user of the updated information via voice or visual means.

[1273] Through this process, users will receive important information about their surroundings in real time, allowing them to travel and drive safely according to their emotional state.

[1274] Example 2

[1275] 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."

[1276] For elderly people and visually impaired people to travel and drive safely, they need not only to accurately grasp their surroundings but also to respond to changes in their own emotions and physical condition. However, conventional systems focus on recognizing the surroundings and do not provide feedback that adapts to the user's emotional and physical states, which does not ensure safety. Therefore, a system that provides adaptive feedback that also takes the user's emotional state into account is needed.

[1277] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and for identifying the user's emotional state, a means for generating voice data or visual data for notifying the user based on the analysis result, and a means for providing the generated data to the user. This allows the user to receive feedback according to not only the surrounding situation but also their own emotional state and physical condition, thereby realizing safe travel and driving.

[1278] A "head-mounted device" is a device worn on the user's head, equipped with a camera and various sensors, for obtaining the user's field of vision and biometric information.

[1279] A "camera" is a photographing device for acquiring image data, which is attached to a head-mounted device and positioned to cover the user's field of vision.

[1280] "Location data" is data indicating the current location of the user, obtained using a location information system such as a GPS.

[1281] "Voice data" refers to data including the user's voice and surrounding sounds acquired using a voice input device such as a microphone.

[1282] "Biometric data" refers to data indicating the user's heart rate and other biometric indicators obtained using a biometric information sensor such as a heart rate sensor.

[1283] A "server" is a computer system that provides the computational resources to analyze data, including captured image data, location data, audio data, and biometric data, to identify the user's situation and emotional state.

[1284] "Analysis" is the process of processing data and extracting meaningful information, and the system uses image recognition algorithms and sentiment analysis engines.

[1285] "Audio data or visual data" is notification data for the user that is generated based on the analysis results obtained, and is data for providing audio guidance or displaying visual information.

[1286] "Wireless communication" refers to technology that transmits data wirelessly using Bluetooth or Wi-Fi, etc.

[1287] A "wearable support" is a structure for fixing a head-mounted device to the user's head, and includes eyeglass frame types, etc.

[1288] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to move and drive safely, and also combines it with an emotion engine that recognizes the user's emotional state and provides adaptive feedback. This system is composed of a camera attached to a head-mounted device, a terminal, a server linked via the Internet, and the emotion engine.

[1289] head-mounted devices

[1290] The head-mounted device worn by the user is equipped with a camera and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of it. Additional devices such as a microphone and heart rate sensor can also be added, making it possible to capture the user's voice and biometric data. This allows the user to understand the situation around them in real time.

[1291] Terminal

[1292] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time. Furthermore, the terminal uses the GPS function to acquire current location data and identify the user's movement route.

[1293] server

[1294] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. The server is equipped with advanced image recognition algorithms and machine learning models, such as TensorFlow and PyTorch, to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine uses technologies such as IBM Watson and Azure Cognitive Services to analyze the user's voice and biometric data to identify emotions. This is achieved by analyzing voice tone and heart rate fluctuation patterns.

[1295] Information generation and provision

[1296] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[1297] Specific examples

[1298] When a user wears a head-mounted device while driving, the device acquires image data of the road ahead from the camera, GPS location information, as well as audio and heart rate information, and sends them to the server. The server performs image analysis and detects that the traffic light ahead is red. The emotion engine also identifies an increase in the user's stress level. The server generates an audio message saying, "Remain calm, the traffic light ahead is red," and sends it to the device. The device plays the audio message to the user and, if necessary, projects the information on the head-up display. Through this process, the user obtains important information about their surroundings in real time, enabling safe travel and driving according to their emotional state.

[1299] Examples of prompt statements

[1300] Here are some example prompts to input to a generative AI model:

[1301] "We have created a program that describes a system that notifies a user while driving that the traffic light ahead is red and that the user is feeling stressed. The program collects and analyzes image data, GPS location information, voice data, and heart rate information, and then provides an appropriate voice message. The processing steps are described in detail."

[1302] This allows the generative AI model to generate a detailed program for the system.

[1303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1304] Step 1: Initialization

[1305] When the user starts the system, the terminal automatically connects to the head-mounted device via Bluetooth or Wi-Fi.

[1306] An initial data packet of the connection is sent to the server to ensure all sensors and system components are working properly. At this stage, the head-mounted device's camera, microphone, heart rate sensor, etc. are online.

[1307] Input: System startup command

[1308] Output: Device and server connection status, sensor online status

[1309] Step 2: Data collection

[1310] The device begins to acquire real-time image data of the front view from the head-mounted device's camera, which continuously captures images at frames per second (fps).

[1311] The device uses its built-in GPS to acquire location data, which allows the device to determine the user's current location and route.

[1312] The device records the user's voice data through a microphone in the head-mounted device and obtains the user's heart rate data using a heart rate sensor, which is used to analyze the user's stress level and emotional state.

[1313] Input: Camera footage, GPS data, audio data, heart rate data

[1314] Output: Formatted data packet

[1315] Step 3: Send data

[1316] The device uses a dedicated app to packetize the collected image data, location data, voice data, and biometric data, and transmits it to a server via the Internet.

[1317] A security protocol (e.g. HTTPS) is used for transmission and data is encrypted.

[1318] Input: Formatted data packet

[1319] Output: Data transfer completion notification to the server

[1320] Step 4: Data analysis

[1321] The server passes the received data packets to an analysis program, which uses image analysis algorithms to detect important objects such as traffic lights, signs, and pedestrians.

[1322] The server applies machine learning models such as TensorFlow and PyTorch to analyze image and audio data. The audio and biometric data are then analyzed by an emotion engine to identify the user's emotional state from their tone of voice and heart rate fluctuation patterns.

[1323] Input: Data packet sent to the server

[1324] Output: Analysis results (surrounding situation and emotional state)

[1325] Step 5: Information Generation

[1326] Based on the analysis, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, it generates a voice message saying, "The light ahead is red." If the emotion engine identifies an increase in the user's stress level, it creates a message such as, "Calm down, the light ahead is red."

[1327] Input: Analysis results

[1328] Output: Generated audio and visual data

[1329] Step 6: Notification

[1330] The server transmits the generated audio guide and visual information to the terminal.

[1331] The terminal plays the received audio data to the user through a speaker and, if necessary, displays visual information on the display of the head-mounted device.

[1332] Input: Generated audio and visual data

[1333] Output: User notification (audio and visual display)

[1334] This allows users to receive real-time situational awareness information and feedback based on their emotional state, enabling safe travel and driving.

[1335] (Application example 2)

[1336] 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."

[1337] Elderly people and visually impaired people with reduced visual and motor skills have difficulty traveling and driving safely. They also often experience mental strain while traveling and require support tailored to their emotional state. However, current mobility assistance systems lack the ability to recognize the user's emotional state and provide appropriate feedback. Therefore, there is a need for a system that can reduce the user's mental strain and improve the safety of travel.

[1338] 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 analyzing transmitted image data, position data, voice data, and biometric data to recognize the surrounding situation and the user's emotional state, means for generating voice data or visual data for notifying the user based on the analysis results, and means for generating voice guidance appropriate to the user's emotional state. This enables real-time feedback in line with the user's emotional state, enabling safe and secure travel and driving.

[1339] A "head-mounted device" is a device worn by a user on the head to capture visual data of the area in front of the user and the surrounding environment.

[1340] An "imaging device" is a device for acquiring image data, such as a camera, that is attached to a head-mounted device.

[1341] "Image data" is digital data containing visual information obtained from an imaging device.

[1342] "Location Data" means GPS signals and other location information data used to indicate a user's current location.

[1343] "Audio data" refers to digitized audio signals including user conversation and environmental sounds.

[1344] "Biometric data" refers to data used to measure the user's physical condition, such as heart rate and body temperature.

[1345] A "server" is a central system that communicates with multiple terminals via a network and analyzes and stores data.

[1346] "Analysis" is the act of processing data to recognize a specific situation or emotional state based on the transmitted data.

[1347] "Notification" refers to the act of providing information to the user in audio or visual form based on the analysis results.

[1348] "Audio guidance" refers to verbal instructions and advice provided to the user based on the analysis results.

[1349] "Wireless communication" is a method of sending and receiving data without using cables, using methods such as radio waves, Bluetooth, and Wi-Fi.

[1350] "Emotional state" refers to the user's current psychological state or feelings, including feelings of stress or relief.

[1351] This invention is a system that enables elderly people and visually impaired people with reduced visual and motor skills to travel and drive safely. It also combines an emotion engine that recognizes the user's emotional state and provides adaptive feedback accordingly. This system is composed of an imaging device attached to a head-mounted device, a terminal such as a smartphone, a server linked via the Internet, and the emotion engine.

[1352] System configuration:

[1353] head-mounted device

[1354] Head-mounted devices are equipped with cameras and various sensors. The camera is attached in a position that covers the user's field of vision and constantly captures image data of the area in front of the device. In addition, a microphone and heart rate sensor can be added, making it possible to capture the user's voice data and biometric data.

[1355] Smartphones and other devices

[1356] The terminal is connected to the head-mounted device via Bluetooth or Wi-Fi and is responsible for transmitting image data and location data acquired from the camera, as well as the user's voice data, to the server. A dedicated app is installed on the terminal, which transfers and receives data in real time.

[1357] server

[1358] The server receives and analyzes image data, location data, voice data, and biometric data sent from the device. It is equipped with advanced image recognition algorithms and machine learning models to detect important objects such as traffic lights, signs, and pedestrians. The emotion engine analyzes the user's voice data and biometric data to identify emotions. The detected information and emotional state are converted into voice data or visual data and sent back to the device.

[1359] Program processing

[1360] Data collection and transmission

[1361] When the user activates the system, the device begins acquiring image data of the area in front of it from the head-mounted device's camera. The device then uses its GPS to acquire its current location. It also uses a microphone and biometric sensors to collect the user's voice data, heart rate, and other biometric data. This data is then shaped into a single data packet via a dedicated app and sent to a server via the internet.

[1362] Data analysis

[1363] The server passes the received data packets to an analysis program. Image data is decoded and image analysis algorithms are applied. Audio data and biometric data are analyzed by an emotion engine to identify the user's emotional state. This is done using techniques that analyze voice tone and heart rate fluctuation patterns.

[1364] Information Generation and Notification

[1365] Based on the analysis results, the server generates audio guidance and visual information that best suits the user's current emotional state. For example, if the traffic light is red, the server generates an audio message saying, "The traffic light ahead is red," but if the user is identified as feeling stressed, the server sends a message like, "Calm down, the traffic light ahead is red." The generated information is sent to the device, which then provides it to the user in real time.

[1366] Specific examples

[1367] Traffic light notifications while driving

[1368] 1. A user wears a head-mounted device while driving a car.

[1369] 2. The device acquires forward image data, GPS location information, audio data, and heart rate information from the camera and sends them to the server.

[1370] 3. The server performs image analysis to detect that the traffic light ahead is red, and the emotion engine identifies an increase in the user's stress level.

[1371] 4. The server generates a voice message saying "Please stay calm, the traffic light ahead is red" and sends it to the device.

[1372] 5. The device plays audio messages to the user and, if necessary, projects information on the heads-up display.

[1373] This allows users to obtain important information about their surroundings in real time, enabling them to travel and drive safely according to their emotional state.

[1374] Prompt sentences for generative AI models (examples)

[1375] Audio data preprocessing:

[1376] python

[1377] audio_input = preprocess_audio(audio_data)

[1378] emotion_prediction = emotion_model.predict(audio_input)

[1379] This provides users with an environment in which they can move around comfortably and safely.

[1380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1381] Step 1: System startup and data collection

[1382] The user starts the system. The device acquires image data of the area in front of the vehicle from a camera mounted on a head-mounted device. The input data includes image data, audio data, location data, and heart rate data. This data is then collected by the device. The device then collects information on the area in front of the vehicle, the sound environment, location information, and the user's biometric information all at once.

[1383] Step 2: Organize and send data

[1384] The device assembles the collected data into a single data packet, which includes image data of the road ahead, current location information, voice data, and heart rate data. The assembled data packet is then sent to a server via the Internet. The device is responsible for aggregating and transmitting the data.

[1385] Step 3: Analyzing the image data

[1386] The server separates and decodes the image data from the received data packets. It has data packets as input and decoded image data as output. The server's image analysis algorithms detect important objects such as traffic lights, signs, and pedestrians. This analysis process uses machine learning models and image recognition algorithms.

[1387] Step 4: Analyzing audio and biometric data

[1388] The server extracts voice and heart rate data from the data packets. The voice data is analyzed by an emotion engine to identify the user's emotional state. The inputs are voice data and biometric data, and the output is the analyzed emotional state. The tone of voice and heart rate fluctuation patterns are analyzed. A generative AI model is used for this process.

[1389] Step 5: Generate notification data

[1390] The server generates information to be notified to the user based on the results of image analysis and emotion analysis. The analysis results are input, and audio or visual data for notification is generated as output. For example, if the traffic light is red and the user is feeling stressed, an audio message such as "Calm down, the light ahead is red" is generated.

[1391] Step 6: Submit and provide data

[1392] The generated notification data is sent from the server to the device. The device provides this notification data to the user in real time. The generated notification data is input, and audio messages and visual information are provided to the user as output. Information is displayed on the audio output and display of the head-mounted device.

[1393] This allows the user to accurately grasp the situation and receive optimal feedback tailored to their emotional state.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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).

[1401] 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.

[1402] 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."

[1403] 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.

[1404] 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).

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] The following is further disclosed regarding the above embodiment.

[1416] (Claim 1)

[1417] means for acquiring image data from a camera attached to a head-mounted device;

[1418] means for transmitting the acquired image data and position data to a server;

[1419] a means for the server to analyze the transmitted image data and position data to recognize the surrounding situation;

[1420] means for generating audio data or visual data for notifying a user based on the analysis results;

[1421] means for providing the generated data to a user;

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, wherein the camera-mounted device comprises a frame of eyeglasses.

[1425] (Claim 3)

[1426] 10. The system of claim 1, wherein the means for transmitting data uses wireless communication.

[1427] "Example 1"

[1428] (Claim 1)

[1429] means for acquiring image data from a camera attached to a head-mounted device;

[1430] means for transmitting the acquired image data and position data to a server;

[1431] a means for the server to analyze the transmitted image data and position data to recognize the surrounding situation;

[1432] A means for generating audio data or visual data for notifying a user based on the analysis results using a generative AI model;

[1433] means for providing the generated data to a user;

[1434] A system including:

[1435] (Claim 2)

[1436] 10. The system of claim 1, wherein the camera-mounted device comprises a head-mounted device.

[1437] (Claim 3)

[1438] 10. The system of claim 1, wherein the means for transmitting data uses wireless communication.

[1439] "Application Example 1"

[1440] (Claim 1)

[1441] means for acquiring image data from a camera attached to a head-mounted device;

[1442] means for transmitting the acquired image data and position data to a server;

[1443] a means for the server to analyze the transmitted image data and position data to recognize the surrounding situation;

[1444] means for generating audio data or visual data for notifying a user based on the analysis results;

[1445] means for providing the generated data to a user;

[1446] A means for detecting traffic lights, pedestrians, and obstacles and generating information to support safe driving;

[1447] A system including:

[1448] (Claim 2)

[1449] 10. The system of claim 1, wherein the camera-mounted device comprises a frame of eyeglasses.

[1450] (Claim 3)

[1451] 10. The system of claim 1, wherein the means for transmitting data uses wireless communication.

[1452] "Example 2: Combining Emotion Engines"

[1453] (Claim 1)

[1454] means for acquiring image data from a camera attached to a head-mounted device;

[1455] means for transmitting the acquired image data, position data, voice data, and biometric data to a server;

[1456] means for analyzing the transmitted image data, location data, voice data, and biometric data to recognize the surrounding situation and identify the emotional state of the user;

[1457] means for generating audio data or visual data for notifying a user based on the analysis results;

[1458] means for providing the generated data to a user;

[1459] A system including:

[1460] (Claim 2)

[1461] 10. The system of claim 1, wherein the camera-mounted device includes a wearable support.

[1462] (Claim 3)

[1463] 10. The system of claim 1, wherein the means for transmitting data uses wireless communication.

[1464] "Application example 2 when combining emotion engines"

[1465] (Claim 1)

[1466] means for acquiring image data from an imaging device attached to the head-mounted device;

[1467] means for transmitting the acquired image data, position data, voice data, and biometric data to a server;

[1468] a means for the server to analyze the transmitted image data, location data, voice data, and biometric data to recognize the surrounding situation and the emotional state of the user;

[1469] means for generating audio data or visual data for notifying a user based on the analysis results;

[1470] means for providing the generated data to a user;

[1471] A system including:

[1472] (Claim 2)

[1473] 10. The system of claim 1, wherein the device to which the imaging device is attached comprises a frame of eyeglasses.

[1474] (Claim 3)

[1475] 10. The system of claim 1, wherein the means for transmitting data uses wireless communication.

[1476] (Claim 4)

[1477] 2. The system according to claim 1, wherein the server further comprises means for generating audio guidance appropriate to the emotional state of the user based on the analysis result.

[1478] (Claim 5)

[1479] 5. The system of claim 4, wherein said audio data includes means for analyzing the tone of a user's voice.

[1480] (Claim 6)

[1481] The system of claim 1 , wherein the biometric data includes heart rate. [Explanation of symbols]

[1482] 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 from a camera attached to a head-mounted device; means for transmitting the acquired image data and position data to a server; a means for the server to analyze the transmitted image data and position data to recognize the surrounding situation; means for generating audio data or visual data for notifying a user based on the analysis results; means for providing the generated data to a user; A system including:

2. The system of claim 1 , wherein the camera-mounted device comprises an eyeglass frame.

3. 2. The system of claim 1, wherein the means for transmitting data uses wireless communication.

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