system

Abstract

Provide a system. A means for collecting environmental information using a camera; a means for acquiring surrounding physical data using sensors; means including artificial intelligence for analyzing data obtained from the cameras and sensors; means for transmitting an action determined based on the analysis result to the child device; means for providing feedback to a user based on the signals received by the child device; A system including:

Description

[Technical Field]

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

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

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

[0004] People with physical disabilities and the elderly who are both visually and hearing impaired have difficulty crossing crosswalks safely at night, in heavy traffic, or at crowded crosswalks. In such situations, they are unable to visually or aurally recognize the status of traffic lights or the movement of surrounding vehicles, limiting their options for safely crossing the street. Therefore, there is a need for a support system that can solve this problem and enable these people to cross crosswalks safely. [Means for solving the problem]

[0005] The present invention provides a system including a means for collecting environmental information using a camera, a means for acquiring surrounding physical data using a sensor, a means including artificial intelligence for analyzing the data acquired from the camera and sensor, a means for transmitting an action determined based on the analysis results to a child device, and a means for providing feedback to a user based on signals received by the child device. Specifically, the system collects environmental information using a camera and a sensor, and analyzes the data using artificial intelligence to determine the status of traffic lights and the movement of surrounding vehicles. Based on the analysis results, the system determines the timing for crossing the intersection and transmits this information as a special signal to the child device. Based on the received signal, the child device provides feedback to the user through vibrations and audio, allowing even people with visual and hearing impairments to cross the crosswalk safely.

[0006] A "camera" is a device that converts light into an electrical signal and records it as video data.

[0007] "Environmental information" is a general term for the physical and visual data of the surroundings collected through cameras and sensors.

[0008] A "sensor" is a device that converts physical quantities into electrical signals, and can obtain information about the surrounding environment, such as temperature, distance, and sound.

[0009] "Artificial intelligence" refers to algorithms or systems that analyze large amounts of data and perform pattern recognition and predictions.

[0010] "Analysis results" are judgments and conclusions derived by analyzing data collected by artificial intelligence.

[0011] A "child device" is a device that receives signals sent from the server and provides appropriate feedback to the user.

[0012] A "special signal" is a signal sent from a server to a child device to convey a specific operation or status.

[0013] "Feedback" refers to information or instructions that the slave device provides to the user in the form of vibration, sound, or the like.

[0014] An "action" is a specific instruction for action determined based on the analysis results of artificial intelligence. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or when there are many people. This system is composed of a server and a slave device, and operates as follows.

[0037] 1. Collecting environment information (server):

[0038] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0039] 2. Data analysis and decision making (server):

[0040] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data and determines the color of the traffic light and the speed and distance of approaching vehicles. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the action of "cross." On the other hand, if the light is red or a vehicle is approaching, it decides to take the action of "wait."

[0041] 3. Information transmission (server → child device):

[0042] The server generates a specific signal based on the determined action and sends it to the child device: in the case of a "cross" action, a signal is sent to encourage the child device to cross, and in the case of a "wait" action, a signal is sent to encourage the child device to wait.

[0043] 4. Signal reception and feedback (child device):

[0044] The child device receives a signal from the server and provides feedback to the user based on the received signal. Specifically, when the child device receives a "cross" signal, it vibrates for a long time to let the user know when to cross. When the child device receives a "wait" signal, it vibrates for a short time and, in some cases, plays a voice message to let the user know to wait.

[0045] Specific examples

[0046] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles have stopped, determines that it is safe, and sends a "cross" signal to the sub-device. The user wearing the sub-device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the sub-device instructs the user to wait with a short vibration or audio.

[0047] This system allows people with visual and hearing impairments to walk safely.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0051] Step 2:

[0052] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0053] Step 3:

[0054] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the road, and conversely, if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0055] Step 4:

[0056] The server generates a special signal corresponding to the determined action and sends it to the child device, for example, a signal that generates a long vibration for the "cross" action, and a signal that generates a short vibration or a voice instruction for the "wait" action.

[0057] Step 5:

[0058] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0059] Step 6:

[0060] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0061] Step 7:

[0062] The user receives feedback from the child device. The user feels vibration and audio feedback and takes action accordingly, such as crossing the street or waiting.

[0063] This series of processes allows visually and hearing impaired people to cross crosswalks safely, even at night, in heavy traffic, or when there are a lot of people.

[0064] Example 1

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

[0066] The present invention aims to provide a system that enables visually or hearing-impaired people and the elderly to cross intersections safely at night or at busy intersections. Conventional systems lack the advanced analytical capabilities to accurately collect information about the surrounding environment and determine safety. As a result, it has been difficult for visually or hearing-impaired users to cross the crosswalk at the correct time. Therefore, to solve this problem, a system with advanced environmental information collection and analysis capabilities is needed.

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

[0068] In this invention, the server includes means for collecting environmental information using a device for acquiring video data and a device for acquiring sensor data, means having artificial intelligence for analyzing the collected data, means for generating a signal corresponding to an action determined based on the analysis result and transmitting it to a slave device, and means for providing feedback to the user based on the signal received by the slave device. This enables the collection and real-time analysis of detailed environmental information to help visually or hearing-impaired users cross the crosswalk safely, ensuring safe crossing at the correct time.

[0069] "Video data" refers to visual information around intersections and crosswalks acquired using devices such as cameras.

[0070] "Sensor data" refers to numerical information obtained from sensors about the surrounding physical conditions, such as vehicle speed, distance, and presence confirmation.

[0071] "Device" is a general term for equipment used to acquire video data or sensor data, including cameras and distance measurement devices.

[0072] "Artificial intelligence" refers to algorithms and models that analyze collected video and sensor data and determine environmental conditions.

[0073] A "child device" is a mobile device that receives signals transmitted from the server and provides feedback to the user.

[0074] An "action" is a user behavior instruction determined by the server based on the analysis results, and refers to either "cross" or "wait."

[0075] "Feedback" refers to information that a slave device provides to a user, and is a means of conveying instructions to the user using vibrations, sound, or the like.

[0076] This invention is a system that enables the visually and hearing impaired and the elderly to cross intersections safely at night or at busy intersections. The system is mainly composed of a server and a slave device, each of which plays a specific role.

[0077] Collecting environmental information (server)

[0078] The server uses cameras to acquire video data around intersections and crosswalks, allowing it to collect environmental information in real time. The server also uses various sensors (e.g., LiDAR and ultrasonic sensors) to acquire sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. This data is temporarily stored in the server's database.

[0079] Data analysis and judgment (server)

[0080] The server analyzes the collected video data and sensor data using artificial intelligence (for example, deep learning models such as TENSORFLOW (registered trademark) and PyTorch). As a result, it identifies the color of the traffic light and determines the vehicle's speed and distance. For example, if the light is green or the vehicle's speed is 30 km / h or less, the server determines that it is safe to cross the street and takes the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it takes the "wait" action.

[0081] Information transmission (server → child device)

[0082] Based on the analysis result, the server generates a specific signal. For example, in the case of a "cross" action, it generates a specific bit pattern (e.g., '1010') and sends it to the child device through the Wi-Fi module. On the other hand, in the case of a "wait" action, it generates a different bit pattern and sends it through the Wi-Fi module.

[0083] Signal reception and feedback (child device)

[0084] The child device receives the signal sent from the server. Based on that signal, the child device provides feedback to the user. When it receives the "cross" signal, it vibrates for a long time (e.g., 3 seconds) to let the user know when to cross. On the other hand, when it receives the "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or sends a voice message saying "Please wait."

[0085] Specific examples

[0086] For example, consider a case where a user uses this system at a busy intersection at night. The server uses a camera and LiDAR to collect environmental information and analyzes that data in real time using an AI model (e.g., TensorFlow). The moment the light turns green, the server determines that it is safe to cross after confirming that surrounding vehicles have stopped, and sends a "cross" signal to the child device. The user wearing the child device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the child device instructs the user to wait with a short vibration or sound.

[0087] Prompt Sentence Examples

[0088] The user is blind and deaf and needs help crossing intersections safely. The server collects environmental information using cameras and sensors and analyzes it in real time. When the light is green and a car is stopped, the server generates a "cross" signal and sends it to the child device. The child device vibrates long to let the user know it's time to cross. On the other hand, when the light is red or a vehicle is approaching, the server generates a "wait" signal and sends it to the child device. The child device vibrates shortly or sounds to tell the user to wait.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1: Gather environmental information

[0091] The server activates the camera and captures video of the intersection and crosswalk area in real time. The server collects video data as input and temporarily stores it. The server also activates sensors to obtain sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. The server then temporarily stores the video and sensor data and collects the basic data needed to proceed to the next analysis step. Specifically, the server uses an HD camera to capture video at 30 frames per second and a LiDAR sensor to measure vehicle speed and distance.

[0092] Step 2: Analyze the data

[0093] The server inputs the temporarily stored video data and sensor data into an artificial intelligence model. Specifically, the video data is input into a Convolutional Neural Network (CNN) model to analyze the color of the traffic light. The sensor data is input into a Recurrent Neural Network (RNN) model to determine the vehicle's speed and distance. The output is whether the traffic light is green or red, and whether the vehicle's speed is below 30 km / h. For example, the server uses TensorFlow to run the CNN model to identify that the traffic light is green, while at the same time using the RNN model to confirm that the vehicle's speed is below 30 km / h.

[0094] Step 3: Decide on an action

[0095] Based on the analysis results, the server determines whether it is safe to cross the street. Based on this determination, the server decides on the action of "cross" or "wait." For example, if the light is green and no vehicles are approaching, the server decides on the action of "cross," while if the light is red or a vehicle is approaching, the server decides on the action of "wait." In this way, the server prepares the signal to be sent to the child device in the next step.

[0096] Step 4: Generate the signal

[0097] The server generates a corresponding signal based on the determined action. Specifically, for a "cross" action, it generates a specific bit pattern (e.g., '1010'), and for a "wait" action, it generates a different bit pattern (e.g., '0101'). This is then sent to the child device via the Wi-Fi module. For example, the server generates the bit pattern '1010' for the "cross" action and sends it to the child device via the Wi-Fi module.

[0098] Step 5: Receiving a signal

[0099] The child device receives the signal sent from the server. Specifically, the Wi-Fi module of the child device receives the bit pattern from the server. Based on this received signal, the child device provides feedback to the user in the next step.

[0100] Step 6: User feedback

[0101] The child device provides feedback to the user based on the received signal. Specifically, when it receives a "cross" signal, it vibrates for a long time (e.g., 3 seconds) to notify the user that it is time to cross. When it receives a "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or gives a voice message saying "Please wait." For example, the child device may activate its vibration motor for 3 seconds to notify the user of the "cross" signal.

[0102] Through these steps, the system can provide visually and hearing impaired users with detailed environmental information collection, real-time analysis, and feedback to help them cross the crosswalk safely.

[0103] (Application example 1)

[0104] 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."

[0105] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to cross pedestrian crossings safely, and aims to provide a means for them to cross safely even in today's busy traffic conditions and nighttime environments. In particular, there is a need for a method that can grasp the situation at intersections and pedestrian crossings in real time and provide information to users effectively.

[0106] 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.

[0107] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing the data acquired from the camera and the sensor, means for transmitting an action determined based on the analysis result to the slave device, means for providing feedback to the user based on the signal received by the slave device, and means for providing visual and tactile feedback to the user using a smart device, thereby enabling visually and hearing impaired users to grasp the status of traffic lights and vehicle movements in real time and cross the crosswalk safely.

[0108] A "camera" is a device for capturing images and processing the data as digital information.

[0109] "Environmental information" is information about the user's surroundings, including the surrounding conditions of crosswalks and intersections.

[0110] A "sensor" is a device that detects physical data and captures it as a digital signal. Examples include distance sensors and ultrasonic sensors.

[0111] "Physical data" is data about the physical characteristics of the environment, such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0112] "Artificial intelligence" is a technology that allows computers to mimic human intelligence and analyze data and make decisions. Specifically, it uses machine learning models.

[0113] "Analysis results" are the conclusions or judgments reached after AI processes and analyzes data obtained from cameras and sensors.

[0114] An "action" is a specific movement or instruction that is triggered based on the analysis results. Examples include "cross" and "wait."

[0115] A "child device" is a device that receives signals sent from the server and provides feedback to the user. Specifically, this applies to smart glasses and smartphones.

[0116] "Feedback" is information or instructions provided to the user. Examples include vibration, sound, and visual indication.

[0117] A "smart device" is a device that has advanced computing power and can connect to a network. Examples include smart glasses and smartphones.

[0118] "Visual feedback" is any form of feedback that visually conveys information to a user, such as a display or a light.

[0119] "Haptic feedback" is a form of feedback that conveys information to the user through physical vibration or pressure. An example is the vibration feature on a device.

[0120] A "system" is a set of devices and software that enable multiple elements to work together to achieve a specific function. The system of this invention is composed of cameras, sensors, servers, artificial intelligence, child devices, etc.

[0121] The present invention is an advanced assistance system for visually and hearing impaired people and elderly people to cross pedestrian crossings safely, and is implemented in the following steps.

[0122] 1. Means of collecting environmental information

[0123] The server uses a camera to capture images of the area around the crosswalk and collect environmental information in real time. The camera has high resolution and provides a wide field of view.

[0124] It also uses sensors to acquire physical data about the surroundings, including LiDAR and ultrasonic sensors, to measure with high precision physical data such as vehicle movement, pedestrian presence, and traffic light status.

[0125] 2. Data analysis methods

[0126] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. This analysis uses machine learning libraries such as TensorFlow and image processing libraries such as OpenCV. The analysis is performed in real time to recognize the status of traffic lights and the distance and speed of vehicles.

[0127] 3. How to decide on actions based on analysis results

[0128] The server determines the action to take based on the analysis results, such as "cross" or "wait." The action is determined as "cross" if the traffic light is green and no vehicles are approaching, and as "wait" if the traffic light is red or a vehicle is approaching.

[0129] 4. How to send an action

[0130] The server sends the determined action to the child device, which is a smart device (e.g., smart glasses or a smartphone) that receives the signal using wireless communication technology.

[0131] 5. Means of providing feedback

[0132] The child device provides feedback to the user based on the received signal. The feedback is displayed visually on the smart device's display and also provides tactile feedback using a vibration motor. Specifically, when a "cross" signal is received, a long vibration and a visual display indicate when to cross, and when a "wait" signal is received, a short vibration and a visual display indicate that the user should wait.

[0133] Hardware and software used

[0134] Hardware: high-resolution cameras, LiDAR sensors, ultrasonic sensors, high-performance servers (with CPUs and GPUs), smart glasses, smartphones

[0135] Software: TensorFlow (machine learning library), OpenCV (image processing library), Python (data analysis and application development)

[0136] Specific examples

[0137] For example, consider a case where a user approaches a busy intersection at night. In this case, the server uses cameras and sensors to collect information about the surrounding area in real time and analyzes the traffic light status and vehicle movement. When the server confirms that the light is green and no vehicles are approaching, it sends a "cross" signal to the child device. The smart glasses worn by the user will then vibrate and display a visual indication to indicate when to cross. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, instructing the smart glasses to wait with a short vibration and a visual indication.

[0138] Example prompts for generative AI models

[0139] "When the intersection light is green and vehicles are far away, how can we use AI models and smart glasses to provide real-time feedback to visually and hearing impaired users to help them safely cross the street?"

[0140] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0141] Step 1:

[0142] The server uses a camera to collect environmental information around the crosswalk in real time. The camera captures high-resolution video and sends the data to the server as sensor data. The input is the video data from the camera, and the output is the video data stored on the server.

[0143] Step 2:

[0144] The server acquires surrounding physical data using sensors (LiDAR and ultrasonic sensors). The sensors acquire physical data such as vehicle movement, pedestrian presence, and traffic light status, and transmits this data to the server. The input is the physical data from the sensors, and the output is the physical data stored on the server.

[0145] Step 3:

[0146] The server uses artificial intelligence to analyze data acquired from cameras and sensors. It processes the data using libraries such as TensorFlow and OpenCV and analyzes environmental information. The input is data from the cameras and sensors, and the output is the analysis results (traffic signal status, estimated vehicle distance and speed). Specific operations include data preprocessing, inference using machine learning models, and postprocessing of the results.

[0147] Step 4:

[0148] The server sends an action determined based on the analysis result to the child device. The action is determined to be "cross" or "wait," and a signal is sent to the smart device using wireless communication. The input is the analysis result, and the output is the signal sent to the smart device.

[0149] Step 5:

[0150] The child device (smart device) provides feedback to the user based on the received signal. The feedback is displayed visually on the display and also tactilely using a vibration motor. For example, a "cross" signal is accompanied by a long vibration and a visual display, while a "wait" signal is accompanied by a short vibration and a visual display. The input is the signal from the server, and the output is the feedback to the user.

[0151] Step 6:

[0152] The user follows the feedback from the smart device to safely cross the crosswalk. Specifically, the user checks the display of the smart device, senses the feedback vibration, and crosses the crosswalk at the appropriate time. The input is the feedback from the smart device, and the output is the action of crossing the crosswalk safely.

[0153] 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.

[0154] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or at crowded crosswalks. This system comprises a server, a child device, and an emotion engine, and operates as follows.

[0155] 1. Collecting environment information (server):

[0156] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0157] 2. Data analysis and decision making (server):

[0158] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data, detects the color of the traffic light (red or green), and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it decides to take the "wait" action.

[0159] 3. Emotion recognition by emotion engine (server):

[0160] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's voice and facial expressions to understand whether the user is tense or relaxed. This allows the server to adapt the feedback content to the user's emotional state.

[0161] 4. Information transmission (server → child device):

[0162] The server generates a specific signal based on the determined action and the recognition result by the emotion engine and sends it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, and a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0163] 5. Signal Reception and Feedback (Child Device):

[0164] The child device receives signals from the server and provides feedback to the user based on the received signal. For example, if the child device receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If the child device receives a "wait" signal, it will vibrate for a short time and, in some cases, play a voice prompt to let the user know to wait.

[0165] Specific examples

[0166] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles are stopped, determines that it is safe, and sends a "cross" signal to the child device. At that time, the server uses an emotion engine to recognize the user's emotional state (for example, whether they are nervous). If the user is nervous, the server provides gentler feedback than usual.

[0167] The user wearing the device will feel a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, and the device will instruct the user to wait with a short vibration or voice. The feedback will also be adjusted taking into account the user's emotional state.

[0168] As described above, by combining this system with an emotion engine, we can provide users with more appropriate and reassuring feedback, enabling visually and hearing impaired people to cross the crosswalk safely.

[0169] The processing flow will be explained below.

[0170] Step 1:

[0171] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0172] Step 2:

[0173] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0174] Step 3:

[0175] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the vehicle, and if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0176] Step 4:

[0177] The server activates the emotion engine to recognize the user's emotional state. Specifically, it collects data such as voice and facial expressions through sensors and cameras in the device worn by the user to determine whether the user is tense or relaxed.

[0178] Step 5:

[0179] The server determines the final action and feedback content based on the analysis results and the emotion engine results. For example, if the user is nervous, the feedback when crossing the street may be gentler than usual to make it easier for the user to cross.

[0180] Step 6:

[0181] The server generates a special signal corresponding to the determined action and transmits it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, or a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0182] Step 7:

[0183] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0184] Step 8:

[0185] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0186] Step 9:

[0187] The user receives feedback from the child device and acts accordingly. For example, if they feel a vibration, they confirm that it is safe to cross and begin walking. On the other hand, if they receive a short vibration or voice instruction, they stop and wait.

[0188] Through this series of processes, the system can provide users with timely feedback, helping people with visual and hearing impairments cross the street safely. The addition of an emotion engine makes it possible to provide feedback that takes into account the user's emotional state, providing an even greater sense of safety and security.

[0189] Example 2

[0190] 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."

[0191] The problem is that there are not enough systems in place to solve the problems faced by the elderly and people with visual and hearing impairments when crossing the street safely, especially at night or in busy traffic environments.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0193] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means including an emotion engine for recognizing the emotional state of the user, means for transmitting a determined action to the child device based on the analysis result and the emotion recognition result, and means for providing feedback to the user based on the signal received by the child device, thereby enabling visually and hearing impaired users to cross the crosswalk safely and with peace of mind.

[0194] A "camera" is a device that captures information about the surrounding environment as an image.

[0195] A "sensor" is a device that acquires physical data about its surroundings (e.g., vehicle movement, pedestrian presence, traffic light status, etc.).

[0196] "Artificial intelligence" refers to programs and systems that analyze data obtained from cameras and sensors and make situational judgments.

[0197] An "emotion engine" is software and hardware that recognizes the user's emotional state from their voice and facial expressions.

[0198] A "child device" is a device that receives signals sent from the server and provides feedback to the user.

[0199] "Feedback" refers to instructions or information that a user receives from a system, and specifically includes vibrations and audio instructions.

[0200] The present invention relates to a system that enables physically disabled people with visual and hearing impairments and elderly people to cross pedestrian crossings safely. This system is mainly composed of a server, a child device, and an emotion engine. A specific embodiment of this system will be described in detail below.

[0201] Server functions and hardware / software used

[0202] The server operates using the following hardware and software:

[0203] Environmental information collection: The server uses cameras and sensors to collect information about the surrounding environment. In a specific example, a Hikvision camera is used, with a LiDAR sensor capturing physical data about the surroundings.

[0204] Data analysis: The server analyzes the acquired data using a generative AI model such as TensorFlow, which determines the color of the traffic light (e.g., red or green), vehicle movement, and the presence of pedestrians.

[0205] Emotion recognition using an emotion engine: The server uses Affectiva's SDK to recognize the user's emotional state. It analyzes emotions such as tension or relaxation based on the user's voice and facial expression data.

[0206] Functions of child devices

[0207] The slave device has the following functions:

[0208] Signal reception: The slave device receives a signal from the server. There are no particular restrictions on the communication method used, but Wi-Fi, Bluetooth, etc. are possible.

[0209] Providing feedback: The child device provides feedback to the user based on the received signal, for example, a long vibration for the "cross" action, a short vibration for the "wait" action, or a voice instruction. Specifically, an Arduino-based vibration motor is used.

[0210] Server Processing Details

[0211] The server performs the process in the following procedure.

[0212] 1. Environmental information collection: The server captures video data from Hikvision cameras and collects physical data using LiDAR sensors, allowing it to understand the situation at intersections and crosswalks in real time.

[0213] 2. Data analysis: The server uses the TensorFlow model to analyze the acquired video and sensor data, checking the color of traffic lights and vehicle movements to determine whether it is safe.

[0214] 3. Emotion Recognition by Emotion Engine: The server uses Affectiva's SDK to recognize the user's emotional state. For example, it analyzes whether the user is tense or relaxed.

[0215] 4. Information transmission: Based on the analysis results and emotion recognition results, the appropriate action is determined and a signal is sent to the child device.

[0216] Specific examples

[0217] For example, consider a situation where a user is using this system at a busy intersection at night. The server collects environmental information through Hikvision's camera and LiDAR sensor, and analyzes that data in real time using a TensorFlow model. The moment the light turns green, the server confirms that surrounding vehicles are stopped, and if it determines that it is safe, it sends a "cross" signal to the child device. If the server detects that the user is nervous using Affectiva's SDK, it can add settings to provide gentler feedback.

[0218] The device worn by the user generates a long vibration based on the received "cross" signal. The user feels the vibration and can cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal and provides a short vibration or sound to instruct the user to wait.

[0219] Prompt Sentence Examples

[0220] "I would like to design a system that instructs pedestrians to cross safely when vehicles are stopped at a busy intersection the moment the light turns green. Please explain in detail how the emotion engine can detect whether pedestrians are nervous and adjust the feedback accordingly."

[0221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0222] Step 1:

[0223] Collecting environmental information (server)

[0224] The server receives input from cameras and sensors to obtain streaming data. Cameras provide real-time video of the area around intersections and crosswalks. Sensors (e.g., LiDAR sensors) collect physical data such as vehicle movement, pedestrian presence, and traffic light status. This data is input to the server.

[0225] Input: Video data from cameras, physical data from sensors

[0226] Output: Video data and physical data are stored in the server's memory.

[0227] How it works: The server captures video from a Hikvision camera at 30 frames per second and obtains 3D scan data of surrounding objects from a LiDAR sensor.

[0228] Step 2:

[0229] Data analysis and judgment (server)

[0230] The server uses AI models such as TensorFlow to analyze the acquired data. First, it extracts the color of traffic lights and the location of pedestrians from the video data, and then identifies the movement and location of vehicles from the physical data. This analysis determines the current situation, such as whether the light is green and a vehicle is stopped, or whether the light is red and a vehicle is approaching.

[0231] Input: Visual and physical data collected in Step 1

[0232] Output: A decision indicating whether it is safe to cross.

[0233] How it works: The AI ​​model analyzes camera footage to detect the status of traffic lights. It uses data from sensors to determine the speed and location of surrounding vehicles. For example, if the light is green and the vehicle's speed is close to zero, it determines that it is safe and decides to "cross" the road.

[0234] Step 3:

[0235] Emotion recognition by emotion engine (server)

[0236] The server uses Affectiva's emotion recognition engine to recognize the user's emotional state from their voice and facial expression data, thereby determining whether the user is tense or relaxed and providing feedback accordingly.

[0237] Input: User's voice and facial expression data

[0238] Output: Data indicating the user's emotional state

[0239] How it works: The server inputs the user's voice and facial expression data into the Affectiva SDK, which analyzes their emotional state in real time. For example, if the user's voice is high-pitched or their facial expression shows signs of impatience, the emotion engine will determine that the user is nervous.

[0240] Step 4:

[0241] Information transmission (server → child device)

[0242] The server determines the appropriate action based on the analysis results and emotion recognition results, and sends the result to the child device. Specifically, it generates a signal including a long vibration for the "cross" action and a short vibration or voice instruction for the "wait" action.

[0243] Input: Judgment result indicating safety of crossing, emotional state of the user

[0244] Output: Signal sent to child device

[0245] Specific operation: If the result of the judgment is "cross," the server sends a signal to the child device that generates a long vibration. If the result of the judgment is "wait," the server sends a signal to generate a short vibration or a voice instruction.

[0246] Step 5:

[0247] Signal reception and feedback (child device)

[0248] The child device receives the signal sent from the server and provides feedback to the user based on the signal. For example, in the case of a "cross" signal, the child device operates a vibration motor for a long period of time to let the user know when it is time to cross.

[0249] Input: Signal from the server

[0250] Output: Feedback to the user (vibration and audio instructions)

[0251] Specific operation: The slave device analyzes the signal received from the server and controls the vibration motor equipped with Arduino. When the user receives the "cross" signal, the vibration motor generates a long vibration, allowing the user to cross the crosswalk safely.

[0252] (Application example 2)

[0253] 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."

[0254] In systems that enable the visually and hearing impaired, physically disabled, and elderly people to cross crosswalks safely at night, in heavy traffic, or at crowded crosswalks, etc., ensuring the safety and efficiency of workers, particularly in factories, requires real-time analysis of the working environment and providing appropriate feedback that takes into account the emotional state of the worker. However, conventional technologies were unable to recognize the emotional state of the worker and adjust the feedback accordingly, making it difficult to achieve both safety and comfort.

[0255] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0256] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means for transmitting actions determined based on the analysis results to the slave device, means for providing feedback to the user based on signals received by the slave device, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the feedback content based on the user's emotional state. This makes it possible to flexibly provide safety measures according to the worker's emotional state, thereby achieving both safety and comfort in the work environment.

[0257] A "camera" is a device for recording video or images.

[0258] "Environmental information" is data about the surrounding physical conditions and situations.

[0259] A "sensor" is a device for detecting and collecting physical data.

[0260] "Artificial intelligence" refers to algorithms and systems that analyze acquired data and make appropriate decisions and processes.

[0261] "Analysis results" are interpretations and conclusions obtained through data analysis.

[0262] An "action" is a specific movement or behavior that is determined based on the analysis results.

[0263] A "child device" is a device that receives signals from the server and provides feedback to the user.

[0264] "Feedback" refers to information or instructions provided to a user.

[0265] An "emotion engine" is a system or algorithm for recognizing and analyzing a user's emotional state.

[0266] "Emotional state" refers to the mental or emotional state of a user.

[0267] "Adjustment" means changing the content of the feedback to suit the user's emotional state.

[0268] This invention provides a system that can be applied to a system that enables the visually and hearing impaired and elderly to cross crosswalks safely at night or in crowded areas, thereby improving the safety and comfort of workers in factories. This system is mainly composed of cameras, sensors, artificial intelligence, an emotion engine, and sub-devices.

[0269] The server first uses cameras to collect environmental information, specifically capturing the movements of machines and workers in specific areas of the factory, and then uses sensors to acquire surrounding physical data, including temperature, vibration, and location information.

[0270] The server then uses artificial intelligence to analyze the data acquired from the cameras and sensors, detecting specific patterns and anomalies. For example, if a machine is moving and a worker is approaching, it may decide to take a "standby" (wait) action.

[0271] The emotion engine then recognizes the user's emotional state by analyzing their facial expressions and tone of voice to understand whether they are tense or relaxed.

[0272] Based on the analysis results, the server generates a specific signal based on the determined action and the recognition result of the emotion engine and sends it to the child device. For example, in the case of a "standby" action, the child device sends a signal with a short vibration or voice to inform the user to wait. On the other hand, in the case of a "safety" action, it sends a signal with a long vibration or voice to instruct the user to proceed with the work safely.

[0273] The child device provides feedback to the user based on the signal received from the server. If the emotion engine recognizes that the user is nervous, the child device provides gentler feedback (e.g., gentler voice instructions) than usual to help the user act with confidence.

[0274] The hardware used includes cameras (for example, general IP cameras), various sensors (such as temperature and vibration sensors), and a computer server. The software used includes video analysis using OpenCV and neural network models using Keras. A specific example of its use is analyzing data collected by the camera to determine safety and providing appropriate feedback to workers through audio or vibration.

[0275] Examples of prompts for generative AI models include:

[0276] "Generate code for an application that uses an analytical model of the factory environment to determine in real time whether it is safe or not, and an emotion engine to determine the emotional state of workers."

[0277] This prompt allows the AI ​​model to generate code for the system responsible for analyzing the environment and recognizing emotional states.

[0278] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0279] Step 1:

[0280] The server uses cameras to collect environmental information. Specifically, cameras are installed to monitor specific areas within the factory and capture video data in real time. This captured video data is input. The input video data is sent directly to the next step.

[0281] Step 2:

[0282] The server uses sensors to acquire surrounding physical data. For example, a temperature sensor measures the temperature inside a factory, and a vibration sensor checks the operating status of machines. This physical data is the input. The input physical data is sent to the next step along with the video data.

[0283] Step 3:

[0284] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. Specifically, it preprocesses the video data using OpenCV and inputs it into a learning model using Keras. The input is the data collected in steps 1 and 2. This data is analyzed to detect specific patterns and anomalies. The results of this analysis are sent to the next step. The output is the action of "stay safe" or "evacuate" as a result of the analysis.

[0285] Step 4:

[0286] The server uses an emotion engine to recognize the user's emotional state. Specifically, it collects and analyzes data on the user's face and voice to determine the user's emotional state. The input is the user's face and voice data. The output is the user's emotional state, such as "tension" or "relaxation," and this data is sent to the next step.

[0287] Step 5:

[0288] The server determines the appropriate action based on the analysis results and the emotional state. Specifically, if it is safe, it determines a "safe" action, and if evacuation is necessary, it determines an "evacuation" action. Also, if the emotional state is tense, it determines to provide gentler feedback. The inputs are the analysis results from step 3 and the emotional state from step 4. The output is the type of action and the feedback content.

[0289] Step 6:

[0290] The server sends the determined action and feedback content to the child device. Specifically, the feedback content is generated as a vibration or sound signal and sent to the child device. The input is the type of action and feedback content from step 5. The output is the signal sent to the child device.

[0291] Step 7:

[0292] The slave device receives the signal from the server and provides feedback to the user. Specifically, based on the received signal, it provides instructions such as "take shelter" or "safe" to the user through vibration or sound. The input is the signal sent to the slave device in step 6. The output is the vibration or sound feedback provided to the user.

[0293] 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.

[0294] 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.

[0295] 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.

[0296] [Second embodiment]

[0297] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0298] 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.

[0299] 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).

[0300] 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.

[0301] 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.

[0302] 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).

[0303] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0304] 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.

[0305] 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.

[0306] 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.

[0307] 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.

[0308] 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."

[0309] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or when there are many people. This system is composed of a server and a slave device, and operates as follows.

[0310] 1. Collecting environment information (server):

[0311] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0312] 2. Data analysis and decision making (server):

[0313] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data and determines the color of the traffic light and the speed and distance of approaching vehicles. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the action of "cross." On the other hand, if the light is red or a vehicle is approaching, it decides to take the action of "wait."

[0314] 3. Information transmission (server → child device):

[0315] The server generates a specific signal based on the determined action and sends it to the child device: in the case of a "cross" action, a signal is sent to encourage the child device to cross, and in the case of a "wait" action, a signal is sent to encourage the child device to wait.

[0316] 4. Signal reception and feedback (child device):

[0317] The child device receives a signal from the server and provides feedback to the user based on the received signal. Specifically, when the child device receives a "cross" signal, it vibrates for a long time to let the user know when to cross. When the child device receives a "wait" signal, it vibrates for a short time and, in some cases, plays a voice message to let the user know to wait.

[0318] Specific examples

[0319] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles have stopped, determines that it is safe, and sends a "cross" signal to the sub-device. The user wearing the sub-device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the sub-device instructs the user to wait with a short vibration or audio.

[0320] This system allows people with visual and hearing impairments to walk safely.

[0321] The processing flow will be explained below.

[0322] Step 1:

[0323] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0324] Step 2:

[0325] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0326] Step 3:

[0327] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the road, and conversely, if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0328] Step 4:

[0329] The server generates a special signal corresponding to the determined action and sends it to the child device, for example, a signal that generates a long vibration for the "cross" action, and a signal that generates a short vibration or a voice instruction for the "wait" action.

[0330] Step 5:

[0331] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0332] Step 6:

[0333] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0334] Step 7:

[0335] The user receives feedback from the child device. The user feels vibration and audio feedback and takes action accordingly, such as crossing the street or waiting.

[0336] This series of processes allows visually and hearing impaired people to cross crosswalks safely, even at night, in heavy traffic, or when there are a lot of people.

[0337] Example 1

[0338] 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."

[0339] The present invention aims to provide a system that enables visually or hearing-impaired people and the elderly to cross intersections safely at night or at busy intersections. Conventional systems lack the advanced analytical capabilities to accurately collect information about the surrounding environment and determine safety. As a result, it has been difficult for visually or hearing-impaired users to cross the crosswalk at the correct time. Therefore, to solve this problem, a system with advanced environmental information collection and analysis capabilities is needed.

[0340] 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.

[0341] In this invention, the server includes means for collecting environmental information using a device for acquiring video data and a device for acquiring sensor data, means having artificial intelligence for analyzing the collected data, means for generating a signal corresponding to an action determined based on the analysis result and transmitting it to a slave device, and means for providing feedback to the user based on the signal received by the slave device. This enables the collection and real-time analysis of detailed environmental information to help visually or hearing-impaired users cross the crosswalk safely, ensuring safe crossing at the correct time.

[0342] "Video data" refers to visual information around intersections and crosswalks acquired using devices such as cameras.

[0343] "Sensor data" refers to numerical information obtained from sensors about the surrounding physical conditions, such as vehicle speed, distance, and presence confirmation.

[0344] "Device" is a general term for equipment used to acquire video data or sensor data, including cameras and distance measurement devices.

[0345] "Artificial intelligence" refers to algorithms and models that analyze collected video and sensor data and determine environmental conditions.

[0346] A "child device" is a mobile device that receives signals transmitted from the server and provides feedback to the user.

[0347] An "action" is a user behavior instruction determined by the server based on the analysis results, and refers to either "cross" or "wait."

[0348] "Feedback" refers to information that a slave device provides to a user, and is a means of conveying instructions to the user using vibrations, sound, or the like.

[0349] This invention is a system that enables the visually and hearing impaired and the elderly to cross intersections safely at night or at busy intersections. The system is mainly composed of a server and a slave device, each of which plays a specific role.

[0350] Collecting environmental information (server)

[0351] The server uses cameras to acquire video data around intersections and crosswalks, allowing it to collect environmental information in real time. The server also uses various sensors (e.g., LiDAR and ultrasonic sensors) to acquire sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. This data is temporarily stored in the server's database.

[0352] Data analysis and judgment (server)

[0353] The server analyzes the collected video data and sensor data using artificial intelligence (for example, deep learning models such as TensorFlow and PyTorch). As a result, it identifies the color of the traffic light and determines the vehicle's speed and distance. For example, if the light is green or the vehicle's speed is 30 km / h or less, the server determines that it is safe to cross the street and takes the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it takes the "wait" action.

[0354] Information transmission (server → child device)

[0355] Based on the analysis result, the server generates a specific signal. For example, in the case of a "cross" action, it generates a specific bit pattern (e.g., '1010') and sends it to the child device through the Wi-Fi module. On the other hand, in the case of a "wait" action, it generates a different bit pattern and sends it through the Wi-Fi module.

[0356] Signal reception and feedback (child device)

[0357] The child device receives the signal sent from the server. Based on that signal, the child device provides feedback to the user. When it receives the "cross" signal, it vibrates for a long time (e.g., 3 seconds) to let the user know when to cross. On the other hand, when it receives the "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or sends a voice message saying "Please wait."

[0358] Specific examples

[0359] For example, consider a case where a user uses this system at a busy intersection at night. The server uses a camera and LiDAR to collect environmental information and analyzes that data in real time using an AI model (e.g., TensorFlow). The moment the light turns green, the server determines that it is safe to cross after confirming that surrounding vehicles have stopped, and sends a "cross" signal to the child device. The user wearing the child device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the child device instructs the user to wait with a short vibration or sound.

[0360] Prompt Sentence Examples

[0361] The user is blind and deaf and needs help crossing intersections safely. The server collects environmental information using cameras and sensors and analyzes it in real time. When the light is green and a car is stopped, the server generates a "cross" signal and sends it to the child device. The child device vibrates long to let the user know it's time to cross. On the other hand, when the light is red or a vehicle is approaching, the server generates a "wait" signal and sends it to the child device. The child device vibrates shortly or sounds to tell the user to wait.

[0362] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0363] Step 1: Gather environmental information

[0364] The server activates the camera and captures video of the intersection and crosswalk area in real time. The server collects video data as input and temporarily stores it. The server also activates sensors to obtain sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. The server then temporarily stores the video and sensor data and collects the basic data needed to proceed to the next analysis step. Specifically, the server uses an HD camera to capture video at 30 frames per second and a LiDAR sensor to measure vehicle speed and distance.

[0365] Step 2: Analyze the data

[0366] The server inputs the temporarily stored video data and sensor data into an artificial intelligence model. Specifically, the video data is input into a Convolutional Neural Network (CNN) model to analyze the color of the traffic light. The sensor data is input into a Recurrent Neural Network (RNN) model to determine the vehicle's speed and distance. The output is whether the traffic light is green or red, and whether the vehicle's speed is below 30 km / h. For example, the server uses TensorFlow to run the CNN model to identify that the traffic light is green, while at the same time using the RNN model to confirm that the vehicle's speed is below 30 km / h.

[0367] Step 3: Decide on an action

[0368] Based on the analysis results, the server determines whether it is safe to cross the street. Based on this determination, the server decides on the action of "cross" or "wait." For example, if the light is green and no vehicles are approaching, the server decides on the action of "cross," while if the light is red or a vehicle is approaching, the server decides on the action of "wait." In this way, the server prepares the signal to be sent to the child device in the next step.

[0369] Step 4: Generate the signal

[0370] The server generates a corresponding signal based on the determined action. Specifically, for a "cross" action, it generates a specific bit pattern (e.g., '1010'), and for a "wait" action, it generates a different bit pattern (e.g., '0101'). This is then sent to the child device via the Wi-Fi module. For example, the server generates the bit pattern '1010' for the "cross" action and sends it to the child device via the Wi-Fi module.

[0371] Step 5: Receiving a signal

[0372] The child device receives the signal sent from the server. Specifically, the Wi-Fi module of the child device receives the bit pattern from the server. Based on this received signal, the child device provides feedback to the user in the next step.

[0373] Step 6: User feedback

[0374] The child device provides feedback to the user based on the received signal. Specifically, when it receives a "cross" signal, it vibrates for a long time (e.g., 3 seconds) to notify the user that it is time to cross. When it receives a "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or gives a voice message saying "Please wait." For example, the child device may activate its vibration motor for 3 seconds to notify the user of the "cross" signal.

[0375] Through these steps, the system can provide visually and hearing impaired users with detailed environmental information collection, real-time analysis, and feedback to help them cross the crosswalk safely.

[0376] (Application example 1)

[0377] 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."

[0378] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to cross pedestrian crossings safely, and aims to provide a means for them to cross safely even in today's busy traffic conditions and nighttime environments. In particular, there is a need for a method that can grasp the situation at intersections and pedestrian crossings in real time and provide information to users effectively.

[0379] 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.

[0380] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing the data acquired from the camera and the sensor, means for transmitting an action determined based on the analysis result to the slave device, means for providing feedback to the user based on the signal received by the slave device, and means for providing visual and tactile feedback to the user using a smart device, thereby enabling visually and hearing impaired users to grasp the status of traffic lights and vehicle movements in real time and cross the crosswalk safely.

[0381] A "camera" is a device for capturing images and processing the data as digital information.

[0382] "Environmental information" is information about the user's surroundings, including the surrounding conditions of crosswalks and intersections.

[0383] A "sensor" is a device that detects physical data and captures it as a digital signal. Examples include distance sensors and ultrasonic sensors.

[0384] "Physical data" is data about the physical characteristics of the environment, such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0385] "Artificial intelligence" is a technology that allows computers to mimic human intelligence and analyze data and make decisions. Specifically, it uses machine learning models.

[0386] "Analysis results" are the conclusions or judgments reached after AI processes and analyzes data obtained from cameras and sensors.

[0387] An "action" is a specific movement or instruction that is triggered based on the analysis results. Examples include "cross" and "wait."

[0388] A "child device" is a device that receives signals sent from the server and provides feedback to the user. Specifically, this applies to smart glasses and smartphones.

[0389] "Feedback" is information or instructions provided to the user. Examples include vibration, sound, and visual indication.

[0390] A "smart device" is a device that has advanced computing power and can connect to a network. Examples include smart glasses and smartphones.

[0391] "Visual feedback" is any form of feedback that visually conveys information to a user, such as a display or a light.

[0392] "Haptic feedback" is a form of feedback that conveys information to the user through physical vibration or pressure. An example is the vibration feature on a device.

[0393] A "system" is a set of devices and software that enable multiple elements to work together to achieve a specific function. The system of this invention is composed of cameras, sensors, servers, artificial intelligence, child devices, etc.

[0394] The present invention is an advanced assistance system for visually and hearing impaired people and elderly people to cross pedestrian crossings safely, and is implemented in the following steps.

[0395] 1. Means of collecting environmental information

[0396] The server uses a camera to capture images of the area around the crosswalk and collect environmental information in real time. The camera has high resolution and provides a wide field of view.

[0397] It also uses sensors to acquire physical data about the surroundings, including LiDAR and ultrasonic sensors, to measure with high precision physical data such as vehicle movement, pedestrian presence, and traffic light status.

[0398] 2. Data analysis methods

[0399] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. This analysis uses machine learning libraries such as TensorFlow and image processing libraries such as OpenCV. The analysis is performed in real time to recognize the status of traffic lights and the distance and speed of vehicles.

[0400] 3. How to decide on actions based on analysis results

[0401] The server determines the action to take based on the analysis results, such as "cross" or "wait." The action is determined as "cross" if the traffic light is green and no vehicles are approaching, and as "wait" if the traffic light is red or a vehicle is approaching.

[0402] 4. How to send an action

[0403] The server sends the determined action to the child device, which is a smart device (e.g., smart glasses or a smartphone) that receives the signal using wireless communication technology.

[0404] 5. Means of providing feedback

[0405] The child device provides feedback to the user based on the received signal. The feedback is displayed visually on the smart device's display and also provides tactile feedback using a vibration motor. Specifically, when a "cross" signal is received, a long vibration and a visual display indicate when to cross, and when a "wait" signal is received, a short vibration and a visual display indicate that the user should wait.

[0406] Hardware and software used

[0407] Hardware: high-resolution cameras, LiDAR sensors, ultrasonic sensors, high-performance servers (with CPUs and GPUs), smart glasses, smartphones

[0408] Software: TensorFlow (machine learning library), OpenCV (image processing library), Python (data analysis and application development)

[0409] Specific examples

[0410] For example, consider a case where a user approaches a busy intersection at night. In this case, the server uses cameras and sensors to collect information about the surrounding area in real time and analyzes the traffic light status and vehicle movement. When the server confirms that the light is green and no vehicles are approaching, it sends a "cross" signal to the child device. The smart glasses worn by the user will then vibrate and display a visual indication to indicate when to cross. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, instructing the smart glasses to wait with a short vibration and a visual indication.

[0411] Example prompts for generative AI models

[0412] "When the intersection light is green and vehicles are far away, how can we use AI models and smart glasses to provide real-time feedback to visually and hearing impaired users to help them safely cross the street?"

[0413] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0414] Step 1:

[0415] The server uses a camera to collect environmental information around the crosswalk in real time. The camera captures high-resolution video and sends the data to the server as sensor data. The input is the video data from the camera, and the output is the video data stored on the server.

[0416] Step 2:

[0417] The server acquires surrounding physical data using sensors (LiDAR and ultrasonic sensors). The sensors acquire physical data such as vehicle movement, pedestrian presence, and traffic light status, and transmits this data to the server. The input is the physical data from the sensors, and the output is the physical data stored on the server.

[0418] Step 3:

[0419] The server uses artificial intelligence to analyze data acquired from cameras and sensors. It processes the data using libraries such as TensorFlow and OpenCV and analyzes environmental information. The input is data from the cameras and sensors, and the output is the analysis results (traffic signal status, estimated vehicle distance and speed). Specific operations include data preprocessing, inference using machine learning models, and postprocessing of the results.

[0420] Step 4:

[0421] The server sends an action determined based on the analysis result to the child device. The action is determined to be "cross" or "wait," and a signal is sent to the smart device using wireless communication. The input is the analysis result, and the output is the signal sent to the smart device.

[0422] Step 5:

[0423] The child device (smart device) provides feedback to the user based on the received signal. The feedback is displayed visually on the display and also tactilely using a vibration motor. For example, a "cross" signal is accompanied by a long vibration and a visual display, while a "wait" signal is accompanied by a short vibration and a visual display. The input is the signal from the server, and the output is the feedback to the user.

[0424] Step 6:

[0425] The user follows the feedback from the smart device to safely cross the crosswalk. Specifically, the user checks the display of the smart device, senses the feedback vibration, and crosses the crosswalk at the appropriate time. The input is the feedback from the smart device, and the output is the action of crossing the crosswalk safely.

[0426] 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.

[0427] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or at crowded crosswalks. This system comprises a server, a child device, and an emotion engine, and operates as follows.

[0428] 1. Collecting environment information (server):

[0429] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0430] 2. Data analysis and decision making (server):

[0431] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data, detects the color of the traffic light (red or green), and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it decides to take the "wait" action.

[0432] 3. Emotion recognition by emotion engine (server):

[0433] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's voice and facial expressions to understand whether the user is tense or relaxed. This allows the server to adapt the feedback content to the user's emotional state.

[0434] 4. Information transmission (server → child device):

[0435] The server generates a specific signal based on the determined action and the recognition result by the emotion engine and sends it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, and a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0436] 5. Signal Reception and Feedback (Child Device):

[0437] The child device receives signals from the server and provides feedback to the user based on the received signal. For example, if the child device receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If the child device receives a "wait" signal, it will vibrate for a short time and, in some cases, play a voice prompt to let the user know to wait.

[0438] Specific examples

[0439] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles are stopped, determines that it is safe, and sends a "cross" signal to the child device. At that time, the server uses an emotion engine to recognize the user's emotional state (for example, whether they are nervous). If the user is nervous, the server provides gentler feedback than usual.

[0440] The user wearing the device will feel a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, and the device will instruct the user to wait with a short vibration or voice. The feedback will also be adjusted taking into account the user's emotional state.

[0441] As described above, by combining this system with an emotion engine, we can provide users with more appropriate and reassuring feedback, enabling visually and hearing impaired people to cross the crosswalk safely.

[0442] The processing flow will be explained below.

[0443] Step 1:

[0444] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0445] Step 2:

[0446] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0447] Step 3:

[0448] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the vehicle, and if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0449] Step 4:

[0450] The server activates the emotion engine to recognize the user's emotional state. Specifically, it collects data such as voice and facial expressions through sensors and cameras in the device worn by the user to determine whether the user is tense or relaxed.

[0451] Step 5:

[0452] The server determines the final action and feedback content based on the analysis results and the emotion engine results. For example, if the user is nervous, the feedback when crossing the street may be gentler than usual to make it easier for the user to cross.

[0453] Step 6:

[0454] The server generates a special signal corresponding to the determined action and transmits it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, or a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0455] Step 7:

[0456] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0457] Step 8:

[0458] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0459] Step 9:

[0460] The user receives feedback from the child device and acts accordingly. For example, if they feel a vibration, they confirm that it is safe to cross and begin walking. On the other hand, if they receive a short vibration or voice instruction, they stop and wait.

[0461] Through this series of processes, the system can provide users with timely feedback, helping people with visual and hearing impairments cross the street safely. The addition of an emotion engine makes it possible to provide feedback that takes into account the user's emotional state, providing an even greater sense of safety and security.

[0462] Example 2

[0463] 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."

[0464] The problem is that there are not enough systems in place to solve the problems faced by the elderly and people with visual and hearing impairments when crossing the street safely, especially at night or in busy traffic environments.

[0465] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0466] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means including an emotion engine for recognizing the emotional state of the user, means for transmitting a determined action to the child device based on the analysis result and the emotion recognition result, and means for providing feedback to the user based on the signal received by the child device, thereby enabling visually and hearing impaired users to cross the crosswalk safely and with peace of mind.

[0467] A "camera" is a device that captures information about the surrounding environment as an image.

[0468] A "sensor" is a device that acquires physical data about its surroundings (e.g., vehicle movement, pedestrian presence, traffic light status, etc.).

[0469] "Artificial intelligence" refers to programs and systems that analyze data obtained from cameras and sensors and make situational judgments.

[0470] An "emotion engine" is software and hardware that recognizes the user's emotional state from their voice and facial expressions.

[0471] A "child device" is a device that receives signals sent from the server and provides feedback to the user.

[0472] "Feedback" refers to instructions or information that a user receives from a system, and specifically includes vibrations and audio instructions.

[0473] The present invention relates to a system that enables physically disabled people with visual and hearing impairments and elderly people to cross pedestrian crossings safely. This system is mainly composed of a server, a child device, and an emotion engine. A specific embodiment of this system will be described in detail below.

[0474] Server functions and hardware / software used

[0475] The server operates using the following hardware and software:

[0476] Environmental information collection: The server uses cameras and sensors to collect information about the surrounding environment. In a specific example, a Hikvision camera is used, with a LiDAR sensor capturing physical data about the surroundings.

[0477] Data analysis: The server analyzes the acquired data using a generative AI model such as TensorFlow, which determines the color of the traffic light (e.g., red or green), vehicle movement, and the presence of pedestrians.

[0478] Emotion recognition using an emotion engine: The server uses Affectiva's SDK to recognize the user's emotional state. It analyzes emotions such as tension or relaxation based on the user's voice and facial expression data.

[0479] Functions of child devices

[0480] The slave device has the following functions:

[0481] Signal reception: The slave device receives a signal from the server. There are no particular restrictions on the communication method used, but Wi-Fi, Bluetooth, etc. are possible.

[0482] Providing feedback: The child device provides feedback to the user based on the received signal, for example, a long vibration for the "cross" action, a short vibration for the "wait" action, or a voice instruction. Specifically, an Arduino-based vibration motor is used.

[0483] Server Processing Details

[0484] The server performs the process in the following procedure.

[0485] 1. Environmental information collection: The server captures video data from Hikvision cameras and collects physical data using LiDAR sensors, allowing it to understand the situation at intersections and crosswalks in real time.

[0486] 2. Data analysis: The server uses the TensorFlow model to analyze the acquired video and sensor data, checking the color of traffic lights and vehicle movements to determine whether it is safe.

[0487] 3. Emotion Recognition by Emotion Engine: The server uses Affectiva's SDK to recognize the user's emotional state. For example, it analyzes whether the user is tense or relaxed.

[0488] 4. Information transmission: Based on the analysis results and emotion recognition results, the appropriate action is determined and a signal is sent to the child device.

[0489] Specific examples

[0490] For example, consider a situation where a user is using this system at a busy intersection at night. The server collects environmental information through Hikvision's camera and LiDAR sensor, and analyzes that data in real time using a TensorFlow model. The moment the light turns green, the server confirms that surrounding vehicles are stopped, and if it determines that it is safe, it sends a "cross" signal to the child device. If the server detects that the user is nervous using Affectiva's SDK, it can add settings to provide gentler feedback.

[0491] The device worn by the user generates a long vibration based on the received "cross" signal. The user feels the vibration and can cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal and provides a short vibration or sound to instruct the user to wait.

[0492] Prompt Sentence Examples

[0493] "I would like to design a system that instructs pedestrians to cross safely when vehicles are stopped at a busy intersection the moment the light turns green. Please explain in detail how the emotion engine can detect whether pedestrians are nervous and adjust the feedback accordingly."

[0494] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0495] Step 1:

[0496] Collecting environmental information (server)

[0497] The server receives input from cameras and sensors to obtain streaming data. Cameras provide real-time video of the area around intersections and crosswalks. Sensors (e.g., LiDAR sensors) collect physical data such as vehicle movement, pedestrian presence, and traffic light status. This data is input to the server.

[0498] Input: Video data from cameras, physical data from sensors

[0499] Output: Video data and physical data are stored in the server's memory.

[0500] How it works: The server captures video from a Hikvision camera at 30 frames per second and obtains 3D scan data of surrounding objects from a LiDAR sensor.

[0501] Step 2:

[0502] Data analysis and judgment (server)

[0503] The server uses AI models such as TensorFlow to analyze the acquired data. First, it extracts the color of traffic lights and the location of pedestrians from the video data, and then identifies the movement and location of vehicles from the physical data. This analysis determines the current situation, such as whether the light is green and a vehicle is stopped, or whether the light is red and a vehicle is approaching.

[0504] Input: Visual and physical data collected in Step 1

[0505] Output: A decision indicating whether it is safe to cross.

[0506] How it works: The AI ​​model analyzes camera footage to detect the status of traffic lights. It uses data from sensors to determine the speed and location of surrounding vehicles. For example, if the light is green and the vehicle's speed is close to zero, it determines that it is safe and decides to "cross" the road.

[0507] Step 3:

[0508] Emotion recognition by emotion engine (server)

[0509] The server uses Affectiva's emotion recognition engine to recognize the user's emotional state from their voice and facial expression data, thereby determining whether the user is tense or relaxed and providing feedback accordingly.

[0510] Input: User's voice and facial expression data

[0511] Output: Data indicating the user's emotional state

[0512] How it works: The server inputs the user's voice and facial expression data into the Affectiva SDK, which analyzes their emotional state in real time. For example, if the user's voice is high-pitched or their facial expression shows signs of impatience, the emotion engine will determine that the user is nervous.

[0513] Step 4:

[0514] Information transmission (server → child device)

[0515] The server determines the appropriate action based on the analysis results and emotion recognition results, and sends the result to the child device. Specifically, it generates a signal including a long vibration for the "cross" action and a short vibration or voice instruction for the "wait" action.

[0516] Input: Judgment result indicating safety of crossing, emotional state of the user

[0517] Output: Signal sent to child device

[0518] Specific operation: If the result of the judgment is "cross," the server sends a signal to the child device that generates a long vibration. If the result of the judgment is "wait," the server sends a signal to generate a short vibration or a voice instruction.

[0519] Step 5:

[0520] Signal reception and feedback (child device)

[0521] The child device receives the signal sent from the server and provides feedback to the user based on the signal. For example, in the case of a "cross" signal, the child device operates a vibration motor for a long period of time to let the user know when it is time to cross.

[0522] Input: Signal from the server

[0523] Output: Feedback to the user (vibration and audio instructions)

[0524] Specific operation: The slave device analyzes the signal received from the server and controls the vibration motor equipped with Arduino. When the user receives the "cross" signal, the vibration motor generates a long vibration, allowing the user to cross the crosswalk safely.

[0525] (Application example 2)

[0526] 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."

[0527] In systems that enable the visually and hearing impaired, physically disabled, and elderly people to cross crosswalks safely at night, in heavy traffic, or at crowded crosswalks, etc., ensuring the safety and efficiency of workers, particularly in factories, requires real-time analysis of the working environment and providing appropriate feedback that takes into account the emotional state of the worker. However, conventional technologies were unable to recognize the emotional state of the worker and adjust the feedback accordingly, making it difficult to achieve both safety and comfort.

[0528] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0529] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means for transmitting actions determined based on the analysis results to the slave device, means for providing feedback to the user based on signals received by the slave device, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the feedback content based on the user's emotional state. This makes it possible to flexibly provide safety measures according to the worker's emotional state, thereby achieving both safety and comfort in the work environment.

[0530] A "camera" is a device for recording video or images.

[0531] "Environmental information" is data about the surrounding physical conditions and situations.

[0532] A "sensor" is a device for detecting and collecting physical data.

[0533] "Artificial intelligence" refers to algorithms and systems that analyze acquired data and make appropriate decisions and processes.

[0534] "Analysis results" are interpretations and conclusions obtained through data analysis.

[0535] An "action" is a specific movement or behavior that is determined based on the analysis results.

[0536] A "child device" is a device that receives signals from the server and provides feedback to the user.

[0537] "Feedback" refers to information or instructions provided to a user.

[0538] An "emotion engine" is a system or algorithm for recognizing and analyzing a user's emotional state.

[0539] "Emotional state" refers to the mental or emotional state of a user.

[0540] "Adjustment" means changing the content of the feedback to suit the user's emotional state.

[0541] This invention provides a system that can be applied to a system that enables the visually and hearing impaired and elderly to cross crosswalks safely at night or in crowded areas, thereby improving the safety and comfort of workers in factories. This system is mainly composed of cameras, sensors, artificial intelligence, an emotion engine, and sub-devices.

[0542] The server first uses cameras to collect environmental information, specifically capturing the movements of machines and workers in specific areas of the factory, and then uses sensors to acquire surrounding physical data, including temperature, vibration, and location information.

[0543] The server then uses artificial intelligence to analyze the data acquired from the cameras and sensors, detecting specific patterns and anomalies. For example, if a machine is moving and a worker is approaching, it may decide to take a "standby" (wait) action.

[0544] The emotion engine then recognizes the user's emotional state by analyzing their facial expressions and tone of voice to understand whether they are tense or relaxed.

[0545] Based on the analysis results, the server generates a specific signal based on the determined action and the recognition result of the emotion engine and sends it to the child device. For example, in the case of a "standby" action, the child device sends a signal with a short vibration or voice to inform the user to wait. On the other hand, in the case of a "safety" action, it sends a signal with a long vibration or voice to instruct the user to proceed with the work safely.

[0546] The child device provides feedback to the user based on the signal received from the server. If the emotion engine recognizes that the user is nervous, the child device provides gentler feedback (e.g., gentler voice instructions) than usual to help the user act with confidence.

[0547] The hardware used includes cameras (for example, general IP cameras), various sensors (such as temperature and vibration sensors), and a computer server. The software used includes video analysis using OpenCV and neural network models using Keras. A specific example of its use is analyzing data collected by the camera to determine safety and providing appropriate feedback to workers through audio or vibration.

[0548] Examples of prompts for generative AI models include:

[0549] "Generate code for an application that uses an analytical model of the factory environment to determine in real time whether it is safe or not, and an emotion engine to determine the emotional state of workers."

[0550] This prompt allows the AI ​​model to generate code for the system responsible for analyzing the environment and recognizing emotional states.

[0551] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0552] Step 1:

[0553] The server uses cameras to collect environmental information. Specifically, cameras are installed to monitor specific areas within the factory and capture video data in real time. This captured video data is input. The input video data is sent directly to the next step.

[0554] Step 2:

[0555] The server uses sensors to acquire surrounding physical data. For example, a temperature sensor measures the temperature inside a factory, and a vibration sensor checks the operating status of machines. This physical data is the input. The input physical data is sent to the next step along with the video data.

[0556] Step 3:

[0557] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. Specifically, it preprocesses the video data using OpenCV and inputs it into a learning model using Keras. The input is the data collected in steps 1 and 2. This data is analyzed to detect specific patterns and anomalies. The results of this analysis are sent to the next step. The output is the action of "stay safe" or "evacuate" as a result of the analysis.

[0558] Step 4:

[0559] The server uses an emotion engine to recognize the user's emotional state. Specifically, it collects and analyzes data on the user's face and voice to determine the user's emotional state. The input is the user's face and voice data. The output is the user's emotional state, such as "tension" or "relaxation," and this data is sent to the next step.

[0560] Step 5:

[0561] The server determines the appropriate action based on the analysis results and the emotional state. Specifically, if it is safe, it determines a "safe" action, and if evacuation is necessary, it determines an "evacuation" action. Also, if the emotional state is tense, it determines to provide gentler feedback. The inputs are the analysis results from step 3 and the emotional state from step 4. The output is the type of action and the feedback content.

[0562] Step 6:

[0563] The server sends the determined action and feedback content to the child device. Specifically, the feedback content is generated as a vibration or sound signal and sent to the child device. The input is the type of action and feedback content from step 5. The output is the signal sent to the child device.

[0564] Step 7:

[0565] The slave device receives the signal from the server and provides feedback to the user. Specifically, based on the received signal, it provides instructions such as "take shelter" or "safe" to the user through vibration or sound. The input is the signal sent to the slave device in step 6. The output is the vibration or sound feedback provided to the user.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] [Third embodiment]

[0570] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0571] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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."

[0582] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or when there are many people. This system is composed of a server and a slave device, and operates as follows.

[0583] 1. Collecting environment information (server):

[0584] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0585] 2. Data analysis and decision making (server):

[0586] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data and determines the color of the traffic light and the speed and distance of approaching vehicles. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the action of "cross." On the other hand, if the light is red or a vehicle is approaching, it decides to take the action of "wait."

[0587] 3. Information transmission (server → child device):

[0588] The server generates a specific signal based on the determined action and sends it to the child device: in the case of a "cross" action, a signal is sent to encourage the child device to cross, and in the case of a "wait" action, a signal is sent to encourage the child device to wait.

[0589] 4. Signal reception and feedback (child device):

[0590] The child device receives a signal from the server and provides feedback to the user based on the received signal. Specifically, when the child device receives a "cross" signal, it vibrates for a long time to let the user know when to cross. When the child device receives a "wait" signal, it vibrates for a short time and, in some cases, plays a voice message to let the user know to wait.

[0591] Specific examples

[0592] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles have stopped, determines that it is safe, and sends a "cross" signal to the sub-device. The user wearing the sub-device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the sub-device instructs the user to wait with a short vibration or audio.

[0593] This system allows people with visual and hearing impairments to walk safely.

[0594] The processing flow will be explained below.

[0595] Step 1:

[0596] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0597] Step 2:

[0598] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0599] Step 3:

[0600] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the road, and conversely, if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0601] Step 4:

[0602] The server generates a special signal corresponding to the determined action and sends it to the child device, for example, a signal that generates a long vibration for the "cross" action, and a signal that generates a short vibration or a voice instruction for the "wait" action.

[0603] Step 5:

[0604] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0605] Step 6:

[0606] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0607] Step 7:

[0608] The user receives feedback from the child device. The user feels vibration and audio feedback and takes action accordingly, such as crossing the street or waiting.

[0609] This series of processes allows visually and hearing impaired people to cross crosswalks safely, even at night, in heavy traffic, or when there are a lot of people.

[0610] Example 1

[0611] 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."

[0612] The present invention aims to provide a system that enables visually or hearing-impaired people and the elderly to cross intersections safely at night or at busy intersections. Conventional systems lack the advanced analytical capabilities to accurately collect information about the surrounding environment and determine safety. As a result, it has been difficult for visually or hearing-impaired users to cross the crosswalk at the correct time. Therefore, to solve this problem, a system with advanced environmental information collection and analysis capabilities is needed.

[0613] 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.

[0614] In this invention, the server includes means for collecting environmental information using a device for acquiring video data and a device for acquiring sensor data, means having artificial intelligence for analyzing the collected data, means for generating a signal corresponding to an action determined based on the analysis result and transmitting it to a slave device, and means for providing feedback to the user based on the signal received by the slave device. This enables the collection and real-time analysis of detailed environmental information to help visually or hearing-impaired users cross the crosswalk safely, ensuring safe crossing at the correct time.

[0615] "Video data" refers to visual information around intersections and crosswalks acquired using devices such as cameras.

[0616] "Sensor data" refers to numerical information obtained from sensors about the surrounding physical conditions, such as vehicle speed, distance, and presence confirmation.

[0617] "Device" is a general term for equipment used to acquire video data or sensor data, including cameras and distance measurement devices.

[0618] "Artificial intelligence" refers to algorithms and models that analyze collected video and sensor data and determine environmental conditions.

[0619] A "child device" is a mobile device that receives signals transmitted from the server and provides feedback to the user.

[0620] An "action" is a user behavior instruction determined by the server based on the analysis results, and refers to either "cross" or "wait."

[0621] "Feedback" refers to information that a slave device provides to a user, and is a means of conveying instructions to the user using vibrations, sound, or the like.

[0622] This invention is a system that enables the visually and hearing impaired and the elderly to cross intersections safely at night or at busy intersections. The system is mainly composed of a server and a slave device, each of which plays a specific role.

[0623] Collecting environmental information (server)

[0624] The server uses cameras to acquire video data around intersections and crosswalks, allowing it to collect environmental information in real time. The server also uses various sensors (e.g., LiDAR and ultrasonic sensors) to acquire sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. This data is temporarily stored in the server's database.

[0625] Data analysis and judgment (server)

[0626] The server analyzes the collected video data and sensor data using artificial intelligence (for example, deep learning models such as TensorFlow and PyTorch). As a result, it identifies the color of the traffic light and determines the vehicle's speed and distance. For example, if the light is green or the vehicle's speed is 30 km / h or less, the server determines that it is safe to cross the street and takes the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it takes the "wait" action.

[0627] Information transmission (server → child device)

[0628] Based on the analysis result, the server generates a specific signal. For example, in the case of a "cross" action, it generates a specific bit pattern (e.g., '1010') and sends it to the child device through the Wi-Fi module. On the other hand, in the case of a "wait" action, it generates a different bit pattern and sends it through the Wi-Fi module.

[0629] Signal reception and feedback (child device)

[0630] The child device receives the signal sent from the server. Based on that signal, the child device provides feedback to the user. When it receives the "cross" signal, it vibrates for a long time (e.g., 3 seconds) to let the user know when to cross. On the other hand, when it receives the "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or sends a voice message saying "Please wait."

[0631] Specific examples

[0632] For example, consider a case where a user uses this system at a busy intersection at night. The server uses a camera and LiDAR to collect environmental information and analyzes that data in real time using an AI model (e.g., TensorFlow). The moment the light turns green, the server determines that it is safe to cross after confirming that surrounding vehicles have stopped, and sends a "cross" signal to the child device. The user wearing the child device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the child device instructs the user to wait with a short vibration or sound.

[0633] Prompt Sentence Examples

[0634] The user is blind and deaf and needs help crossing intersections safely. The server collects environmental information using cameras and sensors and analyzes it in real time. When the light is green and a car is stopped, the server generates a "cross" signal and sends it to the child device. The child device vibrates long to let the user know it's time to cross. On the other hand, when the light is red or a vehicle is approaching, the server generates a "wait" signal and sends it to the child device. The child device vibrates shortly or sounds to tell the user to wait.

[0635] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0636] Step 1: Gather environmental information

[0637] The server activates the camera and captures video of the intersection and crosswalk area in real time. The server collects video data as input and temporarily stores it. The server also activates sensors to obtain sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. The server then temporarily stores the video and sensor data and collects the basic data needed to proceed to the next analysis step. Specifically, the server uses an HD camera to capture video at 30 frames per second and a LiDAR sensor to measure vehicle speed and distance.

[0638] Step 2: Analyze the data

[0639] The server inputs the temporarily stored video data and sensor data into an artificial intelligence model. Specifically, the video data is input into a Convolutional Neural Network (CNN) model to analyze the color of the traffic light. The sensor data is input into a Recurrent Neural Network (RNN) model to determine the vehicle's speed and distance. The output is whether the traffic light is green or red, and whether the vehicle's speed is below 30 km / h. For example, the server uses TensorFlow to run the CNN model to identify that the traffic light is green, while at the same time using the RNN model to confirm that the vehicle's speed is below 30 km / h.

[0640] Step 3: Decide on an action

[0641] Based on the analysis results, the server determines whether it is safe to cross the street. Based on this determination, the server decides on the action of "cross" or "wait." For example, if the light is green and no vehicles are approaching, the server decides on the action of "cross," while if the light is red or a vehicle is approaching, the server decides on the action of "wait." In this way, the server prepares the signal to be sent to the child device in the next step.

[0642] Step 4: Generate the signal

[0643] The server generates a corresponding signal based on the determined action. Specifically, for a "cross" action, it generates a specific bit pattern (e.g., '1010'), and for a "wait" action, it generates a different bit pattern (e.g., '0101'). This is then sent to the child device via the Wi-Fi module. For example, the server generates the bit pattern '1010' for the "cross" action and sends it to the child device via the Wi-Fi module.

[0644] Step 5: Receiving a signal

[0645] The child device receives the signal sent from the server. Specifically, the Wi-Fi module of the child device receives the bit pattern from the server. Based on this received signal, the child device provides feedback to the user in the next step.

[0646] Step 6: User feedback

[0647] The child device provides feedback to the user based on the received signal. Specifically, when it receives a "cross" signal, it vibrates for a long time (e.g., 3 seconds) to notify the user that it is time to cross. When it receives a "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or gives a voice message saying "Please wait." For example, the child device may activate its vibration motor for 3 seconds to notify the user of the "cross" signal.

[0648] Through these steps, the system can provide visually and hearing impaired users with detailed environmental information collection, real-time analysis, and feedback to help them cross the crosswalk safely.

[0649] (Application example 1)

[0650] 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."

[0651] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to cross pedestrian crossings safely, and aims to provide a means for them to cross safely even in today's busy traffic conditions and nighttime environments. In particular, there is a need for a method that can grasp the situation at intersections and pedestrian crossings in real time and provide information to users effectively.

[0652] 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.

[0653] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing the data acquired from the camera and the sensor, means for transmitting an action determined based on the analysis result to the slave device, means for providing feedback to the user based on the signal received by the slave device, and means for providing visual and tactile feedback to the user using a smart device, thereby enabling visually and hearing impaired users to grasp the status of traffic lights and vehicle movements in real time and cross the crosswalk safely.

[0654] A "camera" is a device for capturing images and processing the data as digital information.

[0655] "Environmental information" is information about the user's surroundings, including the surrounding conditions of crosswalks and intersections.

[0656] A "sensor" is a device that detects physical data and captures it as a digital signal. Examples include distance sensors and ultrasonic sensors.

[0657] "Physical data" is data about the physical characteristics of the environment, such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0658] "Artificial intelligence" is a technology that allows computers to mimic human intelligence and analyze data and make decisions. Specifically, it uses machine learning models.

[0659] "Analysis results" are the conclusions or judgments reached after AI processes and analyzes data obtained from cameras and sensors.

[0660] An "action" is a specific movement or instruction that is triggered based on the analysis results. Examples include "cross" and "wait."

[0661] A "child device" is a device that receives signals sent from the server and provides feedback to the user. Specifically, this applies to smart glasses and smartphones.

[0662] "Feedback" is information or instructions provided to the user. Examples include vibration, sound, and visual indication.

[0663] A "smart device" is a device that has advanced computing power and can connect to a network. Examples include smart glasses and smartphones.

[0664] "Visual feedback" is any form of feedback that visually conveys information to a user, such as a display or a light.

[0665] "Haptic feedback" is a form of feedback that conveys information to the user through physical vibration or pressure. An example is the vibration feature on a device.

[0666] A "system" is a set of devices and software that enable multiple elements to work together to achieve a specific function. The system of this invention is composed of cameras, sensors, servers, artificial intelligence, child devices, etc.

[0667] The present invention is an advanced assistance system for visually and hearing impaired people and elderly people to cross pedestrian crossings safely, and is implemented in the following steps.

[0668] 1. Means of collecting environmental information

[0669] The server uses a camera to capture images of the area around the crosswalk and collect environmental information in real time. The camera has high resolution and provides a wide field of view.

[0670] It also uses sensors to acquire physical data about the surroundings, including LiDAR and ultrasonic sensors, to measure with high precision physical data such as vehicle movement, pedestrian presence, and traffic light status.

[0671] 2. Data analysis methods

[0672] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. This analysis uses machine learning libraries such as TensorFlow and image processing libraries such as OpenCV. The analysis is performed in real time to recognize the status of traffic lights and the distance and speed of vehicles.

[0673] 3. How to decide on actions based on analysis results

[0674] The server determines the action to take based on the analysis results, such as "cross" or "wait." The action is determined as "cross" if the traffic light is green and no vehicles are approaching, and as "wait" if the traffic light is red or a vehicle is approaching.

[0675] 4. How to send an action

[0676] The server sends the determined action to the child device, which is a smart device (e.g., smart glasses or a smartphone) that receives the signal using wireless communication technology.

[0677] 5. Means of providing feedback

[0678] The child device provides feedback to the user based on the received signal. The feedback is displayed visually on the smart device's display and also provides tactile feedback using a vibration motor. Specifically, when a "cross" signal is received, a long vibration and a visual display indicate when to cross, and when a "wait" signal is received, a short vibration and a visual display indicate that the user should wait.

[0679] Hardware and software used

[0680] Hardware: high-resolution cameras, LiDAR sensors, ultrasonic sensors, high-performance servers (with CPUs and GPUs), smart glasses, smartphones

[0681] Software: TensorFlow (machine learning library), OpenCV (image processing library), Python (data analysis and application development)

[0682] Specific examples

[0683] For example, consider a case where a user approaches a busy intersection at night. In this case, the server uses cameras and sensors to collect information about the surrounding area in real time and analyzes the traffic light status and vehicle movement. When the server confirms that the light is green and no vehicles are approaching, it sends a "cross" signal to the child device. The smart glasses worn by the user will then vibrate and display a visual indication to indicate when to cross. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, instructing the smart glasses to wait with a short vibration and a visual indication.

[0684] Example prompts for generative AI models

[0685] "When the intersection light is green and vehicles are far away, how can we use AI models and smart glasses to provide real-time feedback to visually and hearing impaired users to help them safely cross the street?"

[0686] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0687] Step 1:

[0688] The server uses a camera to collect environmental information around the crosswalk in real time. The camera captures high-resolution video and sends the data to the server as sensor data. The input is the video data from the camera, and the output is the video data stored on the server.

[0689] Step 2:

[0690] The server acquires surrounding physical data using sensors (LiDAR and ultrasonic sensors). The sensors acquire physical data such as vehicle movement, pedestrian presence, and traffic light status, and transmits this data to the server. The input is the physical data from the sensors, and the output is the physical data stored on the server.

[0691] Step 3:

[0692] The server uses artificial intelligence to analyze data acquired from cameras and sensors. It processes the data using libraries such as TensorFlow and OpenCV and analyzes environmental information. The input is data from the cameras and sensors, and the output is the analysis results (traffic signal status, estimated vehicle distance and speed). Specific operations include data preprocessing, inference using machine learning models, and postprocessing of the results.

[0693] Step 4:

[0694] The server sends an action determined based on the analysis result to the child device. The action is determined to be "cross" or "wait," and a signal is sent to the smart device using wireless communication. The input is the analysis result, and the output is the signal sent to the smart device.

[0695] Step 5:

[0696] The child device (smart device) provides feedback to the user based on the received signal. The feedback is displayed visually on the display and also tactilely using a vibration motor. For example, a "cross" signal is accompanied by a long vibration and a visual display, while a "wait" signal is accompanied by a short vibration and a visual display. The input is the signal from the server, and the output is the feedback to the user.

[0697] Step 6:

[0698] The user follows the feedback from the smart device to safely cross the crosswalk. Specifically, the user checks the display of the smart device, senses the feedback vibration, and crosses the crosswalk at the appropriate time. The input is the feedback from the smart device, and the output is the action of crossing the crosswalk safely.

[0699] 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.

[0700] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or at crowded crosswalks. This system comprises a server, a child device, and an emotion engine, and operates as follows.

[0701] 1. Collecting environment information (server):

[0702] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0703] 2. Data analysis and decision making (server):

[0704] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data, detects the color of the traffic light (red or green), and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it decides to take the "wait" action.

[0705] 3. Emotion recognition by emotion engine (server):

[0706] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's voice and facial expressions to understand whether the user is tense or relaxed. This allows the server to adapt the feedback content to the user's emotional state.

[0707] 4. Information transmission (server → child device):

[0708] The server generates a specific signal based on the determined action and the recognition result by the emotion engine and sends it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, and a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0709] 5. Signal Reception and Feedback (Child Device):

[0710] The child device receives signals from the server and provides feedback to the user based on the received signal. For example, if the child device receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If the child device receives a "wait" signal, it will vibrate for a short time and, in some cases, play a voice prompt to let the user know to wait.

[0711] Specific examples

[0712] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles are stopped, determines that it is safe, and sends a "cross" signal to the child device. At that time, the server uses an emotion engine to recognize the user's emotional state (for example, whether they are nervous). If the user is nervous, the server provides gentler feedback than usual.

[0713] The user wearing the device will feel a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, and the device will instruct the user to wait with a short vibration or voice. The feedback will also be adjusted taking into account the user's emotional state.

[0714] As described above, by combining this system with an emotion engine, we can provide users with more appropriate and reassuring feedback, enabling visually and hearing impaired people to cross the crosswalk safely.

[0715] The processing flow will be explained below.

[0716] Step 1:

[0717] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0718] Step 2:

[0719] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0720] Step 3:

[0721] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the vehicle, and if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0722] Step 4:

[0723] The server activates the emotion engine to recognize the user's emotional state. Specifically, it collects data such as voice and facial expressions through sensors and cameras in the device worn by the user to determine whether the user is tense or relaxed.

[0724] Step 5:

[0725] The server determines the final action and feedback content based on the analysis results and the emotion engine results. For example, if the user is nervous, the feedback when crossing the street may be gentler than usual to make it easier for the user to cross.

[0726] Step 6:

[0727] The server generates a special signal corresponding to the determined action and transmits it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, or a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0728] Step 7:

[0729] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0730] Step 8:

[0731] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0732] Step 9:

[0733] The user receives feedback from the child device and acts accordingly. For example, if they feel a vibration, they confirm that it is safe to cross and begin walking. On the other hand, if they receive a short vibration or voice instruction, they stop and wait.

[0734] Through this series of processes, the system can provide users with timely feedback, helping people with visual and hearing impairments cross the street safely. The addition of an emotion engine makes it possible to provide feedback that takes into account the user's emotional state, providing an even greater sense of safety and security.

[0735] Example 2

[0736] 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."

[0737] The problem is that there are not enough systems in place to solve the problems faced by the elderly and people with visual and hearing impairments when crossing the street safely, especially at night or in busy traffic environments.

[0738] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0739] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means including an emotion engine for recognizing the emotional state of the user, means for transmitting a determined action to the child device based on the analysis result and the emotion recognition result, and means for providing feedback to the user based on the signal received by the child device, thereby enabling visually and hearing impaired users to cross the crosswalk safely and with peace of mind.

[0740] A "camera" is a device that captures information about the surrounding environment as an image.

[0741] A "sensor" is a device that acquires physical data about its surroundings (e.g., vehicle movement, pedestrian presence, traffic light status, etc.).

[0742] "Artificial intelligence" refers to programs and systems that analyze data obtained from cameras and sensors and make situational judgments.

[0743] An "emotion engine" is software and hardware that recognizes the user's emotional state from their voice and facial expressions.

[0744] A "child device" is a device that receives signals sent from the server and provides feedback to the user.

[0745] "Feedback" refers to instructions or information that a user receives from a system, and specifically includes vibrations and audio instructions.

[0746] The present invention relates to a system that enables physically disabled people with visual and hearing impairments and elderly people to cross pedestrian crossings safely. This system is mainly composed of a server, a child device, and an emotion engine. A specific embodiment of this system will be described in detail below.

[0747] Server functions and hardware / software used

[0748] The server operates using the following hardware and software:

[0749] Environmental information collection: The server uses cameras and sensors to collect information about the surrounding environment. In a specific example, a Hikvision camera is used, with a LiDAR sensor capturing physical data about the surroundings.

[0750] Data analysis: The server analyzes the acquired data using a generative AI model such as TensorFlow, which determines the color of the traffic light (e.g., red or green), vehicle movement, and the presence of pedestrians.

[0751] Emotion recognition using an emotion engine: The server uses Affectiva's SDK to recognize the user's emotional state. It analyzes emotions such as tension or relaxation based on the user's voice and facial expression data.

[0752] Functions of child devices

[0753] The slave device has the following functions:

[0754] Signal reception: The slave device receives a signal from the server. There are no particular restrictions on the communication method used, but Wi-Fi, Bluetooth, etc. are possible.

[0755] Providing feedback: The child device provides feedback to the user based on the received signal, for example, a long vibration for the "cross" action, a short vibration for the "wait" action, or a voice instruction. Specifically, an Arduino-based vibration motor is used.

[0756] Server Processing Details

[0757] The server performs the process in the following procedure.

[0758] 1. Environmental information collection: The server captures video data from Hikvision cameras and collects physical data using LiDAR sensors, allowing it to understand the situation at intersections and crosswalks in real time.

[0759] 2. Data analysis: The server uses the TensorFlow model to analyze the acquired video and sensor data, checking the color of traffic lights and vehicle movements to determine whether it is safe.

[0760] 3. Emotion Recognition by Emotion Engine: The server uses Affectiva's SDK to recognize the user's emotional state. For example, it analyzes whether the user is tense or relaxed.

[0761] 4. Information transmission: Based on the analysis results and emotion recognition results, the appropriate action is determined and a signal is sent to the child device.

[0762] Specific examples

[0763] For example, consider a situation where a user is using this system at a busy intersection at night. The server collects environmental information through Hikvision's camera and LiDAR sensor, and analyzes that data in real time using a TensorFlow model. The moment the light turns green, the server confirms that surrounding vehicles are stopped, and if it determines that it is safe, it sends a "cross" signal to the child device. If the server detects that the user is nervous using Affectiva's SDK, it can add settings to provide gentler feedback.

[0764] The device worn by the user generates a long vibration based on the received "cross" signal. The user feels the vibration and can cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal and provides a short vibration or sound to instruct the user to wait.

[0765] Prompt Sentence Examples

[0766] "I would like to design a system that instructs pedestrians to cross safely when vehicles are stopped at a busy intersection the moment the light turns green. Please explain in detail how the emotion engine can detect whether pedestrians are nervous and adjust the feedback accordingly."

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1:

[0769] Collecting environmental information (server)

[0770] The server receives input from cameras and sensors to obtain streaming data. Cameras provide real-time video of the area around intersections and crosswalks. Sensors (e.g., LiDAR sensors) collect physical data such as vehicle movement, pedestrian presence, and traffic light status. This data is input to the server.

[0771] Input: Video data from cameras, physical data from sensors

[0772] Output: Video data and physical data are stored in the server's memory.

[0773] How it works: The server captures video from a Hikvision camera at 30 frames per second and obtains 3D scan data of surrounding objects from a LiDAR sensor.

[0774] Step 2:

[0775] Data analysis and judgment (server)

[0776] The server uses AI models such as TensorFlow to analyze the acquired data. First, it extracts the color of traffic lights and the location of pedestrians from the video data, and then identifies the movement and location of vehicles from the physical data. This analysis determines the current situation, such as whether the light is green and a vehicle is stopped, or whether the light is red and a vehicle is approaching.

[0777] Input: Visual and physical data collected in Step 1

[0778] Output: A decision indicating whether it is safe to cross.

[0779] How it works: The AI ​​model analyzes camera footage to detect the status of traffic lights. It uses data from sensors to determine the speed and location of surrounding vehicles. For example, if the light is green and the vehicle's speed is close to zero, it determines that it is safe and decides to "cross" the road.

[0780] Step 3:

[0781] Emotion recognition by emotion engine (server)

[0782] The server uses Affectiva's emotion recognition engine to recognize the user's emotional state from their voice and facial expression data, thereby determining whether the user is tense or relaxed and providing feedback accordingly.

[0783] Input: User's voice and facial expression data

[0784] Output: Data indicating the user's emotional state

[0785] How it works: The server inputs the user's voice and facial expression data into the Affectiva SDK, which analyzes their emotional state in real time. For example, if the user's voice is high-pitched or their facial expression shows signs of impatience, the emotion engine will determine that the user is nervous.

[0786] Step 4:

[0787] Information transmission (server → child device)

[0788] The server determines the appropriate action based on the analysis results and emotion recognition results, and sends the result to the child device. Specifically, it generates a signal including a long vibration for the "cross" action and a short vibration or voice instruction for the "wait" action.

[0789] Input: Judgment result indicating safety of crossing, emotional state of the user

[0790] Output: Signal sent to child device

[0791] Specific operation: If the result of the judgment is "cross," the server sends a signal to the child device that generates a long vibration. If the result of the judgment is "wait," the server sends a signal to generate a short vibration or a voice instruction.

[0792] Step 5:

[0793] Signal reception and feedback (child device)

[0794] The child device receives the signal sent from the server and provides feedback to the user based on the signal. For example, in the case of a "cross" signal, the child device operates a vibration motor for a long period of time to let the user know when it is time to cross.

[0795] Input: Signal from the server

[0796] Output: Feedback to the user (vibration and audio instructions)

[0797] Specific operation: The slave device analyzes the signal received from the server and controls the vibration motor equipped with Arduino. When the user receives the "cross" signal, the vibration motor generates a long vibration, allowing the user to cross the crosswalk safely.

[0798] (Application example 2)

[0799] 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."

[0800] In systems that enable the visually and hearing impaired, physically disabled, and elderly people to cross crosswalks safely at night, in heavy traffic, or at crowded crosswalks, etc., ensuring the safety and efficiency of workers, particularly in factories, requires real-time analysis of the working environment and providing appropriate feedback that takes into account the emotional state of the worker. However, conventional technologies were unable to recognize the emotional state of the worker and adjust the feedback accordingly, making it difficult to achieve both safety and comfort.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0802] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means for transmitting actions determined based on the analysis results to the slave device, means for providing feedback to the user based on signals received by the slave device, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the feedback content based on the user's emotional state. This makes it possible to flexibly provide safety measures according to the worker's emotional state, thereby achieving both safety and comfort in the work environment.

[0803] A "camera" is a device for recording video or images.

[0804] "Environmental information" is data about the surrounding physical conditions and situations.

[0805] A "sensor" is a device for detecting and collecting physical data.

[0806] "Artificial intelligence" refers to algorithms and systems that analyze acquired data and make appropriate decisions and processes.

[0807] "Analysis results" are interpretations and conclusions obtained through data analysis.

[0808] An "action" is a specific movement or behavior that is determined based on the analysis results.

[0809] A "child device" is a device that receives signals from the server and provides feedback to the user.

[0810] "Feedback" refers to information or instructions provided to a user.

[0811] An "emotion engine" is a system or algorithm for recognizing and analyzing a user's emotional state.

[0812] "Emotional state" refers to the mental or emotional state of a user.

[0813] "Adjustment" means changing the content of the feedback to suit the user's emotional state.

[0814] This invention provides a system that can be applied to a system that enables the visually and hearing impaired and elderly to cross crosswalks safely at night or in crowded areas, thereby improving the safety and comfort of workers in factories. This system is mainly composed of cameras, sensors, artificial intelligence, an emotion engine, and sub-devices.

[0815] The server first uses cameras to collect environmental information, specifically capturing the movements of machines and workers in specific areas of the factory, and then uses sensors to acquire surrounding physical data, including temperature, vibration, and location information.

[0816] The server then uses artificial intelligence to analyze the data acquired from the cameras and sensors, detecting specific patterns and anomalies. For example, if a machine is moving and a worker is approaching, it may decide to take a "standby" (wait) action.

[0817] The emotion engine then recognizes the user's emotional state by analyzing their facial expressions and tone of voice to understand whether they are tense or relaxed.

[0818] Based on the analysis results, the server generates a specific signal based on the determined action and the recognition result of the emotion engine and sends it to the child device. For example, in the case of a "standby" action, the child device sends a signal with a short vibration or voice to inform the user to wait. On the other hand, in the case of a "safety" action, it sends a signal with a long vibration or voice to instruct the user to proceed with the work safely.

[0819] The child device provides feedback to the user based on the signal received from the server. If the emotion engine recognizes that the user is nervous, the child device provides gentler feedback (e.g., gentler voice instructions) than usual to help the user act with confidence.

[0820] The hardware used includes cameras (for example, general IP cameras), various sensors (such as temperature and vibration sensors), and a computer server. The software used includes video analysis using OpenCV and neural network models using Keras. A specific example of its use is analyzing data collected by the camera to determine safety and providing appropriate feedback to workers through audio or vibration.

[0821] Examples of prompts for generative AI models include:

[0822] "Generate code for an application that uses an analytical model of the factory environment to determine in real time whether it is safe or not, and an emotion engine to determine the emotional state of workers."

[0823] This prompt allows the AI ​​model to generate code for the system responsible for analyzing the environment and recognizing emotional states.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] The server uses cameras to collect environmental information. Specifically, cameras are installed to monitor specific areas within the factory and capture video data in real time. This captured video data is input. The input video data is sent directly to the next step.

[0827] Step 2:

[0828] The server uses sensors to acquire surrounding physical data. For example, a temperature sensor measures the temperature inside a factory, and a vibration sensor checks the operating status of machines. This physical data is the input. The input physical data is sent to the next step along with the video data.

[0829] Step 3:

[0830] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. Specifically, it preprocesses the video data using OpenCV and inputs it into a learning model using Keras. The input is the data collected in steps 1 and 2. This data is analyzed to detect specific patterns and anomalies. The results of this analysis are sent to the next step. The output is the action of "stay safe" or "evacuate" as a result of the analysis.

[0831] Step 4:

[0832] The server uses an emotion engine to recognize the user's emotional state. Specifically, it collects and analyzes data on the user's face and voice to determine the user's emotional state. The input is the user's face and voice data. The output is the user's emotional state, such as "tension" or "relaxation," and this data is sent to the next step.

[0833] Step 5:

[0834] The server determines the appropriate action based on the analysis results and the emotional state. Specifically, if it is safe, it determines a "safe" action, and if evacuation is necessary, it determines an "evacuation" action. Also, if the emotional state is tense, it determines to provide gentler feedback. The inputs are the analysis results from step 3 and the emotional state from step 4. The output is the type of action and the feedback content.

[0835] Step 6:

[0836] The server sends the determined action and feedback content to the child device. Specifically, the feedback content is generated as a vibration or sound signal and sent to the child device. The input is the type of action and feedback content from step 5. The output is the signal sent to the child device.

[0837] Step 7:

[0838] The slave device receives the signal from the server and provides feedback to the user. Specifically, based on the received signal, it provides instructions such as "take shelter" or "safe" to the user through vibration or sound. The input is the signal sent to the slave device in step 6. The output is the vibration or sound feedback provided to the user.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] [Fourth embodiment]

[0843] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0844] 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.

[0845] 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).

[0846] 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.

[0847] 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.

[0848] 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).

[0849] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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."

[0856] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or when there are many people. This system is composed of a server and a slave device, and operates as follows.

[0857] 1. Collecting environment information (server):

[0858] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0859] 2. Data analysis and decision making (server):

[0860] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data and determines the color of the traffic light and the speed and distance of approaching vehicles. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the action of "cross." On the other hand, if the light is red or a vehicle is approaching, it decides to take the action of "wait."

[0861] 3. Information transmission (server → child device):

[0862] The server generates a specific signal based on the determined action and sends it to the child device: in the case of a "cross" action, a signal is sent to encourage the child device to cross, and in the case of a "wait" action, a signal is sent to encourage the child device to wait.

[0863] 4. Signal reception and feedback (child device):

[0864] The child device receives a signal from the server and provides feedback to the user based on the received signal. Specifically, when the child device receives a "cross" signal, it vibrates for a long time to let the user know when to cross. When the child device receives a "wait" signal, it vibrates for a short time and, in some cases, plays a voice message to let the user know to wait.

[0865] Specific examples

[0866] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles have stopped, determines that it is safe, and sends a "cross" signal to the sub-device. The user wearing the sub-device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the sub-device instructs the user to wait with a short vibration or audio.

[0867] This system allows people with visual and hearing impairments to walk safely.

[0868] The processing flow will be explained below.

[0869] Step 1:

[0870] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0871] Step 2:

[0872] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0873] Step 3:

[0874] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the road, and conversely, if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0875] Step 4:

[0876] The server generates a special signal corresponding to the determined action and sends it to the child device, for example, a signal that generates a long vibration for the "cross" action, and a signal that generates a short vibration or a voice instruction for the "wait" action.

[0877] Step 5:

[0878] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[0879] Step 6:

[0880] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[0881] Step 7:

[0882] The user receives feedback from the child device. The user feels vibration and audio feedback and takes action accordingly, such as crossing the street or waiting.

[0883] This series of processes allows visually and hearing impaired people to cross crosswalks safely, even at night, in heavy traffic, or when there are a lot of people.

[0884] Example 1

[0885] 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."

[0886] The present invention aims to provide a system that enables visually or hearing-impaired people and the elderly to cross intersections safely at night or at busy intersections. Conventional systems lack the advanced analytical capabilities to accurately collect information about the surrounding environment and determine safety. As a result, it has been difficult for visually or hearing-impaired users to cross the crosswalk at the correct time. Therefore, to solve this problem, a system with advanced environmental information collection and analysis capabilities is needed.

[0887] 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.

[0888] In this invention, the server includes means for collecting environmental information using a device for acquiring video data and a device for acquiring sensor data, means having artificial intelligence for analyzing the collected data, means for generating a signal corresponding to an action determined based on the analysis result and transmitting it to a slave device, and means for providing feedback to the user based on the signal received by the slave device. This enables the collection and real-time analysis of detailed environmental information to help visually or hearing-impaired users cross the crosswalk safely, ensuring safe crossing at the correct time.

[0889] "Video data" refers to visual information around intersections and crosswalks acquired using devices such as cameras.

[0890] "Sensor data" refers to numerical information obtained from sensors about the surrounding physical conditions, such as vehicle speed, distance, and presence confirmation.

[0891] "Device" is a general term for equipment used to acquire video data or sensor data, including cameras and distance measurement devices.

[0892] "Artificial intelligence" refers to algorithms and models that analyze collected video and sensor data and determine environmental conditions.

[0893] A "child device" is a mobile device that receives signals transmitted from the server and provides feedback to the user.

[0894] An "action" is a user behavior instruction determined by the server based on the analysis results, and refers to either "cross" or "wait."

[0895] "Feedback" refers to information that a slave device provides to a user, and is a means of conveying instructions to the user using vibrations, sound, or the like.

[0896] This invention is a system that enables the visually and hearing impaired and the elderly to cross intersections safely at night or at busy intersections. The system is mainly composed of a server and a slave device, each of which plays a specific role.

[0897] Collecting environmental information (server)

[0898] The server uses cameras to acquire video data around intersections and crosswalks, allowing it to collect environmental information in real time. The server also uses various sensors (e.g., LiDAR and ultrasonic sensors) to acquire sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. This data is temporarily stored in the server's database.

[0899] Data analysis and judgment (server)

[0900] The server analyzes the collected video data and sensor data using artificial intelligence (for example, deep learning models such as TensorFlow and PyTorch). As a result, it identifies the color of the traffic light and determines the vehicle's speed and distance. For example, if the light is green or the vehicle's speed is 30 km / h or less, the server determines that it is safe to cross the street and takes the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it takes the "wait" action.

[0901] Information transmission (server → child device)

[0902] Based on the analysis result, the server generates a specific signal. For example, in the case of a "cross" action, it generates a specific bit pattern (e.g., '1010') and sends it to the child device through the Wi-Fi module. On the other hand, in the case of a "wait" action, it generates a different bit pattern and sends it through the Wi-Fi module.

[0903] Signal reception and feedback (child device)

[0904] The child device receives the signal sent from the server. Based on that signal, the child device provides feedback to the user. When it receives the "cross" signal, it vibrates for a long time (e.g., 3 seconds) to let the user know when to cross. On the other hand, when it receives the "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or sends a voice message saying "Please wait."

[0905] Specific examples

[0906] For example, consider a case where a user uses this system at a busy intersection at night. The server uses a camera and LiDAR to collect environmental information and analyzes that data in real time using an AI model (e.g., TensorFlow). The moment the light turns green, the server determines that it is safe to cross after confirming that surrounding vehicles have stopped, and sends a "cross" signal to the child device. The user wearing the child device feels a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal, and the child device instructs the user to wait with a short vibration or sound.

[0907] Prompt Sentence Examples

[0908] The user is blind and deaf and needs help crossing intersections safely. The server collects environmental information using cameras and sensors and analyzes it in real time. When the light is green and a car is stopped, the server generates a "cross" signal and sends it to the child device. The child device vibrates long to let the user know it's time to cross. On the other hand, when the light is red or a vehicle is approaching, the server generates a "wait" signal and sends it to the child device. The child device vibrates shortly or sounds to tell the user to wait.

[0909] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0910] Step 1: Gather environmental information

[0911] The server activates the camera and captures video of the intersection and crosswalk area in real time. The server collects video data as input and temporarily stores it. The server also activates sensors to obtain sensor data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights. The server then temporarily stores the video and sensor data and collects the basic data needed to proceed to the next analysis step. Specifically, the server uses an HD camera to capture video at 30 frames per second and a LiDAR sensor to measure vehicle speed and distance.

[0912] Step 2: Analyze the data

[0913] The server inputs the temporarily stored video data and sensor data into an artificial intelligence model. Specifically, the video data is input into a Convolutional Neural Network (CNN) model to analyze the color of the traffic light. The sensor data is input into a Recurrent Neural Network (RNN) model to determine the vehicle's speed and distance. The output is whether the traffic light is green or red, and whether the vehicle's speed is below 30 km / h. For example, the server uses TensorFlow to run the CNN model to identify that the traffic light is green, while at the same time using the RNN model to confirm that the vehicle's speed is below 30 km / h.

[0914] Step 3: Decide on an action

[0915] Based on the analysis results, the server determines whether it is safe to cross the street. Based on this determination, the server decides on the action of "cross" or "wait." For example, if the light is green and no vehicles are approaching, the server decides on the action of "cross," while if the light is red or a vehicle is approaching, the server decides on the action of "wait." In this way, the server prepares the signal to be sent to the child device in the next step.

[0916] Step 4: Generate the signal

[0917] The server generates a corresponding signal based on the determined action. Specifically, for a "cross" action, it generates a specific bit pattern (e.g., '1010'), and for a "wait" action, it generates a different bit pattern (e.g., '0101'). This is then sent to the child device via the Wi-Fi module. For example, the server generates the bit pattern '1010' for the "cross" action and sends it to the child device via the Wi-Fi module.

[0918] Step 5: Receiving a signal

[0919] The child device receives the signal sent from the server. Specifically, the Wi-Fi module of the child device receives the bit pattern from the server. Based on this received signal, the child device provides feedback to the user in the next step.

[0920] Step 6: User feedback

[0921] The child device provides feedback to the user based on the received signal. Specifically, when it receives a "cross" signal, it vibrates for a long time (e.g., 3 seconds) to notify the user that it is time to cross. When it receives a "wait" signal, it vibrates for a short time (e.g., 1 second) in succession or gives a voice message saying "Please wait." For example, the child device may activate its vibration motor for 3 seconds to notify the user of the "cross" signal.

[0922] Through these steps, the system can provide visually and hearing impaired users with detailed environmental information collection, real-time analysis, and feedback to help them cross the crosswalk safely.

[0923] (Application example 1)

[0924] 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."

[0925] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to cross pedestrian crossings safely, and aims to provide a means for them to cross safely even in today's busy traffic conditions and nighttime environments. In particular, there is a need for a method that can grasp the situation at intersections and pedestrian crossings in real time and provide information to users effectively.

[0926] 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.

[0927] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing the data acquired from the camera and the sensor, means for transmitting an action determined based on the analysis result to the slave device, means for providing feedback to the user based on the signal received by the slave device, and means for providing visual and tactile feedback to the user using a smart device, thereby enabling visually and hearing impaired users to grasp the status of traffic lights and vehicle movements in real time and cross the crosswalk safely.

[0928] A "camera" is a device for capturing images and processing the data as digital information.

[0929] "Environmental information" is information about the user's surroundings, including the surrounding conditions of crosswalks and intersections.

[0930] A "sensor" is a device that detects physical data and captures it as a digital signal. Examples include distance sensors and ultrasonic sensors.

[0931] "Physical data" is data about the physical characteristics of the environment, such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0932] "Artificial intelligence" is a technology that allows computers to mimic human intelligence and analyze data and make decisions. Specifically, it uses machine learning models.

[0933] "Analysis results" are the conclusions or judgments reached after AI processes and analyzes data obtained from cameras and sensors.

[0934] An "action" is a specific movement or instruction that is triggered based on the analysis results. Examples include "cross" and "wait."

[0935] A "child device" is a device that receives signals sent from the server and provides feedback to the user. Specifically, this applies to smart glasses and smartphones.

[0936] "Feedback" is information or instructions provided to the user. Examples include vibration, sound, and visual indication.

[0937] A "smart device" is a device that has advanced computing power and can connect to a network. Examples include smart glasses and smartphones.

[0938] "Visual feedback" is any form of feedback that visually conveys information to a user, such as a display or a light.

[0939] "Haptic feedback" is a form of feedback that conveys information to the user through physical vibration or pressure. An example is the vibration feature on a device.

[0940] A "system" is a set of devices and software that enable multiple elements to work together to achieve a specific function. The system of this invention is composed of cameras, sensors, servers, artificial intelligence, child devices, etc.

[0941] The present invention is an advanced assistance system for visually and hearing impaired people and elderly people to cross pedestrian crossings safely, and is implemented in the following steps.

[0942] 1. Means of collecting environmental information

[0943] The server uses a camera to capture images of the area around the crosswalk and collect environmental information in real time. The camera has high resolution and provides a wide field of view.

[0944] It also uses sensors to acquire physical data about the surroundings, including LiDAR and ultrasonic sensors, to measure with high precision physical data such as vehicle movement, pedestrian presence, and traffic light status.

[0945] 2. Data analysis methods

[0946] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. This analysis uses machine learning libraries such as TensorFlow and image processing libraries such as OpenCV. The analysis is performed in real time to recognize the status of traffic lights and the distance and speed of vehicles.

[0947] 3. How to decide on actions based on analysis results

[0948] The server determines the action to take based on the analysis results, such as "cross" or "wait." The action is determined as "cross" if the traffic light is green and no vehicles are approaching, and as "wait" if the traffic light is red or a vehicle is approaching.

[0949] 4. How to send an action

[0950] The server sends the determined action to the child device, which is a smart device (e.g., smart glasses or a smartphone) that receives the signal using wireless communication technology.

[0951] 5. Means of providing feedback

[0952] The child device provides feedback to the user based on the received signal. The feedback is displayed visually on the smart device's display and also provides tactile feedback using a vibration motor. Specifically, when a "cross" signal is received, a long vibration and a visual display indicate when to cross, and when a "wait" signal is received, a short vibration and a visual display indicate that the user should wait.

[0953] Hardware and software used

[0954] Hardware: high-resolution cameras, LiDAR sensors, ultrasonic sensors, high-performance servers (with CPUs and GPUs), smart glasses, smartphones

[0955] Software: TensorFlow (machine learning library), OpenCV (image processing library), Python (data analysis and application development)

[0956] Specific examples

[0957] For example, consider a case where a user approaches a busy intersection at night. In this case, the server uses cameras and sensors to collect information about the surrounding area in real time and analyzes the traffic light status and vehicle movement. When the server confirms that the light is green and no vehicles are approaching, it sends a "cross" signal to the child device. The smart glasses worn by the user will then vibrate and display a visual indication to indicate when to cross. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, instructing the smart glasses to wait with a short vibration and a visual indication.

[0958] Example prompts for generative AI models

[0959] "When the intersection light is green and vehicles are far away, how can we use AI models and smart glasses to provide real-time feedback to visually and hearing impaired users to help them safely cross the street?"

[0960] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0961] Step 1:

[0962] The server uses a camera to collect environmental information around the crosswalk in real time. The camera captures high-resolution video and sends the data to the server as sensor data. The input is the video data from the camera, and the output is the video data stored on the server.

[0963] Step 2:

[0964] The server acquires surrounding physical data using sensors (LiDAR and ultrasonic sensors). The sensors acquire physical data such as vehicle movement, pedestrian presence, and traffic light status, and transmits this data to the server. The input is the physical data from the sensors, and the output is the physical data stored on the server.

[0965] Step 3:

[0966] The server uses artificial intelligence to analyze data acquired from cameras and sensors. It processes the data using libraries such as TensorFlow and OpenCV and analyzes environmental information. The input is data from the cameras and sensors, and the output is the analysis results (traffic signal status, estimated vehicle distance and speed). Specific operations include data preprocessing, inference using machine learning models, and postprocessing of the results.

[0967] Step 4:

[0968] The server sends an action determined based on the analysis result to the child device. The action is determined to be "cross" or "wait," and a signal is sent to the smart device using wireless communication. The input is the analysis result, and the output is the signal sent to the smart device.

[0969] Step 5:

[0970] The child device (smart device) provides feedback to the user based on the received signal. The feedback is displayed visually on the display and also tactilely using a vibration motor. For example, a "cross" signal is accompanied by a long vibration and a visual display, while a "wait" signal is accompanied by a short vibration and a visual display. The input is the signal from the server, and the output is the feedback to the user.

[0971] Step 6:

[0972] The user follows the feedback from the smart device to safely cross the crosswalk. Specifically, the user checks the display of the smart device, senses the feedback vibration, and crosses the crosswalk at the appropriate time. The input is the feedback from the smart device, and the output is the action of crossing the crosswalk safely.

[0973] 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.

[0974] The present invention provides a system that enables physically disabled people with visual and hearing impairments and the elderly to safely cross crosswalks at night, in busy traffic, or at crowded crosswalks. This system comprises a server, a child device, and an emotion engine, and operates as follows.

[0975] 1. Collecting environment information (server):

[0976] The server collects environmental information using cameras, specifically capturing images of intersections and crosswalks, and also uses sensors to obtain physical data such as the movement of surrounding vehicles, the presence of pedestrians, and the status of traffic lights.

[0977] 2. Data analysis and decision making (server):

[0978] The server is equipped with artificial intelligence to analyze the acquired video data and sensor data. The artificial intelligence analyzes this data, detects the color of the traffic light (red or green), and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians. For example, if the light is green and no vehicles are approaching, it determines that it is safe to cross the street and decides to take the "cross" action. On the other hand, if the light is red or a vehicle is approaching, it decides to take the "wait" action.

[0979] 3. Emotion recognition by emotion engine (server):

[0980] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's voice and facial expressions to understand whether the user is tense or relaxed. This allows the server to adapt the feedback content to the user's emotional state.

[0981] 4. Information transmission (server → child device):

[0982] The server generates a specific signal based on the determined action and the recognition result by the emotion engine and sends it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, and a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[0983] 5. Signal Reception and Feedback (Child Device):

[0984] The child device receives signals from the server and provides feedback to the user based on the received signal. For example, if the child device receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If the child device receives a "wait" signal, it will vibrate for a short time and, in some cases, play a voice prompt to let the user know to wait.

[0985] Specific examples

[0986] Consider a scenario where a user uses this system at night or at a busy intersection. The server collects environmental information about the intersection using cameras and sensors, and analyzes that data in real time using artificial intelligence. The moment the light turns green, it confirms that surrounding vehicles are stopped, determines that it is safe, and sends a "cross" signal to the child device. At that time, the server uses an emotion engine to recognize the user's emotional state (for example, whether they are nervous). If the user is nervous, the server provides gentler feedback than usual.

[0987] The user wearing the device will feel a long vibration based on the received signal, allowing them to cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server will send a "wait" signal, and the device will instruct the user to wait with a short vibration or voice. The feedback will also be adjusted taking into account the user's emotional state.

[0988] As described above, by combining this system with an emotion engine, we can provide users with more appropriate and reassuring feedback, enabling visually and hearing impaired people to cross the crosswalk safely.

[0989] The processing flow will be explained below.

[0990] Step 1:

[0991] The server initializes the cameras and sensors and starts collecting environmental information by continuously capturing video frames from the cameras and periodically reading surrounding physical data (distance, speed, temperature, etc.) from the sensors.

[0992] Step 2:

[0993] The server inputs the acquired video frames and sensor data into the AI, which then analyzes this data to recognize the situation at intersections and crosswalks. Specifically, it detects the color of traffic lights (red or green) and analyzes the presence or absence of surrounding vehicles and the movements of pedestrians.

[0994] Step 3:

[0995] The server determines the action to take for the user based on the analysis results using artificial intelligence. For example, if the traffic light is green and there are no vehicles nearby, it will decide to "cross" the vehicle, and if the traffic light is red or a vehicle is approaching, it will decide to "wait."

[0996] Step 4:

[0997] The server activates the emotion engine to recognize the user's emotional state. Specifically, it collects data such as voice and facial expressions through sensors and cameras in the device worn by the user to determine whether the user is tense or relaxed.

[0998] Step 5:

[0999] The server determines the final action and feedback content based on the analysis results and the emotion engine results. For example, if the user is nervous, the feedback when crossing the street may be gentler than usual to make it easier for the user to cross.

[1000] Step 6:

[1001] The server generates a special signal corresponding to the determined action and transmits it to the child device, for example, a signal that generates a long vibration in the case of a "cross" action, or a signal that generates a short vibration or a voice instruction in the case of a "wait" action.

[1002] Step 7:

[1003] The slave device receives the special signal sent from the server. Specifically, the signal is received using a communication method such as wireless communication or Bluetooth.

[1004] Step 8:

[1005] The child device provides feedback to the user based on the signal it receives. For example, if it receives a "cross" signal, it will vibrate for a long time to let the user know when it is safe to cross. If it receives a "wait" signal, it will vibrate for a short time or provide audio feedback to instruct the user to wait.

[1006] Step 9:

[1007] The user receives feedback from the child device and acts accordingly. For example, if they feel a vibration, they confirm that it is safe to cross and begin walking. On the other hand, if they receive a short vibration or voice instruction, they stop and wait.

[1008] Through this series of processes, the system can provide users with timely feedback, helping people with visual and hearing impairments cross the street safely. The addition of an emotion engine makes it possible to provide feedback that takes into account the user's emotional state, providing an even greater sense of safety and security.

[1009] Example 2

[1010] 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."

[1011] The problem is that there are not enough systems in place to solve the problems faced by the elderly and people with visual and hearing impairments when crossing the street safely, especially at night or in busy traffic environments.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1013] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means including an emotion engine for recognizing the emotional state of the user, means for transmitting a determined action to the child device based on the analysis result and the emotion recognition result, and means for providing feedback to the user based on the signal received by the child device, thereby enabling visually and hearing impaired users to cross the crosswalk safely and with peace of mind.

[1014] A "camera" is a device that captures information about the surrounding environment as an image.

[1015] A "sensor" is a device that acquires physical data about its surroundings (e.g., vehicle movement, pedestrian presence, traffic light status, etc.).

[1016] "Artificial intelligence" refers to programs and systems that analyze data obtained from cameras and sensors and make situational judgments.

[1017] An "emotion engine" is software and hardware that recognizes the user's emotional state from their voice and facial expressions.

[1018] A "child device" is a device that receives signals sent from the server and provides feedback to the user.

[1019] "Feedback" refers to instructions or information that a user receives from a system, and specifically includes vibrations and audio instructions.

[1020] The present invention relates to a system that enables physically disabled people with visual and hearing impairments and elderly people to cross pedestrian crossings safely. This system is mainly composed of a server, a child device, and an emotion engine. A specific embodiment of this system will be described in detail below.

[1021] Server functions and hardware / software used

[1022] The server operates using the following hardware and software:

[1023] Environmental information collection: The server uses cameras and sensors to collect information about the surrounding environment. In a specific example, a Hikvision camera is used, with a LiDAR sensor capturing physical data about the surroundings.

[1024] Data analysis: The server analyzes the acquired data using a generative AI model such as TensorFlow, which determines the color of the traffic light (e.g., red or green), vehicle movement, and the presence of pedestrians.

[1025] Emotion recognition using an emotion engine: The server uses Affectiva's SDK to recognize the user's emotional state. It analyzes emotions such as tension or relaxation based on the user's voice and facial expression data.

[1026] Functions of child devices

[1027] The slave device has the following functions:

[1028] Signal reception: The slave device receives a signal from the server. There are no particular restrictions on the communication method used, but Wi-Fi, Bluetooth, etc. are possible.

[1029] Providing feedback: The child device provides feedback to the user based on the received signal, for example, a long vibration for the "cross" action, a short vibration for the "wait" action, or a voice instruction. Specifically, an Arduino-based vibration motor is used.

[1030] Server Processing Details

[1031] The server performs the process in the following procedure.

[1032] 1. Environmental information collection: The server captures video data from Hikvision cameras and collects physical data using LiDAR sensors, allowing it to understand the situation at intersections and crosswalks in real time.

[1033] 2. Data analysis: The server uses the TensorFlow model to analyze the acquired video and sensor data, checking the color of traffic lights and vehicle movements to determine whether it is safe.

[1034] 3. Emotion Recognition by Emotion Engine: The server uses Affectiva's SDK to recognize the user's emotional state. For example, it analyzes whether the user is tense or relaxed.

[1035] 4. Information transmission: Based on the analysis results and emotion recognition results, the appropriate action is determined and a signal is sent to the child device.

[1036] Specific examples

[1037] For example, consider a situation where a user is using this system at a busy intersection at night. The server collects environmental information through Hikvision's camera and LiDAR sensor, and analyzes that data in real time using a TensorFlow model. The moment the light turns green, the server confirms that surrounding vehicles are stopped, and if it determines that it is safe, it sends a "cross" signal to the child device. If the server detects that the user is nervous using Affectiva's SDK, it can add settings to provide gentler feedback.

[1038] The device worn by the user generates a long vibration based on the received "cross" signal. The user feels the vibration and can cross the crosswalk safely. On the other hand, if the light is red or a vehicle is approaching, the server sends a "wait" signal and provides a short vibration or sound to instruct the user to wait.

[1039] Prompt Sentence Examples

[1040] "I would like to design a system that instructs pedestrians to cross safely when vehicles are stopped at a busy intersection the moment the light turns green. Please explain in detail how the emotion engine can detect whether pedestrians are nervous and adjust the feedback accordingly."

[1041] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1042] Step 1:

[1043] Collecting environmental information (server)

[1044] The server receives input from cameras and sensors to obtain streaming data. Cameras provide real-time video of the area around intersections and crosswalks. Sensors (e.g., LiDAR sensors) collect physical data such as vehicle movement, pedestrian presence, and traffic light status. This data is input to the server.

[1045] Input: Video data from cameras, physical data from sensors

[1046] Output: Video data and physical data are stored in the server's memory.

[1047] How it works: The server captures video from a Hikvision camera at 30 frames per second and obtains 3D scan data of surrounding objects from a LiDAR sensor.

[1048] Step 2:

[1049] Data analysis and judgment (server)

[1050] The server uses AI models such as TensorFlow to analyze the acquired data. First, it extracts the color of traffic lights and the location of pedestrians from the video data, and then identifies the movement and location of vehicles from the physical data. This analysis determines the current situation, such as whether the light is green and a vehicle is stopped, or whether the light is red and a vehicle is approaching.

[1051] Input: Visual and physical data collected in Step 1

[1052] Output: A decision indicating whether it is safe to cross.

[1053] How it works: The AI ​​model analyzes camera footage to detect the status of traffic lights. It uses data from sensors to determine the speed and location of surrounding vehicles. For example, if the light is green and the vehicle's speed is close to zero, it determines that it is safe and decides to "cross" the road.

[1054] Step 3:

[1055] Emotion recognition by emotion engine (server)

[1056] The server uses Affectiva's emotion recognition engine to recognize the user's emotional state from their voice and facial expression data, thereby determining whether the user is tense or relaxed and providing feedback accordingly.

[1057] Input: User's voice and facial expression data

[1058] Output: Data indicating the user's emotional state

[1059] How it works: The server inputs the user's voice and facial expression data into the Affectiva SDK, which analyzes their emotional state in real time. For example, if the user's voice is high-pitched or their facial expression shows signs of impatience, the emotion engine will determine that the user is nervous.

[1060] Step 4:

[1061] Information transmission (server → child device)

[1062] The server determines the appropriate action based on the analysis results and emotion recognition results, and sends the result to the child device. Specifically, it generates a signal including a long vibration for the "cross" action and a short vibration or voice instruction for the "wait" action.

[1063] Input: Judgment result indicating safety of crossing, emotional state of the user

[1064] Output: Signal sent to child device

[1065] Specific operation: If the result of the judgment is "cross," the server sends a signal to the child device that generates a long vibration. If the result of the judgment is "wait," the server sends a signal to generate a short vibration or a voice instruction.

[1066] Step 5:

[1067] Signal reception and feedback (child device)

[1068] The child device receives the signal sent from the server and provides feedback to the user based on the signal. For example, in the case of a "cross" signal, the child device operates a vibration motor for a long period of time to let the user know when it is time to cross.

[1069] Input: Signal from the server

[1070] Output: Feedback to the user (vibration and audio instructions)

[1071] Specific operation: The slave device analyzes the signal received from the server and controls the vibration motor equipped with Arduino. When the user receives the "cross" signal, the vibration motor generates a long vibration, allowing the user to cross the crosswalk safely.

[1072] (Application example 2)

[1073] 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."

[1074] In systems that enable the visually and hearing impaired, physically disabled, and elderly people to cross crosswalks safely at night, in heavy traffic, or at crowded crosswalks, etc., ensuring the safety and efficiency of workers, particularly in factories, requires real-time analysis of the working environment and providing appropriate feedback that takes into account the emotional state of the worker. However, conventional technologies were unable to recognize the emotional state of the worker and adjust the feedback accordingly, making it difficult to achieve both safety and comfort.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1076] In this invention, the server includes means for collecting environmental information using a camera, means for acquiring surrounding physical data using a sensor, means including artificial intelligence for analyzing data acquired from the camera and the sensor, means for transmitting actions determined based on the analysis results to the slave device, means for providing feedback to the user based on signals received by the slave device, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the feedback content based on the user's emotional state. This makes it possible to flexibly provide safety measures according to the worker's emotional state, thereby achieving both safety and comfort in the work environment.

[1077] A "camera" is a device for recording video or images.

[1078] "Environmental information" is data about the surrounding physical conditions and situations.

[1079] A "sensor" is a device for detecting and collecting physical data.

[1080] "Artificial intelligence" refers to algorithms and systems that analyze acquired data and make appropriate decisions and processes.

[1081] "Analysis results" are interpretations and conclusions obtained through data analysis.

[1082] An "action" is a specific movement or behavior that is determined based on the analysis results.

[1083] A "child device" is a device that receives signals from the server and provides feedback to the user.

[1084] "Feedback" refers to information or instructions provided to a user.

[1085] An "emotion engine" is a system or algorithm for recognizing and analyzing a user's emotional state.

[1086] "Emotional state" refers to the mental or emotional state of a user.

[1087] "Adjustment" means changing the content of the feedback to suit the user's emotional state.

[1088] This invention provides a system that can be applied to a system that enables the visually and hearing impaired and elderly to cross crosswalks safely at night or in crowded areas, thereby improving the safety and comfort of workers in factories. This system is mainly composed of cameras, sensors, artificial intelligence, an emotion engine, and sub-devices.

[1089] The server first uses cameras to collect environmental information, specifically capturing the movements of machines and workers in specific areas of the factory, and then uses sensors to acquire surrounding physical data, including temperature, vibration, and location information.

[1090] The server then uses artificial intelligence to analyze the data acquired from the cameras and sensors, detecting specific patterns and anomalies. For example, if a machine is moving and a worker is approaching, it may decide to take a "standby" (wait) action.

[1091] The emotion engine then recognizes the user's emotional state by analyzing their facial expressions and tone of voice to understand whether they are tense or relaxed.

[1092] Based on the analysis results, the server generates a specific signal based on the determined action and the recognition result of the emotion engine and sends it to the child device. For example, in the case of a "standby" action, the child device sends a signal with a short vibration or voice to inform the user to wait. On the other hand, in the case of a "safety" action, it sends a signal with a long vibration or voice to instruct the user to proceed with the work safely.

[1093] The child device provides feedback to the user based on the signal received from the server. If the emotion engine recognizes that the user is nervous, the child device provides gentler feedback (e.g., gentler voice instructions) than usual to help the user act with confidence.

[1094] The hardware used includes cameras (for example, general IP cameras), various sensors (such as temperature and vibration sensors), and a computer server. The software used includes video analysis using OpenCV and neural network models using Keras. A specific example of its use is analyzing data collected by the camera to determine safety and providing appropriate feedback to workers through audio or vibration.

[1095] Examples of prompts for generative AI models include:

[1096] "Generate code for an application that uses an analytical model of the factory environment to determine in real time whether it is safe or not, and an emotion engine to determine the emotional state of workers."

[1097] This prompt allows the AI ​​model to generate code for the system responsible for analyzing the environment and recognizing emotional states.

[1098] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1099] Step 1:

[1100] The server uses cameras to collect environmental information. Specifically, cameras are installed to monitor specific areas within the factory and capture video data in real time. This captured video data is input. The input video data is sent directly to the next step.

[1101] Step 2:

[1102] The server uses sensors to acquire surrounding physical data. For example, a temperature sensor measures the temperature inside a factory, and a vibration sensor checks the operating status of machines. This physical data is the input. The input physical data is sent to the next step along with the video data.

[1103] Step 3:

[1104] The server uses artificial intelligence to analyze the data acquired from the cameras and sensors. Specifically, it preprocesses the video data using OpenCV and inputs it into a learning model using Keras. The input is the data collected in steps 1 and 2. This data is analyzed to detect specific patterns and anomalies. The results of this analysis are sent to the next step. The output is the action of "stay safe" or "evacuate" as a result of the analysis.

[1105] Step 4:

[1106] The server uses an emotion engine to recognize the user's emotional state. Specifically, it collects and analyzes data on the user's face and voice to determine the user's emotional state. The input is the user's face and voice data. The output is the user's emotional state, such as "tension" or "relaxation," and this data is sent to the next step.

[1107] Step 5:

[1108] The server determines the appropriate action based on the analysis results and the emotional state. Specifically, if it is safe, it determines a "safe" action, and if evacuation is necessary, it determines an "evacuation" action. Also, if the emotional state is tense, it determines to provide gentler feedback. The inputs are the analysis results from step 3 and the emotional state from step 4. The output is the type of action and the feedback content.

[1109] Step 6:

[1110] The server sends the determined action and feedback content to the child device. Specifically, the feedback content is generated as a vibration or sound signal and sent to the child device. The input is the type of action and feedback content from step 5. The output is the signal sent to the child device.

[1111] Step 7:

[1112] The slave device receives the signal from the server and provides feedback to the user. Specifically, based on the received signal, it provides instructions such as "take shelter" or "safe" to the user through vibration or sound. The input is the signal sent to the slave device in step 6. The output is the vibration or sound feedback provided to the user.

[1113] 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.

[1114] 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.

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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).

[1120] 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.

[1121] 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."

[1122] 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.

[1123] 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).

[1124] 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.

[1125] 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.

[1126] 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.

[1127] 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.

[1128] 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.

[1129] 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.

[1130] 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.

[1131] 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.

[1132] 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.

[1133] 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.

[1134] The following is further disclosed regarding the above embodiment.

[1135] (Claim 1)

[1136] a means for collecting environmental information using a camera;

[1137] a means for acquiring surrounding physical data using sensors;

[1138] means including artificial intelligence for analyzing data obtained from the cameras and sensors;

[1139] means for transmitting an action determined based on the analysis result to the child device;

[1140] means for providing feedback to a user based on the signals received by the child device;

[1141] A system including:

[1142] (Claim 2)

[1143] 2. The system according to claim 1, wherein a camera is used as a means for collecting environmental information.

[1144] (Claim 3)

[1145] The system according to claim 1, characterized in that a sensor is used as a means for acquiring surrounding physical data.

[1146] (Claim 4)

[1147] 10. The system of claim 1, wherein the signal sent to the child device uses a specific vibration pattern to encourage pedestrians to cross the street.

[1148] (Claim 5)

[1149] The system according to claim 1, characterized in that the state of traffic lights at the intersection and the movement of surrounding vehicles are determined based on the analysis results.

[1150] (Claim 6)

[1151] 10. The system of claim 1, wherein the means for providing feedback based on the signal received by the slave device includes at least one of a vibration alert and an audio prompt.

[1152] "Example 1"

[1153] (Claim 1)

[1154] means for collecting environmental information using a device for acquiring video data and a device for acquiring sensor data;

[1155] means for providing artificial intelligence for analyzing the collected data;

[1156] means for generating a signal corresponding to an action determined based on the analysis result and transmitting the signal to the slave device;

[1157] A system including means for providing feedback to a user based on signals received by a child device.

[1158] (Claim 2)

[1159] 2. The system according to claim 1, wherein a camera is used as the device for acquiring the video data.

[1160] (Claim 3)

[1161] The system according to claim 1, characterized in that various sensors (e.g., distance measuring devices) are used as devices for acquiring sensor data.

[1162] "Application Example 1"

[1163] (Claim 1)

[1164] a means for collecting environmental information using a camera;

[1165] a means for acquiring surrounding physical data using sensors;

[1166] means including artificial intelligence for analyzing data obtained from the cameras and sensors;

[1167] means for transmitting an action determined based on the analysis result to the child device;

[1168] means for providing feedback to a user based on the signals received by the child device;

[1169] means for providing visual and tactile feedback to a user using the smart device;

[1170] A system including:

[1171] (Claim 2)

[1172] 2. The system according to claim 1, wherein a camera is used as a means for collecting environmental information.

[1173] (Claim 3)

[1174] The system according to claim 1, characterized in that a sensor is used as a means for acquiring surrounding physical data.

[1175] "Example 2: Combining Emotion Engines"

[1176] (Claim 1)

[1177] a means for collecting environmental information using a camera;

[1178] a means for acquiring surrounding physical data using sensors;

[1179] means including artificial intelligence for analyzing data obtained from the cameras and sensors;

[1180] means including an emotion engine for recognizing an emotional state of a user;

[1181] means for transmitting a determined action to the child device based on the analysis result and the emotion recognition result;

[1182] means for providing feedback to a user based on the signals received by the child device;

[1183] A system including:

[1184] (Claim 2)

[1185] 2. The system according to claim 1, wherein a camera is used as a means for collecting environmental information.

[1186] (Claim 3)

[1187] The system according to claim 1, characterized in that a sensor is used as a means for acquiring surrounding physical data.

[1188] "Application example 2 when combining emotion engines"

[1189] (Claim 1)

[1190] a means for collecting environmental information using a camera;

[1191] a means for acquiring surrounding physical data using sensors;

[1192] means including artificial intelligence for analyzing data obtained from the cameras and sensors;

[1193] means for transmitting an action determined based on the analysis result to the child device;

[1194] means for providing feedback to a user based on the signals received by the child device;

[1195] means for recognizing an emotional state of a user using an emotion engine;

[1196] means for adjusting the feedback content based on the user's emotional state;

[1197] A system including:

[1198] (Claim 2)

[1199] 2. The system according to claim 1, wherein a camera is used as a means for collecting environmental information.

[1200] (Claim 3)

[1201] The system according to claim 1, characterized in that a sensor is used as a means for acquiring surrounding physical data. [Explanation of symbols]

[1202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting environmental information using a camera; a means for acquiring surrounding physical data using sensors; means including artificial intelligence for analyzing data obtained from the cameras and sensors; means for transmitting an action determined based on the analysis result to the child device; means for providing feedback to a user based on the signals received by the child device; A system including:

2. 2. The system according to claim 1, wherein a camera is used as a means for collecting environmental information.

3. 2. The system according to claim 1, wherein a sensor is used as a means for acquiring surrounding physical data.

4. 2. The system of claim 1, wherein the signal transmitted to the child device uses a specific vibration pattern to encourage pedestrians to cross the street.

5. 2. The system according to claim 1, wherein the state of traffic lights at the intersection and the movement of surrounding vehicles are determined based on the analysis results.

6. 2. The system of claim 1, wherein the means for providing feedback based on the signal received by the slave device includes at least one of a vibration alert and an audio prompt.

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