System

Abstract

An object of a system according to an embodiment is to find a traffic accident at an intersection at an early stage and to quickly cope with the traffic accident.SOLUTION: A system includes a monitoring unit, a detection unit, a transmission unit, a grasping unit, a determination unit, and a notification unit. The monitoring unit monitors a video of an intersection in real time by a remote camera installed in a traffic signal. The detection unit detects occurrence of a traffic accident based on the video monitored by the monitoring unit. The transmission unit transmits the information detected by the detection unit to the AI through the 5G station. The grasping unit analyzes the information transmitted by the transmission unit and grasps a situation of the accident. The determination unit determines a response content based on the situation grasped by the grasping unit. The notification unit transmits the information determined by the determination unit to the medical institution or the police station.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately detect traffic accidents at intersections early and respond quickly, so there is room for improvement.

[0005] The system according to the embodiment aims to detect traffic accidents at intersections early and respond to them promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, a transmission unit, a grasping unit, a determination unit, and a notification unit. The monitoring unit monitors images of intersections in real time using remote cameras installed at traffic signals. The detection unit detects the occurrence of a traffic accident based on the images monitored by the monitoring unit. The transmission unit transmits the information detected by the detection unit to an AI via a 5G base station. The grasping unit analyzes the information transmitted by the transmission unit and grasps the situation of the accident. The determination unit determines the response based on the situation grasped by the grasping unit. The notification unit transmits the information determined by the grasping unit to a medical institution or police station. [Effects of the Invention]

[0007] The system according to the embodiment can detect traffic accidents at intersections early and respond quickly. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A traffic accident processing system according to an embodiment of the present invention is a system that realizes early detection of traffic accidents and rapid response. The traffic accident processing system monitors intersection video in real time using remote cameras installed at traffic signals and detects traffic accidents using image recognition technology. The detected information is transmitted to an AI via a 5G base station, which analyzes the received information and grasps the accident situation. The AI ​​determines the scale of the accident, the number of vehicles involved, whether there are any injuries, etc., and transmits information for emergency vehicles and traffic control to medical institutions, police stations, etc. as needed. For example, the traffic accident processing system monitors intersection video in real time and detects abnormal behavior such as vehicle collisions and pedestrian falls. The detected information is then transmitted to an AI via a 5G base station, which determines the scale of the accident, the number of vehicles involved, whether there are any injuries, etc., and transmits information for emergency vehicles and traffic control to medical institutions, police stations, etc. as needed. This enables the traffic accident processing system to realize early detection of traffic accidents and rapid response, thereby minimizing damage caused by traffic accidents. This enables the traffic accident processing system to realize early detection of traffic accidents and rapid response, thereby minimizing damage caused by traffic accidents. For example, if emergency vehicles arrive at the scene immediately after an accident, and the injured are rescued and traffic control is implemented promptly, secondary damage can be prevented and traffic can be made smoother. Furthermore, since AI automatically grasps the accident situation and determines what response should be made, it is expected that human error will be reduced and responses will be made more quickly. For example, if AI accurately grasps the scale of the accident and takes appropriate action, it will be possible to prevent unnecessary dispatches of emergency vehicles.

[0029] A traffic accident processing system according to an embodiment includes a monitoring unit, a detection unit, a transmission unit, a determination unit, a judgment unit, and a notification unit. The monitoring unit monitors intersection video in real time using remote cameras installed at traffic signals. For example, the monitoring unit can install multiple cameras to cover the entire intersection and monitor video in real time. The monitoring unit can also be equipped with an infrared camera and waterproofing to capture clear video even at night or in bad weather. The detection unit detects traffic accidents using image recognition technology. For example, the detection unit can detect abnormal movements such as vehicle collisions and pedestrian falls. The detection unit can also identify vehicle license plates and pedestrian faces to obtain detailed information about the accident. The transmission unit transmits information to an AI via a 5G base station. For example, the transmission unit can quickly transmit the detected information to the AI, allowing for rapid analysis by the AI. The determination unit analyzes the information received by the AI ​​and determines the accident situation. For example, the determination unit can determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. The determination unit uses AI to determine the scale of the accident, the number of vehicles involved, whether there are any injuries, etc. For example, the determination unit can select the optimal response based on the scale of the accident and determine the need for emergency vehicles and traffic restrictions. The notification unit transmits information determined by the AI ​​to medical institutions, police stations, etc. For example, the notification unit can quickly transmit information for the dispatch of emergency vehicles and traffic restrictions, allowing for a prompt emergency response. As a result, the traffic accident processing system according to the embodiment can realize early detection of traffic accidents and rapid response, thereby minimizing damage caused by traffic accidents.

[0030] The monitoring unit can monitor the intersection video in real time using a remote camera installed at the traffic light. The monitoring unit can, for example, install multiple cameras to cover the entire intersection and monitor the video in real time. The monitoring unit can also be equipped with an infrared camera and waterproof functionality to capture clear video even at night or in bad weather. This enables early detection of traffic accidents by monitoring the intersection video in real time. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input video data acquired by the camera into a generation AI and have the generation AI analyze the video data.

[0031] The detection unit can detect the occurrence of a traffic accident using image recognition technology. The detection unit can detect abnormal movements such as vehicle collisions and pedestrian falls, for example. The detection unit can also identify vehicle license plates and pedestrian faces to obtain detailed information about the accident. Furthermore, the detection unit can analyze the vehicle's speed and direction of travel to identify the cause of the accident. This makes it possible to accurately detect the occurrence of a traffic accident using image recognition technology. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the video data.

[0032] The transmitting unit can transmit information to the AI ​​through a 5G base station. For example, the transmitting unit can quickly transmit detected information to the AI, allowing for rapid analysis by the AI. The transmitting unit can also encrypt the transmitted data to ensure communication security. Furthermore, the transmitting unit can improve communication speed by using compression technology for the transmitted data. This allows for rapid transmission of information through a 5G base station, allowing for rapid analysis by the AI. Some or all of the above-described processing in the transmitting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmitting unit can input data acquired from the detecting unit to the generating AI and have the generating AI transmit the data.

[0033] The understanding unit can analyze the information received by the AI ​​and understand the circumstances of the accident. The understanding unit can determine, for example, the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. The understanding unit can also record detailed circumstances of the accident and provide them to insurance companies and police. Furthermore, the understanding unit can organize the received information in chronological order and understand the flow from the occurrence of the accident to its resolution. In this way, the AI ​​can analyze the received information and accurately understand the circumstances of the accident. Some or all of the above-mentioned processing in the understanding unit may be performed, for example, using AI, or may be performed without using AI. For example, the understanding unit can input the information received by the AI ​​into the generation AI and have the generation AI analyze the circumstances of the accident.

[0034] The judgment unit allows the AI ​​to determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. The judgment unit can, for example, select the optimal response based on the scale of the accident and determine the need for emergency vehicles and traffic control. The judgment unit can also determine the need for emergency vehicles based on whether or not there are any injuries and their severity. The judgment unit can also determine the need for traffic control based on the location and time of the accident. This allows the AI ​​to accurately determine the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc., enabling appropriate responses. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, AI. For example, the judgment unit can input information received by the AI ​​into the generation AI and have the generation AI determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries.

[0035] The notification unit can transmit the information determined by the AI ​​to a medical institution, a police station, etc. The notification unit can, for example, quickly transmit information for dispatching emergency vehicles or traffic control, thereby enabling a prompt emergency response. The notification unit can also encrypt the notification content to ensure the security of communications. Furthermore, the notification unit can store the notification content in the cloud and make it accessible from a remote location. This allows the prompt transmission of the information determined by the AI, thereby enabling a prompt emergency response. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the information determined by the AI ​​to the generation AI and have the generation AI transmit the information.

[0036] The comprehension unit can record detailed circumstances of the accident and provide them to insurance companies and police. The comprehension unit can, for example, record detailed circumstances of the accident and provide them to insurance companies and police. The comprehension unit can also organize the received information in chronological order and understand the flow from the occurrence of the accident to its resolution. Furthermore, the comprehension unit can display the received information on a map to visually understand the location of the accident. In this way, by recording detailed circumstances of the accident and providing them to insurance companies and police, it is possible to reduce accident investigation costs. Some or all of the above-mentioned processing in the comprehension unit may be performed using AI, for example, or may be performed without using AI. For example, the comprehension unit can input information received by AI to the generation AI and cause the generation AI to record detailed circumstances of the accident.

[0037] The monitoring unit can adjust the installation positions of the surveillance cameras and position them to cover the entire intersection. For example, the monitoring unit can install multiple cameras at different angles to cover the entire intersection. The monitoring unit can also adjust the installation positions of the cameras to enable omnidirectional monitoring in order to eliminate blind spots at the intersection. Furthermore, the monitoring unit can dynamically change the installation positions of the cameras depending on the traffic volume at the intersection to ensure an optimal monitoring range. In this way, the installation positions of the surveillance cameras can be optimized to cover the entire intersection. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the installation positions of the cameras to a generation AI and have the generation AI adjust the installation positions to the optimal positions.

[0038] The monitoring unit can record surveillance camera footage in high resolution, enabling detailed accident conditions to be grasped. For example, the monitoring unit uses a high-resolution camera to clearly record vehicle license plates and pedestrian faces. The monitoring unit can also save the high-resolution footage so that detailed accident conditions can be checked later. Furthermore, the monitoring unit can analyze the high-resolution footage in real time to identify the cause of the accident. Thus, by recording the footage in high resolution, detailed accident conditions can be grasped. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input high-resolution video data acquired by the camera to a generation AI and have the generation AI record and analyze the video.

[0039] The monitoring unit can be equipped with an infrared camera or waterproof functionality to enable clear capture of surveillance camera footage even at night or in bad weather. For example, the monitoring unit uses an infrared camera to capture clear footage even at night. The monitoring unit can also be equipped with waterproof functionality to prevent degradation of footage even in rainy weather. Furthermore, the monitoring unit can apply an anti-fog coating to the camera lens to provide high-quality footage even in bad weather. This allows clear footage to be captured even at night or in bad weather, enabling accurate understanding of the accident situation. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input video data acquired by the camera into a generation AI and have the generation AI analyze the footage at night or in bad weather.

[0040] The monitoring unit can analyze surveillance camera footage in real time and issue an alert if it detects abnormal activity. The monitoring unit detects abnormal activity, such as a vehicle collision or a pedestrian fall, in real time. Furthermore, if the monitoring unit detects abnormal activity, it can immediately issue an alert and notify relevant parties. Furthermore, if the monitoring unit detects abnormal activity, it can automatically save the video so that it can be reviewed later. This enables rapid response by detecting abnormal activity in real time and issuing an alert. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input video data acquired by a camera into a generation AI and have the generation AI detect abnormal activity and issue an alert.

[0041] The monitoring unit can store surveillance camera footage in the cloud and make it accessible from a remote location. For example, the monitoring unit automatically uploads surveillance camera footage to the cloud. The monitoring unit can also remotely access the footage stored in the cloud and view it in real time. Furthermore, the monitoring unit can make the footage stored in the cloud simultaneously accessible from multiple devices. This allows the footage stored in the cloud to be accessed remotely and viewed in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by a camera to a generation AI and have the generation AI store the data in the cloud and access it from a remote location.

[0042] The monitoring unit can optimize traffic flow by linking surveillance camera footage with other traffic management systems. For example, the monitoring unit can provide surveillance camera footage to a traffic management system in real time to optimize traffic flow. The monitoring unit can also link with a traffic management system to automatically regulate traffic when an accident occurs. Furthermore, the monitoring unit can dynamically adjust the timing of traffic signals based on surveillance camera footage to alleviate traffic congestion. This allows for optimization of traffic flow by linking with other traffic management systems. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by a camera into a generation AI and have the generation AI execute linking with the traffic management system.

[0043] The detection unit can use image recognition technology to identify vehicle license plates and pedestrian faces and acquire detailed information about the accident. For example, the detection unit can use image recognition technology to automatically identify vehicle license plates and acquire detailed information about the accident. The detection unit can also use image recognition technology to identify pedestrian faces and acquire detailed information about the accident. Furthermore, the detection unit can also use image recognition technology to identify the type and color of vehicles and acquire detailed information about the accident. In this way, by using image recognition technology, detailed information about the accident can be accurately acquired. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI identify license plates and faces.

[0044] The detection unit can use image recognition technology to analyze the speed and direction of travel of a vehicle and identify the cause of an accident. The detection unit can, for example, use image recognition technology to analyze the speed of a vehicle and identify the cause of an accident. The detection unit can also use image recognition technology to analyze the direction of travel of a vehicle and identify the cause of an accident. Furthermore, the detection unit can also use image recognition technology to analyze the behavior of a vehicle and identify the cause of an accident. As a result, the use of image recognition technology makes it possible to accurately identify the cause of an accident. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the speed and direction of travel.

[0045] The detection unit can use image recognition technology to automatically save video footage before and after an accident occurs and use it as evidence. The detection unit can, for example, use image recognition technology to automatically save video footage before and after an accident occurs. The detection unit can also use the saved video footage as evidence to identify the cause of the accident. Furthermore, the detection unit can provide the saved video footage to insurance companies or police to share detailed information about the accident. In this way, by saving video footage before and after an accident occurs, it can be used as evidence. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI save and analyze the video.

[0046] The detection unit can use image recognition technology to analyze traffic signal status and road signs and identify the causes of accidents. The detection unit can, for example, use image recognition technology to analyze traffic signal status and identify the causes of accidents. The detection unit can also use image recognition technology to analyze road signs and identify the causes of accidents. Furthermore, the detection unit can also use image recognition technology to analyze traffic signal timing and identify the causes of accidents. In this way, the causes of accidents can be identified by analyzing traffic signal status and road signs. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to the generation AI and have the generation AI analyze traffic signals and road signs.

[0047] The detection unit can use image recognition technology to identify the type and color of the vehicle and acquire detailed information about the accident. The detection unit can, for example, use image recognition technology to identify the type of vehicle and acquire detailed information about the accident. The detection unit can also use image recognition technology to identify the color of the vehicle and acquire detailed information about the accident. The detection unit can also use image recognition technology to identify vehicle features and acquire detailed information about the accident. In this way, detailed information about the accident can be accurately acquired by identifying the type and color of the vehicle. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI identify the type and color of the vehicle.

[0048] The detection unit can use image recognition technology to analyze the location and time of accident occurrence and grasp accident trends. The detection unit can, for example, use image recognition technology to analyze the location of accident occurrence and grasp accident trends. The detection unit can also use image recognition technology to analyze the time of accident occurrence and grasp accident trends. Furthermore, the detection unit can also use image recognition technology to analyze accident occurrence patterns and grasp accident trends. In this way, accident trends can be grasped by analyzing the location and time of accident occurrence. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the location and time of accident occurrence.

[0049] The transmitting unit can encrypt the transmission data to ensure the security of communications. The transmitting unit can, for example, encrypt the transmission data using AES encryption technology to ensure the security of communications. The transmitting unit can also encrypt the transmission data using SSL / TLS protocol to ensure the security of communications. Furthermore, the transmitting unit can also encrypt the transmission data using RSA encryption technology to ensure the security of communications. In this way, by encrypting the transmission data, the security of communications can be ensured. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and have the generating AI perform the encryption.

[0050] The transmitting unit can improve the communication speed by using a compression technique for the transmission data. For example, the transmitting unit can compress the transmission data using ZIP compression technology to improve the communication speed. The transmitting unit can also compress the transmission data using GZIP compression technology to improve the communication speed. Furthermore, the transmitting unit can compress the transmission data using LZMA compression technology to improve the communication speed. In this way, the communication speed can be improved by compressing the transmission data. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generation AI and have the generation AI perform compression.

[0051] The transmitting unit can automatically create a backup of the transmission data to prevent data loss. The transmitting unit can, for example, automatically back up the transmission data to a cloud to prevent data loss. The transmitting unit can also automatically back up the transmission data to local storage to prevent data loss. Furthermore, the transmitting unit can automatically back up the transmission data to distributed storage to prevent data loss. In this way, data loss can be prevented by automatically creating a backup of the transmission data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and have the generating AI create a backup.

[0052] The transmitting unit can improve the reliability of communication by making the transmission data compatible with multiple communication protocols. The transmitting unit can improve the reliability of communication by making the transmission data compatible with both HTTP and HTTPS, for example. The transmitting unit can also improve the reliability of communication by making the transmission data compatible with both TCP and UDP. Furthermore, the transmitting unit can also improve the reliability of communication by making the transmission data compatible with both MQTT and CoAP. In this way, by making the transmission data compatible with multiple communication protocols, the reliability of communication can be improved. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and cause the generating AI to support multiple communication protocols.

[0053] The transmitting unit can share the transmission data with other traffic management systems to achieve centralized management of information. The transmitting unit, for example, can share the transmission data with other traffic management systems in real time to achieve centralized management of information. The transmitting unit can also link the transmission data with other traffic management systems to optimize traffic flow. Furthermore, the transmitting unit can share the transmission data with other traffic management systems to speed up accident response. In this way, sharing the transmission data with other traffic management systems enables centralized management of information. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and have the generating AI share the data with other traffic management systems.

[0054] The transmission unit can monitor the transmission data in real time and issue an alert if an abnormality occurs. For example, the transmission unit can monitor the transmission data in real time and immediately issue an alert if an abnormality occurs. The transmission unit can also monitor the transmission data and automatically create a backup if an abnormality occurs. Furthermore, the transmission unit can monitor the transmission data and notify relevant parties if an abnormality occurs. In this way, by monitoring the transmission data in real time, it is possible to respond quickly if an abnormality occurs. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input the transmission data to a generation AI and have the generation AI perform real-time monitoring and issue an alert.

[0055] The comprehension unit can organize the received information in chronological order and grasp the flow from the occurrence of an accident to its resolution. The comprehension unit, for example, can organize the received information in chronological order and visually display the flow from the occurrence of an accident to its resolution. The comprehension unit can also organize the received information in chronological order and record the flow from the occurrence of an accident to its resolution in detail. Furthermore, the comprehension unit can also organize the received information in chronological order and analyze the flow from the occurrence of an accident to its resolution. In this way, by organizing the received information in chronological order, the flow from the occurrence of an accident to its resolution can be grasped. Some or all of the above-mentioned processing in the comprehension unit may be performed, for example, using AI, or may be performed without using AI. For example, the comprehension unit can input the received information to a generation AI and have the generation AI organize and display the information in chronological order.

[0056] The grasping unit displays the received information on a map, and is able to visually grasp the location of the accident. The grasping unit, for example, displays the received information on a map, and is able to visually grasp the location of the accident. The grasping unit can also display the received information on a map and record the location of the accident in detail. Furthermore, the grasping unit can also display the received information on a map and analyze the location of the accident. In this way, by displaying the received information on a map, it is possible to visually grasp the location of the accident. Some or all of the above-described processing in the grasping unit may be performed, for example, using AI, or may be performed without using AI. For example, the grasping unit may input the received information to a generation AI and cause the generation AI to display the information on a map.

[0057] The grasping unit can statistically analyze the received information to grasp trends and patterns of accidents. The grasping unit, for example, statistically analyzes the received information to grasp trends of accidents. The grasping unit can also statistically analyze the received information to grasp patterns of accidents. Furthermore, the grasping unit can statistically analyze the received information to identify the cause of the accident. In this way, trends and patterns of accidents can be grasped by statistically analyzing the received information. Some or all of the above-described processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input the received information to a generation AI and cause the generation AI to perform statistical analysis.

[0058] The grasping unit can coordinate the received information with other traffic management systems to minimize the impact of accidents. For example, the grasping unit can coordinate the received information with other traffic management systems to minimize the impact of accidents. The grasping unit can also share the received information with other traffic management systems to optimize traffic flow. Furthermore, the grasping unit can coordinate the received information with other traffic management systems to achieve a rapid accident response. In this way, by coordinating with other traffic management systems, the impact of accidents can be minimized. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input the received information into a generation AI and cause the generation AI to coordinate with other traffic management systems.

[0059] The grasping unit can store the received information in a cloud and make it accessible from a remote location. For example, the grasping unit can automatically store the received information in a cloud and make it accessible from a remote location. The grasping unit can also access the information stored in the cloud from a remote location and check it in real time. Furthermore, the grasping unit can make the information stored in the cloud accessible from multiple devices simultaneously. This allows the information stored in the cloud to be accessed from a remote location and checked in real time. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input the received information to a generation AI and have the generation AI store the information in the cloud and access it from a remote location.

[0060] The grasping unit can analyze the received information in real time, enabling a rapid response. The grasping unit can, for example, analyze the received information in real time, enabling a rapid response. The grasping unit can also analyze the received information in real time and identify the cause of the accident. Furthermore, the grasping unit can analyze the received information in real time and determine the need for an emergency response. This enables a rapid response by analyzing the received information in real time. Some or all of the above-described processing in the grasping unit may be performed, for example, using AI, or may be performed without using AI. For example, the grasping unit can input the received information to a generation AI and cause the generation AI to perform real-time analysis.

[0061] The judgment unit can select an appropriate response based on the scale of the accident and the number of vehicles involved. The judgment unit selects the optimal response based on, for example, the scale of the accident. The judgment unit can also select the optimal response based on the number of vehicles involved. The judgment unit can also select the optimal response based on the scale of the accident and the number of vehicles involved. This enables a quick and appropriate response by selecting the optimal response based on the scale of the accident and the number of vehicles involved. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on the scale of the accident and the number of vehicles involved into the generation AI and have the generation AI select a response.

[0062] The determination unit can determine whether to dispatch an emergency vehicle based on the presence or absence of injured persons and the severity of their injuries. The determination unit can, for example, determine whether to dispatch an emergency vehicle based on the presence or absence of injured persons. The determination unit can also determine whether to dispatch an emergency vehicle based on the severity of the injured persons. The determination unit can also determine whether to dispatch an emergency vehicle based on the presence or absence of injured persons and the severity of their injuries. This enables rapid rescue operations by determining whether to dispatch an emergency vehicle based on the presence or absence of injured persons and the severity of their injuries. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input data on the presence or absence of injured persons and the severity of their injuries to the generation AI, and have the generation AI determine whether to dispatch an emergency vehicle.

[0063] The determination unit can determine the need for traffic restrictions based on the location and time of the accident. The determination unit can determine the need for traffic restrictions based on, for example, the location of the accident. The determination unit can also determine the need for traffic restrictions based on the time of the accident. Furthermore, the determination unit can also determine the need for traffic restrictions based on the location and time of the accident. This makes it possible to reduce traffic congestion by determining the need for traffic restrictions based on the location and time of the accident. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input data on the location and time of the accident into the generation AI and have the generation AI determine the need for traffic restrictions.

[0064] The judgment unit can select an appropriate medical institution or police station based on the circumstances of the accident. The judgment unit, for example, selects the most appropriate medical institution based on the circumstances of the accident. The judgment unit can also select the most appropriate police station based on the circumstances of the accident. Furthermore, the judgment unit can also select the most appropriate medical institution and police station based on the circumstances of the accident. This enables a quick and appropriate response by selecting the most appropriate medical institution or police station based on the circumstances of the accident. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on the circumstances of the accident into the generation AI and have the generation AI select a medical institution or police station.

[0065] The determination unit can cooperate with other traffic management systems to minimize the impact of an accident. For example, the determination unit cooperates with other traffic management systems in real time to minimize the impact of an accident. The determination unit can also share information with other traffic management systems to minimize the impact of an accident. Furthermore, the determination unit can cooperate with other traffic management systems to minimize the impact of an accident. In this way, the impact of an accident can be minimized by coordinating with other traffic management systems. Some or all of the above-described processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input data on the impact of the accident into the generation AI and cause the generation AI to coordinate with other traffic management systems.

[0066] The judgment unit can automatically generate the report content to be sent to the insurance company based on the circumstances of the accident. The judgment unit automatically generates the report content to be sent to the insurance company based on the circumstances of the accident, for example. The judgment unit can also automatically generate a detailed report based on the circumstances of the accident. Furthermore, the judgment unit can automatically generate a concise report based on the circumstances of the accident. This enables the report content to be sent to the insurance company automatically based on the circumstances of the accident, thereby enabling a quick and accurate report. Some or all of the above-mentioned processing in the judgment unit may be performed using AI, for example, or may be performed without using AI. For example, the judgment unit can input data on the circumstances of the accident into a generation AI and cause the generation AI to automatically generate the report content.

[0067] The notification unit can encrypt the notification content to ensure the security of communication. The notification unit can encrypt the notification content using, for example, AES encryption technology to ensure the security of communication. The notification unit can also encrypt the notification content using the SSL / TLS protocol to ensure the security of communication. Furthermore, the notification unit can also encrypt the notification content using RSA encryption technology to ensure the security of communication. In this way, by encrypting the notification content, the security of communication can be ensured. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI perform the encryption.

[0068] The notification unit can improve the reliability of communication by making the notification content compatible with multiple communication protocols. The notification unit can improve the reliability of communication by making the notification content compatible with both HTTP and HTTPS, for example. The notification unit can also improve the reliability of communication by making the notification content compatible with both TCP and UDP. Furthermore, the notification unit can also improve the reliability of communication by making the notification content compatible with both MQTT and CoAP. In this way, by making the notification content compatible with multiple communication protocols, the reliability of communication can be improved. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and cause the generation AI to support multiple communication protocols.

[0069] The notification unit can automatically back up the notification content to prevent data loss. The notification unit can, for example, automatically back up the notification content to a cloud to prevent data loss. The notification unit can also automatically back up the notification content to local storage to prevent data loss. Furthermore, the notification unit can automatically back up the notification content to distributed storage to prevent data loss. In this way, by automatically backing up the notification content, data loss can be prevented. Some or all of the above-described processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the notification content to a generation AI and cause the generation AI to create a backup.

[0070] The notification unit can share the notification content with other traffic management systems to achieve centralized information management. The notification unit, for example, can share the notification content with other traffic management systems in real time to achieve centralized information management. The notification unit can also coordinate the notification content with other traffic management systems to optimize traffic flow. Furthermore, the notification unit can share the notification content with other traffic management systems to speed up accident response. This enables centralized information management by sharing the notification content with other traffic management systems. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI share the notification content with other traffic management systems.

[0071] The notification unit can monitor the notification content in real time and issue an alert if an abnormality occurs. For example, the notification unit can monitor the notification content in real time and immediately issue an alert if an abnormality occurs. The notification unit can also monitor the notification content and automatically create a backup if an abnormality occurs. Furthermore, the notification unit can monitor the notification content and notify relevant parties if an abnormality occurs. In this way, by monitoring the notification content in real time, it is possible to respond quickly if an abnormality occurs. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and cause the generation AI to perform real-time monitoring and issue an alert.

[0072] The notification unit can store the notification content in the cloud and make it accessible from a remote location. For example, the notification unit can automatically store the notification content in the cloud and make it accessible from a remote location. The notification unit can also access the notification content stored in the cloud from a remote location and check it in real time. Furthermore, the notification unit can also make the notification content stored in the cloud accessible from multiple devices simultaneously. This allows the notification content stored in the cloud to be accessed from a remote location and checked in real time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI store the notification content in the cloud and access it from a remote location.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The monitoring unit can monitor intersection footage in real time using remote cameras installed at traffic signals. For example, the monitoring unit can install multiple cameras to cover the entire intersection and monitor footage in real time. The monitoring unit can also be equipped with infrared cameras and waterproofing to capture clear footage even at night or in bad weather. Furthermore, the monitoring unit can analyze traffic volume at the intersection and automatically adjust the camera's viewpoint during peak times to monitor the most important areas. This enables early detection of traffic accidents and rapid response.

[0075] The detection unit can detect the occurrence of a traffic accident using image recognition technology. For example, the detection unit can detect abnormal movements such as a vehicle collision or a pedestrian fall. The detection unit can also identify the vehicle's license plate or the pedestrian's face to obtain detailed information about the accident. Furthermore, the detection unit can analyze the vehicle's speed and direction of travel to identify the cause of the accident. In this way, the use of image recognition technology can accurately detect the occurrence of a traffic accident. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the video data.

[0076] The transmitting unit can transmit information to the AI ​​through a 5G base station. For example, the transmitting unit can quickly transmit detected information to the AI, allowing for rapid analysis by the AI. The transmitting unit can also encrypt the transmitted data to ensure communication security. Furthermore, the transmitting unit can improve communication speed by using compression technology for the transmitted data. This allows for rapid transmission of information through a 5G base station, allowing for rapid analysis by the AI. Some or all of the above-described processing in the transmitting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmitting unit can input data acquired from the detecting unit to the generating AI and have the generating AI transmit the data.

[0077] The understanding unit can analyze the information received by the AI ​​and understand the circumstances of the accident. For example, the understanding unit can determine the scale of the accident, the number of vehicles involved, whether there are any injuries, etc. The understanding unit can also record detailed circumstances of the accident and provide them to insurance companies and police. Furthermore, the understanding unit can organize the received information in chronological order and understand the flow from the occurrence of the accident to its resolution. In this way, the AI ​​can analyze the received information and accurately understand the circumstances of the accident. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input the information received by the AI ​​into the generation AI and have the generation AI analyze the circumstances of the accident.

[0078] The judgment unit allows the AI ​​to determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. For example, the judgment unit can select the optimal response based on the scale of the accident and determine the need for emergency vehicles and traffic control. The judgment unit can also determine the need for emergency vehicles based on whether or not there are any injuries and their severity. Furthermore, the judgment unit can determine the need for traffic control based on the location and time of the accident. This allows the AI ​​to accurately determine the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc., enabling appropriate responses. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, AI. For example, the judgment unit can input information received by the AI ​​into the generation AI and have the generation AI determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries.

[0079] The notification unit can transmit the information determined by the AI ​​to a medical institution, police station, etc. For example, the notification unit can quickly transmit information for dispatching emergency vehicles or traffic control, allowing for a prompt emergency response. The notification unit can also encrypt the notification content to ensure the security of communications. Furthermore, the notification unit can store the notification content in the cloud and make it accessible from a remote location. This allows for a prompt transmission of the information determined by the AI, thereby enabling a prompt emergency response. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the information determined by the AI ​​to the generation AI and have the generation AI transmit the information.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The monitoring unit monitors the intersection in real time using remote cameras installed at traffic signals. For example, multiple cameras can be installed to cover the entire intersection, and they can be equipped with infrared cameras and waterproofing to capture clear images even at night or in bad weather. Step 2: The detection unit detects the occurrence of a traffic accident based on the video captured by the monitoring unit. For example, image recognition technology can be used to detect abnormal movements such as vehicle collisions or pedestrian falls, and detailed information about the accident can be obtained by identifying vehicle license plates and pedestrian faces. Step 3: The transmitter transmits the information detected by the detector to the AI ​​via a 5G base station. For example, the transmitter can quickly transmit the detected information to the AI, allowing it to quickly analyze the information. Step 4: The understanding unit analyzes the information sent by the transmission unit and understands the accident situation, such as the scale of the accident, the number of vehicles involved, and whether there were any injuries. Step 5: The decision unit determines the response based on the situation grasped by the understanding unit. For example, it can select the optimal response based on the scale of the accident and determine the need for emergency vehicles or traffic restrictions. Step 6: The notification unit transmits the information determined by the determination unit to a medical institution or a police station. For example, information for dispatching emergency vehicles or traffic control can be transmitted quickly, enabling a prompt emergency response.

[0082] (Example 2) A traffic accident processing system according to an embodiment of the present invention is a system that realizes early detection of traffic accidents and rapid response. The traffic accident processing system monitors intersection video in real time using remote cameras installed at traffic signals and detects traffic accidents using image recognition technology. The detected information is transmitted to an AI via a 5G base station, which analyzes the received information and grasps the accident situation. The AI ​​determines the scale of the accident, the number of vehicles involved, whether there are any injuries, etc., and transmits information for emergency vehicles and traffic control to medical institutions, police stations, etc. as needed. For example, the traffic accident processing system monitors intersection video in real time and detects abnormal behavior such as vehicle collisions and pedestrian falls. The detected information is then transmitted to an AI via a 5G base station, which determines the scale of the accident, the number of vehicles involved, whether there are any injuries, etc., and transmits information for emergency vehicles and traffic control to medical institutions, police stations, etc. as needed. This enables the traffic accident processing system to realize early detection of traffic accidents and rapid response, thereby minimizing damage caused by traffic accidents. This enables the traffic accident processing system to realize early detection of traffic accidents and rapid response, thereby minimizing damage caused by traffic accidents. For example, if emergency vehicles arrive at the scene immediately after an accident, and the injured are rescued and traffic control is implemented promptly, secondary damage can be prevented and traffic can be made smoother. Furthermore, since AI automatically grasps the accident situation and determines what response should be made, it is expected that human error will be reduced and responses will be made more quickly. For example, if AI accurately grasps the scale of the accident and takes appropriate action, it will be possible to prevent unnecessary dispatches of emergency vehicles.

[0083] A traffic accident processing system according to an embodiment includes a monitoring unit, a detection unit, a transmission unit, a determination unit, a judgment unit, and a notification unit. The monitoring unit monitors intersection video in real time using remote cameras installed at traffic signals. For example, the monitoring unit can install multiple cameras to cover the entire intersection and monitor video in real time. The monitoring unit can also be equipped with an infrared camera and waterproofing to capture clear video even at night or in bad weather. The detection unit detects traffic accidents using image recognition technology. For example, the detection unit can detect abnormal movements such as vehicle collisions and pedestrian falls. The detection unit can also identify vehicle license plates and pedestrian faces to obtain detailed information about the accident. The transmission unit transmits information to an AI via a 5G base station. For example, the transmission unit can quickly transmit the detected information to the AI, allowing for rapid analysis by the AI. The determination unit analyzes the information received by the AI ​​and determines the accident situation. For example, the determination unit can determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. The determination unit uses AI to determine the scale of the accident, the number of vehicles involved, whether there are any injuries, etc. For example, the determination unit can select the optimal response based on the scale of the accident and determine the need for emergency vehicles and traffic restrictions. The notification unit transmits information determined by the AI ​​to medical institutions, police stations, etc. For example, the notification unit can quickly transmit information for the dispatch of emergency vehicles and traffic restrictions, allowing for a prompt emergency response. As a result, the traffic accident processing system according to the embodiment can realize early detection of traffic accidents and rapid response, thereby minimizing damage caused by traffic accidents.

[0084] The monitoring unit can monitor the intersection video in real time using a remote camera installed at the traffic light. The monitoring unit can, for example, install multiple cameras to cover the entire intersection and monitor the video in real time. The monitoring unit can also be equipped with an infrared camera and waterproof functionality to capture clear video even at night or in bad weather. This enables early detection of traffic accidents by monitoring the intersection video in real time. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input video data acquired by the camera into a generation AI and have the generation AI analyze the video data.

[0085] The detection unit can detect the occurrence of a traffic accident using image recognition technology. The detection unit can detect abnormal movements such as vehicle collisions and pedestrian falls, for example. The detection unit can also identify vehicle license plates and pedestrian faces to obtain detailed information about the accident. Furthermore, the detection unit can analyze the vehicle's speed and direction of travel to identify the cause of the accident. This makes it possible to accurately detect the occurrence of a traffic accident using image recognition technology. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the video data.

[0086] The transmitting unit can transmit information to the AI ​​through a 5G base station. For example, the transmitting unit can quickly transmit detected information to the AI, allowing for rapid analysis by the AI. The transmitting unit can also encrypt the transmitted data to ensure communication security. Furthermore, the transmitting unit can improve communication speed by using compression technology for the transmitted data. This allows for rapid transmission of information through a 5G base station, allowing for rapid analysis by the AI. Some or all of the above-described processing in the transmitting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmitting unit can input data acquired from the detecting unit to the generating AI and have the generating AI transmit the data.

[0087] The understanding unit can analyze the information received by the AI ​​and understand the circumstances of the accident. The understanding unit can determine, for example, the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. The understanding unit can also record detailed circumstances of the accident and provide them to insurance companies and police. Furthermore, the understanding unit can organize the received information in chronological order and understand the flow from the occurrence of the accident to its resolution. In this way, the AI ​​can analyze the received information and accurately understand the circumstances of the accident. Some or all of the above-mentioned processing in the understanding unit may be performed, for example, using AI, or may be performed without using AI. For example, the understanding unit can input the information received by the AI ​​into the generation AI and have the generation AI analyze the circumstances of the accident.

[0088] The judgment unit allows the AI ​​to determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. The judgment unit can, for example, select the optimal response based on the scale of the accident and determine the need for emergency vehicles and traffic control. The judgment unit can also determine the need for emergency vehicles based on whether or not there are any injuries and their severity. The judgment unit can also determine the need for traffic control based on the location and time of the accident. This allows the AI ​​to accurately determine the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc., enabling appropriate responses. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, AI. For example, the judgment unit can input information received by the AI ​​into the generation AI and have the generation AI determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries.

[0089] The notification unit can transmit the information determined by the AI ​​to a medical institution, a police station, etc. The notification unit can, for example, quickly transmit information for dispatching emergency vehicles or traffic control, thereby enabling a prompt emergency response. The notification unit can also encrypt the notification content to ensure the security of communications. Furthermore, the notification unit can store the notification content in the cloud and make it accessible from a remote location. This allows the prompt transmission of the information determined by the AI, thereby enabling a prompt emergency response. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the information determined by the AI ​​to the generation AI and have the generation AI transmit the information.

[0090] The comprehension unit can record detailed circumstances of the accident and provide them to insurance companies and police. The comprehension unit can, for example, record detailed circumstances of the accident and provide them to insurance companies and police. The comprehension unit can also organize the received information in chronological order and understand the flow from the occurrence of the accident to its resolution. Furthermore, the comprehension unit can display the received information on a map to visually understand the location of the accident. In this way, by recording detailed circumstances of the accident and providing them to insurance companies and police, it is possible to reduce accident investigation costs. Some or all of the above-mentioned processing in the comprehension unit may be performed using AI, for example, or may be performed without using AI. For example, the comprehension unit can input information received by AI to the generation AI and cause the generation AI to record detailed circumstances of the accident.

[0091] The monitoring unit can estimate the user's emotions and adjust the viewpoint and zoom level of the surveillance camera based on the user's emotions. For example, if the user is nervous, the monitoring unit can widen the viewpoint of the surveillance camera to make it easier to grasp the overall situation. Furthermore, if the user is relaxed, the monitoring unit can adjust the zoom level of the surveillance camera to enable detailed viewing. Furthermore, if the user is excited, the monitoring unit can frequently switch the viewpoint of the surveillance camera to provide footage from different angles. This enables more appropriate monitoring by adjusting the viewpoint and zoom level of the surveillance camera according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input video data acquired by a camera into a generative AI and have the generative AI estimate the user's emotions and adjust the viewpoint and zoom level of the camera.

[0092] The monitoring unit can adjust the installation positions of the surveillance cameras and position them to cover the entire intersection. For example, the monitoring unit can install multiple cameras at different angles to cover the entire intersection. The monitoring unit can also adjust the installation positions of the cameras to enable omnidirectional monitoring in order to eliminate blind spots at the intersection. Furthermore, the monitoring unit can dynamically change the installation positions of the cameras depending on the traffic volume at the intersection to ensure an optimal monitoring range. In this way, the installation positions of the surveillance cameras can be optimized to cover the entire intersection. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the installation positions of the cameras to a generation AI and have the generation AI adjust the installation positions to the optimal positions.

[0093] The monitoring unit can record surveillance camera footage in high resolution, enabling detailed accident conditions to be grasped. For example, the monitoring unit uses a high-resolution camera to clearly record vehicle license plates and pedestrian faces. The monitoring unit can also save the high-resolution footage so that detailed accident conditions can be checked later. Furthermore, the monitoring unit can analyze the high-resolution footage in real time to identify the cause of the accident. Thus, by recording the footage in high resolution, detailed accident conditions can be grasped. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input high-resolution video data acquired by the camera to a generation AI and have the generation AI record and analyze the video.

[0094] The monitoring unit can be equipped with an infrared camera or waterproof functionality to enable clear capture of surveillance camera footage even at night or in bad weather. For example, the monitoring unit uses an infrared camera to capture clear footage even at night. The monitoring unit can also be equipped with waterproof functionality to prevent degradation of footage even in rainy weather. Furthermore, the monitoring unit can apply an anti-fog coating to the camera lens to provide high-quality footage even in bad weather. This allows clear footage to be captured even at night or in bad weather, enabling accurate understanding of the accident situation. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input video data acquired by the camera into a generation AI and have the generation AI analyze the footage at night or in bad weather.

[0095] The monitoring unit can estimate the user's emotions and adjust the recording time of the surveillance camera based on the user's emotions. For example, if the user is nervous, the monitoring unit can extend the recording time and keep a detailed record. Furthermore, if the user is relaxed, the monitoring unit can shorten the recording time and record only the necessary parts. Furthermore, if the user is excited, the monitoring unit can dynamically adjust the recording time to avoid missing important moments. This enables more appropriate monitoring by adjusting the recording time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input video data acquired by a camera into the generation AI and have the generation AI estimate the user's emotions and adjust the recording time.

[0096] The monitoring unit can analyze surveillance camera footage in real time and issue an alert if it detects abnormal activity. The monitoring unit detects abnormal activity, such as a vehicle collision or a pedestrian fall, in real time. Furthermore, if the monitoring unit detects abnormal activity, it can immediately issue an alert and notify relevant parties. Furthermore, if the monitoring unit detects abnormal activity, it can automatically save the video so that it can be reviewed later. This enables rapid response by detecting abnormal activity in real time and issuing an alert. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input video data acquired by a camera into a generation AI and have the generation AI detect abnormal activity and issue an alert.

[0097] The monitoring unit can store surveillance camera footage in the cloud and make it accessible from a remote location. For example, the monitoring unit automatically uploads surveillance camera footage to the cloud. The monitoring unit can also remotely access the footage stored in the cloud and view it in real time. Furthermore, the monitoring unit can make the footage stored in the cloud simultaneously accessible from multiple devices. This allows the footage stored in the cloud to be accessed remotely and viewed in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by a camera to a generation AI and have the generation AI store the data in the cloud and access it from a remote location.

[0098] The monitoring unit can optimize traffic flow by linking surveillance camera footage with other traffic management systems. For example, the monitoring unit can provide surveillance camera footage to a traffic management system in real time to optimize traffic flow. The monitoring unit can also link with a traffic management system to automatically regulate traffic when an accident occurs. Furthermore, the monitoring unit can dynamically adjust the timing of traffic signals based on surveillance camera footage to alleviate traffic congestion. This allows for optimization of traffic flow by linking with other traffic management systems. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by a camera into a generation AI and have the generation AI execute linking with the traffic management system.

[0099] The detection unit can estimate the user's emotions and adjust the sensitivity of the detection algorithm based on the user's emotions. For example, if the user is nervous, the detection unit can increase the sensitivity of the detection algorithm to detect even subtle abnormalities. Alternatively, if the user is relaxed, the detection unit can decrease the sensitivity of the detection algorithm to detect only important abnormalities. Furthermore, if the user is excited, the detection unit can dynamically adjust the sensitivity of the detection algorithm to avoid missing important abnormalities. This enables more accurate detection by adjusting the sensitivity of the detection algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input video data acquired by a camera into the generative AI and have the generative AI estimate the user's emotions and adjust the sensitivity of the detection algorithm.

[0100] The detection unit can use image recognition technology to identify vehicle license plates and pedestrian faces and acquire detailed information about the accident. For example, the detection unit can use image recognition technology to automatically identify vehicle license plates and acquire detailed information about the accident. The detection unit can also use image recognition technology to identify pedestrian faces and acquire detailed information about the accident. Furthermore, the detection unit can also use image recognition technology to identify the type and color of vehicles and acquire detailed information about the accident. In this way, by using image recognition technology, detailed information about the accident can be accurately acquired. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI identify license plates and faces.

[0101] The detection unit can use image recognition technology to analyze the speed and direction of travel of a vehicle and identify the cause of an accident. The detection unit can, for example, use image recognition technology to analyze the speed of a vehicle and identify the cause of an accident. The detection unit can also use image recognition technology to analyze the direction of travel of a vehicle and identify the cause of an accident. Furthermore, the detection unit can also use image recognition technology to analyze the behavior of a vehicle and identify the cause of an accident. As a result, the use of image recognition technology makes it possible to accurately identify the cause of an accident. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the speed and direction of travel.

[0102] The detection unit can use image recognition technology to automatically save video footage before and after an accident occurs and use it as evidence. The detection unit can, for example, use image recognition technology to automatically save video footage before and after an accident occurs. The detection unit can also use the saved video footage as evidence to identify the cause of the accident. Furthermore, the detection unit can provide the saved video footage to insurance companies or police to share detailed information about the accident. In this way, by saving video footage before and after an accident occurs, it can be used as evidence. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI save and analyze the video.

[0103] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the user's emotions. For example, if the user is nervous, the detection unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the detection unit can provide a display method including detailed information. Furthermore, if the user is excited, the detection unit can provide a visually stimulating display method. This enables more appropriate display by adjusting the display method of the detection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input video data acquired by a camera into the generation AI and cause the generation AI to estimate the user's emotions and adjust the display method.

[0104] The detection unit can use image recognition technology to analyze traffic signal status and road signs and identify the causes of accidents. The detection unit can, for example, use image recognition technology to analyze traffic signal status and identify the causes of accidents. The detection unit can also use image recognition technology to analyze road signs and identify the causes of accidents. Furthermore, the detection unit can also use image recognition technology to analyze traffic signal timing and identify the causes of accidents. In this way, the causes of accidents can be identified by analyzing traffic signal status and road signs. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to the generation AI and have the generation AI analyze traffic signals and road signs.

[0105] The detection unit can use image recognition technology to identify the type and color of the vehicle and acquire detailed information about the accident. The detection unit can, for example, use image recognition technology to identify the type of vehicle and acquire detailed information about the accident. The detection unit can also use image recognition technology to identify the color of the vehicle and acquire detailed information about the accident. The detection unit can also use image recognition technology to identify vehicle features and acquire detailed information about the accident. In this way, detailed information about the accident can be accurately acquired by identifying the type and color of the vehicle. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI identify the type and color of the vehicle.

[0106] The detection unit can use image recognition technology to analyze the location and time of accident occurrence and grasp accident trends. The detection unit can, for example, use image recognition technology to analyze the location of accident occurrence and grasp accident trends. The detection unit can also use image recognition technology to analyze the time of accident occurrence and grasp accident trends. Furthermore, the detection unit can also use image recognition technology to analyze accident occurrence patterns and grasp accident trends. In this way, accident trends can be grasped by analyzing the location and time of accident occurrence. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the location and time of accident occurrence.

[0107] The transmission unit can estimate the user's emotions and determine the priority of transmission data based on the user's emotions. For example, if the user is nervous, the transmission unit prioritizes the transmission of important data. Furthermore, if the user is relaxed, the transmission unit can also adjust the data transmission order to transmit data efficiently. Furthermore, if the user is excited, the transmission unit can also prioritize the transmission of highly urgent data. This enables more appropriate data transmission by determining the priority of transmission data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of transmission data.

[0108] The transmitting unit can encrypt the transmission data to ensure the security of communications. The transmitting unit can, for example, encrypt the transmission data using AES encryption technology to ensure the security of communications. The transmitting unit can also encrypt the transmission data using SSL / TLS protocol to ensure the security of communications. Furthermore, the transmitting unit can also encrypt the transmission data using RSA encryption technology to ensure the security of communications. In this way, by encrypting the transmission data, the security of communications can be ensured. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and have the generating AI perform the encryption.

[0109] The transmitting unit can improve the communication speed by using a compression technique for the transmission data. For example, the transmitting unit can compress the transmission data using ZIP compression technology to improve the communication speed. The transmitting unit can also compress the transmission data using GZIP compression technology to improve the communication speed. Furthermore, the transmitting unit can compress the transmission data using LZMA compression technology to improve the communication speed. In this way, the communication speed can be improved by compressing the transmission data. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generation AI and have the generation AI perform compression.

[0110] The transmitting unit can automatically create a backup of the transmission data to prevent data loss. The transmitting unit can, for example, automatically back up the transmission data to a cloud to prevent data loss. The transmitting unit can also automatically back up the transmission data to local storage to prevent data loss. Furthermore, the transmitting unit can automatically back up the transmission data to distributed storage to prevent data loss. In this way, data loss can be prevented by automatically creating a backup of the transmission data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and have the generating AI create a backup.

[0111] The transmission unit can estimate the user's emotions and adjust the format of the transmission data based on the user's emotions. For example, if the user is nervous, the transmission unit can transmit data in a simple data format to facilitate analysis. Furthermore, if the user is relaxed, the transmission unit can transmit data in a detailed data format to provide abundant information. Furthermore, if the user is excited, the transmission unit can transmit data in a visually easy-to-understand data format to enable a quick response. This enables more appropriate data transmission by adjusting the format of the transmission data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit can be performed using, for example, an AI, or without an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI adjust the format of the transmission data.

[0112] The transmitting unit can improve the reliability of communication by making the transmission data compatible with multiple communication protocols. The transmitting unit can improve the reliability of communication by making the transmission data compatible with both HTTP and HTTPS, for example. The transmitting unit can also improve the reliability of communication by making the transmission data compatible with both TCP and UDP. Furthermore, the transmitting unit can also improve the reliability of communication by making the transmission data compatible with both MQTT and CoAP. In this way, by making the transmission data compatible with multiple communication protocols, the reliability of communication can be improved. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and cause the generating AI to support multiple communication protocols.

[0113] The transmitting unit can share the transmission data with other traffic management systems to achieve centralized management of information. The transmitting unit, for example, can share the transmission data with other traffic management systems in real time to achieve centralized management of information. The transmitting unit can also link the transmission data with other traffic management systems to optimize traffic flow. Furthermore, the transmitting unit can share the transmission data with other traffic management systems to speed up accident response. In this way, sharing the transmission data with other traffic management systems enables centralized management of information. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the transmission data to a generating AI and have the generating AI share the data with other traffic management systems.

[0114] The transmission unit can monitor the transmission data in real time and issue an alert if an abnormality occurs. For example, the transmission unit can monitor the transmission data in real time and immediately issue an alert if an abnormality occurs. The transmission unit can also monitor the transmission data and automatically create a backup if an abnormality occurs. Furthermore, the transmission unit can monitor the transmission data and notify relevant parties if an abnormality occurs. In this way, by monitoring the transmission data in real time, it is possible to respond quickly if an abnormality occurs. Some or all of the above-mentioned processing in the transmission unit may be performed using AI, for example, or may be performed without using AI. For example, the transmission unit can input the transmission data to a generation AI and have the generation AI perform real-time monitoring and issue an alert.

[0115] The grasping unit can estimate the user's emotions and adjust the display method of the accident situation based on the user's emotions. For example, if the user is nervous, the grasping unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the grasping unit can provide a display method including detailed information. Furthermore, if the user is excited, the grasping unit can provide a visually stimulating display method. This enables a more appropriate display by adjusting the display method of the accident situation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the grasping unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the grasping unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the accident situation.

[0116] The comprehension unit can organize the received information in chronological order and grasp the flow from the occurrence of an accident to its resolution. The comprehension unit, for example, can organize the received information in chronological order and visually display the flow from the occurrence of an accident to its resolution. The comprehension unit can also organize the received information in chronological order and record the flow from the occurrence of an accident to its resolution in detail. Furthermore, the comprehension unit can also organize the received information in chronological order and analyze the flow from the occurrence of an accident to its resolution. In this way, by organizing the received information in chronological order, the flow from the occurrence of an accident to its resolution can be grasped. Some or all of the above-mentioned processing in the comprehension unit may be performed, for example, using AI, or may be performed without using AI. For example, the comprehension unit can input the received information to a generation AI and have the generation AI organize and display the information in chronological order.

[0117] The grasping unit displays the received information on a map, and is able to visually grasp the location of the accident. The grasping unit, for example, displays the received information on a map, and is able to visually grasp the location of the accident. The grasping unit can also display the received information on a map and record the location of the accident in detail. Furthermore, the grasping unit can also display the received information on a map and analyze the location of the accident. In this way, by displaying the received information on a map, it is possible to visually grasp the location of the accident. Some or all of the above-described processing in the grasping unit may be performed, for example, using AI, or may be performed without using AI. For example, the grasping unit may input the received information to a generation AI and cause the generation AI to display the information on a map.

[0118] The grasping unit can statistically analyze the received information to grasp trends and patterns of accidents. The grasping unit, for example, statistically analyzes the received information to grasp trends of accidents. The grasping unit can also statistically analyze the received information to grasp patterns of accidents. Furthermore, the grasping unit can statistically analyze the received information to identify the cause of the accident. In this way, trends and patterns of accidents can be grasped by statistically analyzing the received information. Some or all of the above-described processing in the grasping unit may be performed using AI, for example, or may be performed without using AI. For example, the grasping unit can input the received information to a generation AI and cause the generation AI to perform statistical analysis.

[0119] The grasping unit can estimate the user's emotions and automatically generate a report of the accident situation based on the user's emotions. For example, if the user is nervous, the grasping unit can automatically generate a simple, highly visible report. Furthermore, if the user is relaxed, the grasping unit can automatically generate a report including detailed information. Furthermore, if the user is excited, the grasping unit can automatically generate a visually stimulating report. This enables a more appropriate report by automatically generating a report based on the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the grasping unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the grasping unit can input the user's emotion data into the generation AI and cause the generation AI to automatically generate a report.

[0120] The grasping unit can coordinate the received information with other traffic management systems to minimize the impact of accidents. For example, the grasping unit can coordinate the received information with other traffic management systems to minimize the impact of accidents. The grasping unit can also share the received information with other traffic management systems to optimize traffic flow. Furthermore, the grasping unit can coordinate the received information with other traffic management systems to achieve a rapid accident response. In this way, by coordinating with other traffic management systems, the impact of accidents can be minimized. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input the received information into a generation AI and cause the generation AI to coordinate with other traffic management systems.

[0121] The grasping unit can store the received information in a cloud and make it accessible from a remote location. For example, the grasping unit can automatically store the received information in a cloud and make it accessible from a remote location. The grasping unit can also access the information stored in the cloud from a remote location and check it in real time. Furthermore, the grasping unit can make the information stored in the cloud accessible from multiple devices simultaneously. This allows the information stored in the cloud to be accessed from a remote location and checked in real time. Some or all of the above-mentioned processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input the received information to a generation AI and have the generation AI store the information in the cloud and access it from a remote location.

[0122] The grasping unit can analyze the received information in real time, enabling a rapid response. The grasping unit can, for example, analyze the received information in real time, enabling a rapid response. The grasping unit can also analyze the received information in real time and identify the cause of the accident. Furthermore, the grasping unit can analyze the received information in real time and determine the need for an emergency response. This enables a rapid response by analyzing the received information in real time. Some or all of the above-described processing in the grasping unit may be performed, for example, using AI, or may be performed without using AI. For example, the grasping unit can input the received information to a generation AI and cause the generation AI to perform real-time analysis.

[0123] The determination unit can estimate the user's emotions and prioritize responses based on the user's emotions. For example, if the user is nervous, the determination unit prioritizes responses with a high level of urgency. Furthermore, if the user is relaxed, the determination unit can prioritize responses that are efficient. Furthermore, if the user is excited, the determination unit can prioritize responses that are quick. This enables more appropriate responses by prioritizing responses based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of responses.

[0124] The judgment unit can select an appropriate response based on the scale of the accident and the number of vehicles involved. The judgment unit selects the optimal response based on, for example, the scale of the accident. The judgment unit can also select the optimal response based on the number of vehicles involved. The judgment unit can also select the optimal response based on the scale of the accident and the number of vehicles involved. This enables a quick and appropriate response by selecting the optimal response based on the scale of the accident and the number of vehicles involved. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on the scale of the accident and the number of vehicles involved into the generation AI and have the generation AI select a response.

[0125] The determination unit can determine whether to dispatch an emergency vehicle based on the presence or absence of injured persons and the severity of their injuries. The determination unit can, for example, determine whether to dispatch an emergency vehicle based on the presence or absence of injured persons. The determination unit can also determine whether to dispatch an emergency vehicle based on the severity of the injured persons. The determination unit can also determine whether to dispatch an emergency vehicle based on the presence or absence of injured persons and the severity of their injuries. This enables rapid rescue operations by determining whether to dispatch an emergency vehicle based on the presence or absence of injured persons and the severity of their injuries. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input data on the presence or absence of injured persons and the severity of their injuries to the generation AI, and have the generation AI determine whether to dispatch an emergency vehicle.

[0126] The determination unit can determine the need for traffic restrictions based on the location and time of the accident. The determination unit can determine the need for traffic restrictions based on, for example, the location of the accident. The determination unit can also determine the need for traffic restrictions based on the time of the accident. Furthermore, the determination unit can also determine the need for traffic restrictions based on the location and time of the accident. This makes it possible to reduce traffic congestion by determining the need for traffic restrictions based on the location and time of the accident. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input data on the location and time of the accident into the generation AI and have the generation AI determine the need for traffic restrictions.

[0127] The determination unit can estimate the user's emotions and adjust the level of detail of the response content based on the user's emotions. For example, if the user is nervous, the determination unit can provide detailed response content. Furthermore, if the user is relaxed, the determination unit can provide concise response content. Furthermore, if the user is excited, the determination unit can provide visually easy-to-understand response content. This enables a more appropriate response by adjusting the level of detail of the response content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the response content.

[0128] The judgment unit can select an appropriate medical institution or police station based on the circumstances of the accident. The judgment unit, for example, selects the most appropriate medical institution based on the circumstances of the accident. The judgment unit can also select the most appropriate police station based on the circumstances of the accident. Furthermore, the judgment unit can also select the most appropriate medical institution and police station based on the circumstances of the accident. This enables a quick and appropriate response by selecting the most appropriate medical institution or police station based on the circumstances of the accident. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on the circumstances of the accident into the generation AI and have the generation AI select a medical institution or police station.

[0129] The determination unit can cooperate with other traffic management systems to minimize the impact of an accident. For example, the determination unit cooperates with other traffic management systems in real time to minimize the impact of an accident. The determination unit can also share information with other traffic management systems to minimize the impact of an accident. Furthermore, the determination unit can cooperate with other traffic management systems to minimize the impact of an accident. In this way, the impact of an accident can be minimized by coordinating with other traffic management systems. Some or all of the above-described processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can input data on the impact of the accident into the generation AI and cause the generation AI to coordinate with other traffic management systems.

[0130] The judgment unit can automatically generate the report content to be sent to the insurance company based on the circumstances of the accident. The judgment unit automatically generates the report content to be sent to the insurance company based on the circumstances of the accident, for example. The judgment unit can also automatically generate a detailed report based on the circumstances of the accident. Furthermore, the judgment unit can automatically generate a concise report based on the circumstances of the accident. This enables the report content to be sent to the insurance company automatically based on the circumstances of the accident, thereby enabling a quick and accurate report. Some or all of the above-mentioned processing in the judgment unit may be performed using AI, for example, or may be performed without using AI. For example, the judgment unit can input data on the circumstances of the accident into a generation AI and cause the generation AI to automatically generate the report content.

[0131] The notification unit can estimate the user's emotions and prioritize notification content based on the user's emotions. For example, if the user is nervous, the notification unit can prioritize sending important notification content. Furthermore, if the user is relaxed, the notification unit can prioritize sending efficient notification content. Furthermore, if the user is excited, the notification unit can prioritize sending urgent notification content. This enables more appropriate notifications by prioritizing notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the notification content.

[0132] The notification unit can encrypt the notification content to ensure the security of communication. The notification unit can encrypt the notification content using, for example, AES encryption technology to ensure the security of communication. The notification unit can also encrypt the notification content using the SSL / TLS protocol to ensure the security of communication. Furthermore, the notification unit can also encrypt the notification content using RSA encryption technology to ensure the security of communication. In this way, by encrypting the notification content, the security of communication can be ensured. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI perform the encryption.

[0133] The notification unit can improve the reliability of communication by making the notification content compatible with multiple communication protocols. The notification unit can improve the reliability of communication by making the notification content compatible with both HTTP and HTTPS, for example. The notification unit can also improve the reliability of communication by making the notification content compatible with both TCP and UDP. Furthermore, the notification unit can also improve the reliability of communication by making the notification content compatible with both MQTT and CoAP. In this way, by making the notification content compatible with multiple communication protocols, the reliability of communication can be improved. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and cause the generation AI to support multiple communication protocols.

[0134] The notification unit can automatically back up the notification content to prevent data loss. The notification unit can, for example, automatically back up the notification content to a cloud to prevent data loss. The notification unit can also automatically back up the notification content to local storage to prevent data loss. Furthermore, the notification unit can automatically back up the notification content to distributed storage to prevent data loss. In this way, by automatically backing up the notification content, data loss can be prevented. Some or all of the above-described processing in the notification unit can be performed using AI, for example, or can be performed without using AI. For example, the notification unit can input the notification content to a generation AI and cause the generation AI to create a backup.

[0135] The notification unit can estimate the user's emotions and adjust the format of the notification content based on the user's emotions. For example, if the user is nervous, the notification unit can send a simple notification format to facilitate analysis. Furthermore, if the user is relaxed, the notification unit can send a detailed notification format to provide abundant information. Furthermore, if the user is excited, the notification unit can send a visually easy-to-understand notification format to enable a quick response. This allows for more appropriate notifications by adjusting the format of the notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the format of the notification content.

[0136] The notification unit can share the notification content with other traffic management systems to achieve centralized information management. The notification unit, for example, can share the notification content with other traffic management systems in real time to achieve centralized information management. The notification unit can also coordinate the notification content with other traffic management systems to optimize traffic flow. Furthermore, the notification unit can share the notification content with other traffic management systems to speed up accident response. This enables centralized information management by sharing the notification content with other traffic management systems. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI share the notification content with other traffic management systems.

[0137] The notification unit can monitor the notification content in real time and issue an alert if an abnormality occurs. For example, the notification unit can monitor the notification content in real time and immediately issue an alert if an abnormality occurs. The notification unit can also monitor the notification content and automatically create a backup if an abnormality occurs. Furthermore, the notification unit can monitor the notification content and notify relevant parties if an abnormality occurs. In this way, by monitoring the notification content in real time, it is possible to respond quickly if an abnormality occurs. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and cause the generation AI to perform real-time monitoring and issue an alert.

[0138] The notification unit can store the notification content in the cloud and make it accessible from a remote location. For example, the notification unit can automatically store the notification content in the cloud and make it accessible from a remote location. The notification unit can also access the notification content stored in the cloud from a remote location and check it in real time. Furthermore, the notification unit can also make the notification content stored in the cloud accessible from multiple devices simultaneously. This allows the notification content stored in the cloud to be accessed from a remote location and checked in real time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the notification content to a generation AI and have the generation AI store the notification content in the cloud and access it from a remote location. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, detection unit, transmission unit, understanding unit, determination unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit can monitor intersection video in real time using the camera 42 of the smart device 14. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects the occurrence of a traffic accident using image recognition technology. The transmission unit transmits information to an AI via a 5G base station via the communication I / F 44 of the smart device 14. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information to determine the accident situation. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc. The notification unit is realized by the control unit 46A of the smart device 14 and transmits information determined by the AI ​​to a medical institution, police station, etc. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, detection unit, transmission unit, understanding unit, determination unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit can monitor intersection images in real time using the camera 42 of the smart glasses 214. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects the occurrence of a traffic accident using image recognition technology. The transmission unit transmits information to an AI via a 5G base station via the communication I / F 44 of the smart glasses 214. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information to understand the accident situation. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc. The notification unit is realized by the control unit 46A of the smart glasses 214 and transmits information determined by the AI ​​to a medical institution, police station, etc. === Hard Collateral 1-3 === Each of the multiple elements, including the monitoring unit, detection unit, transmission unit, understanding unit, determination unit, and notification unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the monitoring unit can monitor intersection video in real time using the camera 42 of the headset-type terminal 314. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects the occurrence of a traffic accident using image recognition technology. The transmission unit transmits information to an AI via a 5G base station via the communication I / F 44 of the headset-type terminal 314. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information to determine the accident situation. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc. The notification unit is realized by the control unit 46A of the headset-type terminal 314 and transmits information determined by the AI ​​to a medical institution, police station, etc. === Hard Collateral 1-4 === Each of the multiple elements, including the monitoring unit, detection unit, transmission unit, understanding unit, judgment unit, and notification unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit can monitor intersection images in real time using the camera 42 of the robot 414. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects the occurrence of a traffic accident using image recognition technology. The transmission unit transmits information to an AI via a 5G base station via the communication I / F 44 of the robot 414. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information to understand the accident situation. The judgment unit is realized by the specific processing unit 290 of the data processing device 12 and determines the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc. The notification unit is realized by the control unit 46A of the robot 414 and transmits information determined by the AI ​​to a medical institution, police station, etc.

[0139] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0140] The monitoring unit can monitor intersection footage in real time using remote cameras installed at traffic signals. For example, the monitoring unit can install multiple cameras to cover the entire intersection and monitor footage in real time. The monitoring unit can also be equipped with infrared cameras and waterproofing to capture clear footage even at night or in bad weather. Furthermore, the monitoring unit can analyze traffic volume at the intersection and automatically adjust the camera's viewpoint during peak times to monitor the most important areas. This enables early detection of traffic accidents and rapid response.

[0141] The detection unit can detect the occurrence of a traffic accident using image recognition technology. For example, the detection unit can detect abnormal movements such as a vehicle collision or a pedestrian fall. The detection unit can also identify the vehicle's license plate or the pedestrian's face to obtain detailed information about the accident. Furthermore, the detection unit can analyze the vehicle's speed and direction of travel to identify the cause of the accident. In this way, the use of image recognition technology can accurately detect the occurrence of a traffic accident. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input video data acquired by a camera to a generation AI and have the generation AI analyze the video data.

[0142] The transmitting unit can transmit information to the AI ​​through a 5G base station. For example, the transmitting unit can quickly transmit detected information to the AI, allowing for rapid analysis by the AI. The transmitting unit can also encrypt the transmitted data to ensure communication security. Furthermore, the transmitting unit can improve communication speed by using compression technology for the transmitted data. This allows for rapid transmission of information through a 5G base station, allowing for rapid analysis by the AI. Some or all of the above-described processing in the transmitting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmitting unit can input data acquired from the detecting unit to the generating AI and have the generating AI transmit the data.

[0143] The understanding unit can analyze the information received by the AI ​​and understand the circumstances of the accident. For example, the understanding unit can determine the scale of the accident, the number of vehicles involved, whether there are any injuries, etc. The understanding unit can also record detailed circumstances of the accident and provide them to insurance companies and police. Furthermore, the understanding unit can organize the received information in chronological order and understand the flow from the occurrence of the accident to its resolution. In this way, the AI ​​can analyze the received information and accurately understand the circumstances of the accident. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input the information received by the AI ​​into the generation AI and have the generation AI analyze the circumstances of the accident.

[0144] The judgment unit allows the AI ​​to determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries. For example, the judgment unit can select the optimal response based on the scale of the accident and determine the need for emergency vehicles and traffic control. The judgment unit can also determine the need for emergency vehicles based on whether or not there are any injuries and their severity. Furthermore, the judgment unit can determine the need for traffic control based on the location and time of the accident. This allows the AI ​​to accurately determine the scale of the accident, the number of vehicles involved, whether or not there are any injuries, etc., enabling appropriate responses. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, AI. For example, the judgment unit can input information received by the AI ​​into the generation AI and have the generation AI determine the scale of the accident, the number of vehicles involved, and whether or not there are any injuries.

[0145] The notification unit can transmit the information determined by the AI ​​to a medical institution, police station, etc. For example, the notification unit can quickly transmit information for dispatching emergency vehicles or traffic control, allowing for a prompt emergency response. The notification unit can also encrypt the notification content to ensure the security of communications. Furthermore, the notification unit can store the notification content in the cloud and make it accessible from a remote location. This allows for a prompt transmission of the information determined by the AI, thereby enabling a prompt emergency response. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the information determined by the AI ​​to the generation AI and have the generation AI transmit the information.

[0146] The monitoring unit can estimate the user's emotions and adjust the viewpoint and zoom level of the surveillance camera based on the user's emotions. For example, if the user is nervous, the viewpoint of the surveillance camera can be set to a wide angle to make it easier to grasp the overall situation. Alternatively, if the user is relaxed, the zoom level of the surveillance camera can be adjusted to allow for detailed viewing. Furthermore, if the user is excited, the viewpoint of the surveillance camera can be frequently switched to provide footage from different angles. This allows for more appropriate monitoring by adjusting the viewpoint and zoom level of the surveillance camera according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input video data acquired by a camera into a generative AI and have the generative AI estimate the user's emotions and adjust the viewpoint and zoom level of the camera.

[0147] The detection unit can estimate the user's emotions and adjust the sensitivity of the detection algorithm based on the user's emotions. For example, if the user is nervous, the sensitivity of the detection algorithm can be increased to detect even subtle abnormalities. Alternatively, if the user is relaxed, the sensitivity of the detection algorithm can be decreased to detect only important abnormalities. Furthermore, if the user is excited, the sensitivity of the detection algorithm can be dynamically adjusted to avoid missing important abnormalities. This enables more accurate detection by adjusting the sensitivity of the detection algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input video data acquired by a camera into the generative AI and have the generative AI estimate the user's emotions and adjust the sensitivity of the detection algorithm.

[0148] The grasping unit can estimate the user's emotions and adjust the display method of the accident situation based on the user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is excited, a visually stimulating display method can be provided. This allows for a more appropriate display by adjusting the display method of the accident situation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the grasping unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the grasping unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the accident situation.

[0149] The transmission unit can estimate the user's emotions and determine the priority of data to be transmitted based on the user's emotions. For example, if the user is nervous, important data can be transmitted with priority. Furthermore, if the user is relaxed, the data transmission order can be adjusted for efficient transmission. Furthermore, if the user is excited, data with high urgency can be transmitted with priority. This enables more appropriate data transmission by determining the priority of data to be transmitted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the transmission unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data to be transmitted.

[0150] The processing flow of the second embodiment will be briefly explained below.

[0151] Step 1: The monitoring unit monitors the intersection in real time using remote cameras installed at traffic signals. For example, multiple cameras can be installed to cover the entire intersection, and they can be equipped with infrared cameras and waterproofing to capture clear images even at night or in bad weather. Step 2: The detection unit detects the occurrence of a traffic accident based on the video captured by the monitoring unit. For example, image recognition technology can be used to detect abnormal movements such as vehicle collisions or pedestrian falls, and detailed information about the accident can be obtained by identifying vehicle license plates and pedestrian faces. Step 3: The transmitter transmits the information detected by the detector to the AI ​​via a 5G base station. For example, the transmitter can quickly transmit the detected information to the AI, allowing it to quickly analyze the information. Step 4: The understanding unit analyzes the information sent by the transmission unit and understands the accident situation, such as the scale of the accident, the number of vehicles involved, and whether there were any injuries. Step 5: The decision unit determines the response based on the situation grasped by the understanding unit. For example, it can select the optimal response based on the scale of the accident and determine the need for emergency vehicles or traffic restrictions. Step 6: The notification unit transmits the information determined by the determination unit to a medical institution or a police station. For example, information for dispatching emergency vehicles or traffic control can be transmitted quickly, enabling a prompt emergency response.

[0152] 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.

[0153] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0157] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] 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.

[0164] 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.

[0165] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0173] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0174] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0175] 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.

[0176] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0177] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0178] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0179] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0180] 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.

[0181] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0188] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0189] 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.

[0190] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0191] 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.

[0192] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0193] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0194] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0195] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0196] 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.

[0197] 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.

[0198] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0199] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0200] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0201] 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.

[0202] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0203] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0204] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0205] 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.

[0206] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0207] 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.

[0208] 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).

[0209] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0210] 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."

[0211] 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.

[0212] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0217] The hardware resource that executes the specific process 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 process may be a single processor.

[0218] 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.

[0219] 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.

[0220] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0221] 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, in order to avoid confusion and to 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.

[0222] 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.

[0223] [Explanation of symbols]

[0224] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring unit monitors the intersection in real time using remote cameras installed at traffic signals, and a detection unit that detects the occurrence of a traffic accident based on the video monitored by the monitoring unit; A transmission unit that transmits information detected by the detection unit to AI through a 5G base station; a grasping unit that analyzes the information transmitted by the transmitting unit and grasps the situation of the accident; a determination unit that determines a response based on the situation grasped by the grasping unit; a notification unit that transmits the information determined by the determination unit to a medical institution or a police station; Equipped with A system characterized by:

2. The monitoring unit Remote cameras installed at traffic lights monitor intersections in real time 2. The system of claim 1.

3. The detection unit Detecting traffic accidents using image recognition technology 2. The system of claim 1.

4. The transmission unit Sending information to AI via 5G base stations 2. The system of claim 1.

5. The grasping unit AI analyzes the received information and grasps the circumstances of the accident 2. The system of claim 1.

6. The determination unit AI will determine the scale of the accident, the number of vehicles involved, and whether there are any injuries.

2. The system of claim 1.

7. The notification unit The information determined by AI will be sent to medical institutions, police stations, etc.

2. The system of claim 1.

8. The grasping unit Record the details of the accident and provide them to insurance companies and the police 2. The system of claim 1.

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