System

Abstract

An object of a system according to an embodiment is to quickly detect a traffic accident and perform notification and handling.SOLUTION: A system according to an embodiment includes a millimeter-wave 5G station, a remote camera, a reporting unit, and a handling unit. The millimeter wave 5G base stations are installed in traffic lights. The remote camera automatically detects traffic accidents based on a low-delay and high-speed communication network provided by millimeter wave 5G base stations. The reporting unit reports the traffic accident detected by the remote camera. The countermeasure unit performs a countermeasure based on the information reported by the reporting unit.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 detect and report traffic accidents quickly, which can lead to delayed responses.

[0005] The system according to the embodiment aims to quickly detect traffic accidents and to report and respond to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a millimeter wave 5G base station, a remote camera, a reporting unit, and a response unit. The millimeter wave 5G base station is installed at a traffic light. The remote camera automatically detects traffic accidents based on the low-latency, high-speed communication network provided by the millimeter wave 5G base station. The reporting unit reports the traffic accident detected by the remote camera. The response unit responds based on the information reported by the reporting unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly detect traffic accidents, report them, and take action. [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 system according to an embodiment of the present invention uses millimeter-wave 5G base stations and remote cameras installed at traffic lights to automatically detect and rapidly respond to traffic accidents. This system provides a communication environment necessary for Level 4 and 5 autonomous driving by installing millimeter-wave 5G base stations at traffic lights and providing a low-latency, high-speed communication network. Remote cameras are also used to automatically detect traffic accidents, which are then promptly reported by the reporting department and then investigated by the response department. Furthermore, by promoting the use of LED lights at traffic lights, installation, maintenance, and electricity costs are reduced. For example, installing millimeter-wave 5G base stations at traffic lights creates a low-latency, high-speed communication network, improving the safety and efficiency of autonomous vehicles. Next, remote cameras are installed at traffic lights and automatically detect traffic accidents using image recognition technology. When an accident occurs, the reporting department promptly reports the accident, and the response department investigates the scene. This is expected to significantly reduce the costs of accident handling and investigation. Furthermore, by promoting the use of LED lights at traffic lights, installation, maintenance, and electricity costs can be reduced. This enables the system to automatically detect and rapidly respond to traffic accidents. For example, installing millimeter wave 5G base stations at traffic lights will provide a low-latency, high-speed communication network, improving the safety and efficiency of autonomous vehicles. Furthermore, by automatically detecting traffic accidents using remote cameras, promptly reporting the incident to the reporting department, and having the response department conduct on-site inspections, it is expected that the costs of accident handling and investigation will be significantly reduced. Furthermore, by promoting the use of LED lights on traffic lights, it will be possible to reduce installation and maintenance costs, as well as electricity costs.

[0029] A traffic management system according to an embodiment includes a millimeter-wave 5G base station, a remote camera, a reporting unit, and a response unit. The millimeter-wave 5G base station is installed at traffic lights and provides a low-latency, high-speed communication network. For example, the millimeter-wave 5G base station can provide a latency of less than 1 millisecond and a communication speed of more than 1 Gbps. The millimeter-wave 5G base station can also provide the communication environment required for Level 4 and 5 autonomous driving. The remote camera automatically detects traffic accidents using image recognition technology. For example, the remote camera can detect traffic accidents with high accuracy using an object detection algorithm based on deep learning. The remote camera can also detect traffic accidents in real time and transmit information to the reporting unit. The reporting unit can promptly report a traffic accident when it is detected. For example, the reporting unit can report a traffic accident within one second of detecting it. The reporting unit can also automatically select the report recipient and content and provide appropriate information. The response unit investigates the scene based on the reported information. For example, the response unit can take photographs of the scene, collect evidence, and analyze the situation. Furthermore, the response unit can take appropriate action based on the results of the on-site investigation. As a result, the traffic management system according to the embodiment can automatically detect traffic accidents and respond quickly to them. For example, when a traffic accident occurs, the remote camera detects the accident, the reporting unit quickly reports it, and the response unit investigates the scene, thereby understanding the details of the accident.

[0030] The remote camera can automatically detect traffic accidents using image recognition technology. Image recognition technology includes, for example, an object detection algorithm using deep learning. For example, the remote camera can detect traffic accidents with high accuracy using deep learning. Furthermore, the remote camera can detect the occurrence of traffic accidents in real time using an object detection algorithm. Furthermore, the remote camera can quickly detect the occurrence of traffic accidents using image recognition technology. For example, when the remote camera detects the occurrence of a traffic accident, it can send information to a reporting unit. As a result, the use of image recognition technology improves the accuracy of traffic accident detection.

[0031] The reporting unit can quickly report a traffic accident when it detects it. For example, the reporting unit can report a traffic accident within one second of detecting it. The reporting unit can also automatically select the reporting destination and report content, and provide appropriate information. For example, the reporting unit can automatically report to the emergency response organization closest to the location of the traffic accident. The reporting unit can also automatically generate report content that includes detailed information about the traffic accident (location, time, scale, etc.). This allows for a rapid response by quickly reporting a traffic accident when it is detected.

[0032] The response department can investigate the scene based on the reported information. The response department can, for example, take photographs of the scene, collect evidence, and analyze the situation. The response department can also take appropriate action based on the results of the scene investigation. For example, the response department can analyze the situation of the traffic accident in real time and generate detailed response content. The response department can also automatically generate response content including detailed information about the traffic accident (location, time, scale, etc.). This makes it possible to grasp the detailed situation of the accident by investigating the scene based on the reported information.

[0033] By converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills. Converting traffic lights to LED includes, for example, energy-saving design and optimization of lighting times. For example, by converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills. In addition, by converting traffic lights to LED, it is possible to minimize power consumption and improve energy efficiency. Furthermore, by converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills. As a result, by converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills.

[0034] Millimeter wave 5G base stations can provide a communications environment suitable for Level 4 and 5 autonomous driving. Level 4 and 5 autonomous driving includes, for example, fully autonomous driving and automation of all driving operations. For example, millimeter wave 5G base stations can provide latency of less than 1 millisecond and communications speeds of more than 1 Gbps. Furthermore, millimeter wave 5G base stations can provide the communications environment necessary for Level 4 and 5 autonomous driving. By providing the communications environment necessary for Level 4 and 5 autonomous driving, the safety and efficiency of autonomous vehicles will be improved.

[0035] The installation location of mmWave 5G base stations can be optimized based on traffic volume and accident rates. For example, mmWave 5G base stations can be installed at intersections with heavy traffic to meet communication demands. Also, mmWave 5G base stations can be installed in locations with high accident rates to enable rapid accident response. Furthermore, traffic volume and accident rate data can be analyzed to select the optimal installation location. By optimizing installation locations based on traffic volume and accident rates, communication demands can be met and accidents can be quickly responded to.

[0036] A mmWave 5G base station can adjust its communication range appropriately depending on surrounding buildings and terrain. For example, a mmWave 5G base station can dynamically adjust its communication range using criteria for expanding or contracting the communication range and the timing of adjustment. For example, in areas with many high-rise buildings, the communication range can be narrowed to prevent signal interference. On flat terrain, the communication range can be widened to expand the coverage area. Furthermore, the communication range can be adjusted in real time in response to changes in surrounding buildings and terrain. This allows for dynamic adjustment of the communication range depending on surrounding buildings and terrain, preventing communication interference and optimizing the coverage area.

[0037] A millimeter wave 5G base station can optimize power consumption according to the operating status of a traffic signal. For example, a millimeter wave 5G base station can optimize power consumption using a power consumption reduction rate or a specific reduction method. For example, it can reduce power consumption when the traffic signal is red. It can also increase power consumption when the traffic signal is green to meet communication demands. It can also monitor the operating status of traffic signals in real time and optimize power consumption. This improves energy efficiency by optimizing power consumption according to the operating status of traffic signals.

[0038] mmWave 5G base stations can select installation locations to avoid interference with surrounding Wi-Fi networks. For example, mmWave 5G base stations can analyze the signal strength of surrounding Wi-Fi networks and select locations with minimal interference. For example, they can monitor Wi-Fi network usage in real time and select the optimal installation location. They can also dynamically adjust the installation location of mmWave 5G base stations to minimize interference with Wi-Fi networks. This avoids interference with surrounding Wi-Fi networks and improves communication quality.

[0039] A millimeter wave 5G base station can expand or contract its communication range appropriately according to the surrounding traffic conditions. For example, a millimeter wave 5G base station can dynamically adjust its communication range using an adjustment method according to changes in traffic conditions. For example, it can expand the communication range during times of heavy traffic to meet communication demand. It can also reduce the communication range during times of light traffic to reduce power consumption. Furthermore, it can monitor surrounding traffic conditions in real time and dynamically adjust the communication range. This allows it to dynamically adjust the communication range according to the surrounding traffic conditions to meet communication demand and reduce power consumption.

[0040] Millimeter-wave 5G base stations can minimize power consumption by using renewable energy sources such as solar power and wind power. Millimeter-wave 5G base stations can minimize power consumption by using renewable energy sources such as solar power and wind power. For example, solar power can be used to minimize power consumption during the day. Wind power can also be used to minimize power consumption on windy days. Furthermore, the usage status of renewable energy can be monitored in real time to minimize power consumption. This makes it possible to minimize power consumption and reduce environmental impact by using renewable energy.

[0041] The remote camera can change the resolution as appropriate depending on the occurrence of a traffic accident. The remote camera can dynamically change the resolution, for example, using a resolution range or the timing of the change. For example, if a traffic accident occurs, the resolution can be increased to record detailed video. If no traffic accident occurs, the resolution can be decreased to reduce the amount of data. Furthermore, the occurrence of traffic accidents can be monitored in real time and the resolution can be dynamically changed. This makes it possible to record detailed video and optimize the amount of data by dynamically changing the resolution depending on the occurrence of a traffic accident.

[0042] The installation location of a remote camera can be optimized based on the frequency of traffic accidents. The installation location of a remote camera can be optimized using, for example, a data analysis method of accident frequency and installation location selection criteria. For example, remote cameras can be installed at intersections where traffic accidents occur frequently to enhance accident detection. Also, installation of remote cameras can be avoided in locations where traffic accidents occur less frequently. Furthermore, data on the frequency of traffic accidents can be analyzed to select the optimal installation location. In this way, optimizing installation locations based on the frequency of traffic accidents improves the accuracy of accident detection.

[0043] Remote cameras can instantly upload data to the cloud for analysis. Remote cameras can upload data to the cloud in real time, for example, using the timing of upload and the type of data. For example, video captured by a remote camera can be uploaded to the cloud in real time, and analyzed by a generating AI. In addition, traffic accidents can be detected quickly based on the data analyzed on the cloud. Furthermore, data uploaded in real time can be analyzed to respond to changes in traffic conditions. This allows data to be uploaded to the cloud in real time and analyzed, enabling quick accident detection and response.

[0044] The remote camera can change the resolution appropriately depending on the ambient lighting conditions. The remote camera can dynamically change the resolution, for example, using a resolution range or the timing of the change. For example, when the lighting is low, the resolution can be increased to record detailed images. When the lighting is bright, the resolution can be decreased to reduce the amount of data. Furthermore, the ambient lighting conditions can be monitored in real time and the resolution can be dynamically changed. This allows for detailed video recording and optimization of the amount of data by dynamically changing the resolution depending on the ambient lighting conditions.

[0045] The installation location of the remote camera can be selected so as to avoid surrounding buildings and obstacles. The installation location of the remote camera can be optimized using, for example, building and obstacle avoidance criteria and installation location selection criteria. For example, the remote camera can be installed in a high position to avoid surrounding buildings and obstacles. Also, a location that is not affected by buildings and obstacles can be selected and the remote camera can be installed. Furthermore, the arrangement of surrounding buildings and obstacles can be analyzed to select the optimal installation location. This allows the camera's field of view to be optimized by avoiding surrounding buildings and obstacles, improving the quality of the video.

[0046] Remote cameras can analyze data using AI to predict traffic accidents. Remote cameras can analyze data using, for example, machine learning algorithms or data analysis methods. For example, AI can analyze footage captured by remote cameras to predict the occurrence of traffic accidents. Furthermore, based on the data analyzed by AI, it is possible to identify locations with a high risk of traffic accidents. Furthermore, AI can analyze data from remote cameras in real time to predict traffic accidents. As a result, using AI to analyze data improves the accuracy of traffic accident predictions.

[0047] The reporting unit can optimize the reporting method according to the scale of the traffic accident. The reporting unit can optimize the reporting method, for example, by using the type of reporting method and optimization criteria. For example, when a serious traffic accident occurs, a report can be made quickly by using a combination of voice and text reporting. Also, when a minor traffic accident occurs, a report can be made only by text reporting. Furthermore, the scale of the traffic accident can be analyzed in real time and the optimal reporting method can be selected. As a result, appropriate reporting can be made by optimizing the reporting method according to the scale of the traffic accident.

[0048] The reporting unit can automatically select a reporting destination based on the location of the accident. The reporting unit can automatically select a reporting destination using, for example, a list of reporting destinations or selection criteria. For example, the reporting unit can automatically report to the emergency response organization closest to the location of the accident. The reporting unit can also select an appropriate reporting destination based on the location of the accident. Furthermore, the reporting unit can analyze data on the location of the accident and automatically select the optimal reporting destination. This allows for a rapid response by automatically selecting a reporting destination based on the location of the accident.

[0049] The reporting unit can appropriately generate report content including detailed information about the accident. The reporting unit can dynamically generate report content using, for example, the type of report content and the timing of generation. For example, the reporting unit can analyze the accident occurrence situation in real time and generate detailed report content. In addition, the reporting unit can automatically generate report content including detailed information about the accident (location, time, scale, etc.). Furthermore, the reporting unit can dynamically generate report content according to the accident situation and provide appropriate information. As a result, by dynamically generating report content including detailed information about the accident, appropriate information can be provided.

[0050] The reporting unit can report using multiple methods, such as voice reporting and text reporting. The reporting unit can report using multiple methods, for example, using voice reporting, text reporting, and the timing of reporting. For example, when a serious traffic accident occurs, a report can be made quickly using both voice reporting and text reporting. Also, when a minor traffic accident occurs, a report can be made using only text reporting. Furthermore, the scale of the traffic accident can be analyzed in real time and the optimal reporting method can be selected. As a result, using multiple reporting methods enables quick and reliable reporting.

[0051] The reporting unit can automatically notify surrounding emergency response organizations. The reporting unit can automatically notify surrounding emergency response organizations using, for example, a list of notification destinations and notification timing. For example, the reporting unit can automatically notify the emergency response organization closest to the location where the accident occurred. In addition, the reporting unit can select an appropriate reporting destination based on the location of the accident. Furthermore, the reporting unit can analyze data on the location of the accident and automatically select the optimal reporting destination. This allows for a rapid response by automatically notifying surrounding emergency response organizations.

[0052] The reporting unit can appropriately generate report content including video data of the accident. The reporting unit can dynamically generate report content using, for example, the type of video data and the timing of generation. For example, the reporting unit can analyze the accident occurrence situation in real time and generate report content including video data. In addition, the reporting unit can automatically generate report content including detailed information about the accident (location, time, scale, etc.). Furthermore, the reporting unit can dynamically generate report content including video data according to the accident situation and provide appropriate information. As a result, by generating report content including video data of the accident, detailed information can be provided.

[0053] The response unit can optimize the response method according to the scale of the traffic accident. The response unit can optimize the response method using, for example, the type of response method and optimization criteria. For example, when a serious traffic accident occurs, on-site investigation and first aid can be carried out quickly. On the other hand, when a minor traffic accident occurs, only on-site investigation can be carried out. Furthermore, the scale of the traffic accident can be analyzed in real time and the optimal response method can be selected. As a result, an appropriate response can be taken by optimizing the response method according to the scale of the traffic accident.

[0054] The response unit can automatically select a response destination based on the location of the accident. The response unit can automatically select a response destination using, for example, a list of response destinations or selection criteria. For example, the response unit can automatically notify the emergency response organization closest to the location of the accident. The response unit can also select an appropriate response destination based on the location of the accident. Furthermore, the response unit can analyze data on the location of the accident and automatically select the optimal response destination. This enables a rapid response by automatically selecting a response destination based on the location of the accident.

[0055] The response unit can appropriately generate response content including detailed information about the accident. The response unit can dynamically generate response content using, for example, the type of response content and the timing of generation. For example, the response unit can analyze the accident occurrence situation in real time and generate detailed response content. In addition, the response unit can automatically generate response content including detailed information about the accident (location, time, scale, etc.). Furthermore, the response content can be dynamically generated according to the accident situation and appropriate information can be provided. As a result, by dynamically generating response content including detailed information about the accident, appropriate information can be provided.

[0056] The response unit can respond in multiple ways, such as by conducting on-site investigation and first aid. The response unit can respond in multiple ways, for example, by using on-site investigation, first aid, and the timing of the response. For example, when a serious traffic accident occurs, on-site investigation and first aid can be carried out quickly. Also, when a minor traffic accident occurs, only on-site investigation can be carried out. Furthermore, the scale of the traffic accident can be analyzed in real time, and the optimal response method can be selected. This makes it possible to respond quickly and appropriately by using multiple response methods.

[0057] The response unit can automatically notify surrounding emergency response organizations. The response unit can automatically notify surrounding emergency response organizations using, for example, a list of notification destinations and notification timing. For example, the response unit can automatically notify the emergency response organization closest to the location where the accident occurred. Also, the response unit can select an appropriate response destination based on the location of the accident. Furthermore, the response unit can analyze data on the location of the accident and automatically select the most appropriate response destination. This allows for a rapid response by automatically notifying surrounding emergency response organizations.

[0058] The response unit can appropriately generate response content including video data of the accident. The response unit can dynamically generate response content using, for example, the type of video data and the timing of generation. For example, the response unit can analyze the accident occurrence situation in real time and generate response content including video data. In addition, the response unit can automatically generate response content including detailed information about the accident (location, time, scale, etc.). Furthermore, response content including video data can be dynamically generated according to the accident situation and appropriate information can be provided. As a result, by generating response content including video data of the accident, detailed information can be provided.

[0059] The conversion of traffic lights to LED can be optimized based on traffic volume and accident rates. The conversion of traffic lights to LED can be optimized, for example, using data analysis methods on traffic volume and accident rates. For example, LED conversion can be prioritized at intersections with heavy traffic volume. Also, LED conversion can be prioritized at locations with high accident rates. Furthermore, data on traffic volume and accident rates can be analyzed to select the optimal locations for LED conversion. This allows for efficient installation by optimizing the conversion to LED based on traffic volume and accident rates.

[0060] The use of LED traffic lights can be adjusted appropriately according to the ambient lighting conditions. For example, the brightness of the LEDs can be dynamically adjusted using an adjustment method according to changes in lighting conditions. For example, when the lighting is dim, the LED brightness can be set high. When the lighting is bright, the LED brightness can be set low. Furthermore, the ambient lighting conditions can be monitored in real time and the LED brightness can be dynamically adjusted. This improves visibility by dynamically adjusting the LED brightness according to the ambient lighting conditions.

[0061] The conversion of traffic lights to LEDs can be designed to minimize power consumption. For example, the conversion of traffic lights to LEDs can minimize power consumption using a power consumption reduction rate or specific reduction methods. For example, energy-saving designs can be adopted to minimize the power consumption of LEDs. Furthermore, the lighting time of LEDs can be optimized to reduce power consumption. Furthermore, power consumption can be monitored in real time and adjustments can be made to minimize it. This minimizes power consumption, improving energy efficiency.

[0062] The conversion of traffic lights to LED can be done using renewable energy such as solar power and wind power. The conversion of traffic lights to LED can minimize power consumption by using renewable energy such as solar power and wind power. For example, solar power can be used to minimize power consumption during the day. Also, wind power can be used to minimize power consumption on windy days. Furthermore, the usage status of renewable energy can be monitored in real time to minimize power consumption. In this way, by using renewable energy, power consumption can be minimized and the environmental burden can be reduced.

[0063] LED traffic lights can be designed to avoid surrounding buildings and obstacles. LED traffic lights can be designed using building and obstacle avoidance criteria and design details, for example. For example, LED traffic lights can be installed in high positions to avoid surrounding buildings and obstacles. LED traffic lights can also be installed in locations that are not affected by buildings and obstacles. Furthermore, the layout of surrounding buildings and obstacles can be analyzed to select the optimal installation location. This improves visibility and installation efficiency by avoiding surrounding buildings and obstacles.

[0064] The use of LED traffic lights can be adjusted appropriately according to the traffic signal's operating status. For example, the brightness of the LEDs can be dynamically adjusted using an adjustment method according to the traffic signal's operating status. For example, the brightness of the LEDs can be reduced when the traffic signal is red. Also, the brightness of the LEDs can be increased when the traffic signal is green, improving visibility. Furthermore, the operating status of the traffic signal can be monitored in real time and the brightness of the LEDs can be dynamically adjusted. This improves visibility by dynamically adjusting the brightness of the LEDs according to the traffic signal's operating status.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] Millimeter-wave 5G base stations can analyze ambient noise and adjust communication priorities according to noise levels. For example, if the ambient noise level is high, communication priority can be set high to ensure important information is transmitted. If the noise level is low, communication priority can be returned to normal. Furthermore, changes in noise levels can be monitored in real time and communication priorities can be dynamically adjusted. This allows important information to be transmitted reliably by adjusting communication priorities according to the ambient noise level.

[0067] The reporting unit can acquire surrounding weather information when a traffic accident occurs and reflect it in the report content. For example, slippery road conditions can be included in the report content when it is raining. In addition, information about poor visibility can be added when there is fog. Furthermore, when strong winds are blowing, report content can be generated that takes the effects of wind into account. As a result, by reflecting surrounding weather information in the report content when a traffic accident occurs, more accurate information can be provided.

[0068] The conversion of traffic lights to LEDs makes it possible to dynamically change lighting patterns according to the volume of surrounding traffic. For example, the lights can be turned on for shorter periods during times of heavy traffic, allowing for smoother traffic flow. The lights can also be turned on for longer periods during times of light traffic, reducing energy consumption. Furthermore, changes in traffic volume can be monitored in real time and lighting patterns can be dynamically changed. This makes it possible to optimize traffic flow and improve energy efficiency by dynamically changing lighting patterns according to the volume of surrounding traffic.

[0069] The remote camera can obtain ambient temperature information and optimize its operation. For example, if the ambient temperature is high, the camera's cooling function can be strengthened to stabilize operation. Also, if the ambient temperature is low, the camera's operating speed can be adjusted and it can switch to energy-saving mode. Furthermore, temperature changes can be monitored in real time and camera operation can be dynamically optimized. This allows for stable video recording by optimizing camera operation based on ambient temperature information.

[0070] The response unit can obtain the status of surrounding traffic signals when a traffic accident occurs and optimize the response method. For example, if the traffic signal is red, the response unit adjusts the response method taking into account the time it takes to arrive at the scene. Also, if the traffic signal is green, a quick response can be made. Furthermore, the status of traffic signals can be monitored in real time and the response method can be dynamically optimized. This makes it possible to respond to a traffic accident taking into account the status of surrounding traffic signals.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: Millimeter-wave 5G base stations will be installed at traffic lights to provide a low-latency, high-speed communication network. For example, millimeter-wave 5G base stations can provide latency of less than 1 millisecond and communication speeds of more than 1 Gbps. In addition, millimeter-wave 5G base stations can provide the communication environment required for Level 4 and 5 autonomous driving. Step 2: The remote camera automatically detects traffic accidents using image recognition technology. For example, the remote camera can use deep learning-based object detection algorithms to detect traffic accidents with high accuracy. The remote camera can also detect traffic accidents in real time and send information to the reporting center. Step 3: The reporting unit promptly reports a traffic accident when it detects it. For example, the reporting unit can report a traffic accident within one second of detecting it. The reporting unit can also automatically select the contact person and content of the report, and provide appropriate information. Step 4: The response department conducts an on-site investigation based on the reported information. For example, the response department can take photographs of the scene, collect evidence, and analyze the situation. The response department can also take appropriate action based on the results of the on-site investigation.

[0073] (Example 2) A system according to an embodiment of the present invention uses millimeter-wave 5G base stations and remote cameras installed at traffic lights to automatically detect and rapidly respond to traffic accidents. This system provides a communication environment necessary for Level 4 and 5 autonomous driving by installing millimeter-wave 5G base stations at traffic lights and providing a low-latency, high-speed communication network. Remote cameras are also used to automatically detect traffic accidents, which are then promptly reported by the reporting department and then investigated by the response department. Furthermore, by promoting the use of LED lights at traffic lights, installation, maintenance, and electricity costs are reduced. For example, installing millimeter-wave 5G base stations at traffic lights creates a low-latency, high-speed communication network, improving the safety and efficiency of autonomous vehicles. Next, remote cameras are installed at traffic lights and automatically detect traffic accidents using image recognition technology. When an accident occurs, the reporting department promptly reports the accident, and the response department investigates the scene. This is expected to significantly reduce the costs of accident handling and investigation. Furthermore, by promoting the use of LED lights at traffic lights, installation, maintenance, and electricity costs can be reduced. This enables the system to automatically detect and rapidly respond to traffic accidents. For example, installing millimeter wave 5G base stations at traffic lights will provide a low-latency, high-speed communication network, improving the safety and efficiency of autonomous vehicles. Furthermore, by automatically detecting traffic accidents using remote cameras, promptly reporting the incident to the reporting department, and having the response department conduct on-site inspections, it is expected that the costs of accident handling and investigation will be significantly reduced. Furthermore, by promoting the use of LED lights on traffic lights, it will be possible to reduce installation and maintenance costs, as well as electricity costs.

[0074] A traffic management system according to an embodiment includes a millimeter-wave 5G base station, a remote camera, a reporting unit, and a response unit. The millimeter-wave 5G base station is installed at traffic lights and provides a low-latency, high-speed communication network. For example, the millimeter-wave 5G base station can provide a latency of less than 1 millisecond and a communication speed of more than 1 Gbps. The millimeter-wave 5G base station can also provide the communication environment required for Level 4 and 5 autonomous driving. The remote camera automatically detects traffic accidents using image recognition technology. For example, the remote camera can detect traffic accidents with high accuracy using an object detection algorithm based on deep learning. The remote camera can also detect traffic accidents in real time and transmit information to the reporting unit. The reporting unit can promptly report a traffic accident when it is detected. For example, the reporting unit can report a traffic accident within one second of detecting it. The reporting unit can also automatically select the report recipient and content and provide appropriate information. The response unit investigates the scene based on the reported information. For example, the response unit can take photographs of the scene, collect evidence, and analyze the situation. Furthermore, the response unit can take appropriate action based on the results of the on-site investigation. As a result, the traffic management system according to the embodiment can automatically detect traffic accidents and respond quickly to them. For example, when a traffic accident occurs, the remote camera detects the accident, the reporting unit quickly reports it, and the response unit investigates the scene, thereby understanding the details of the accident.

[0075] The remote camera can automatically detect traffic accidents using image recognition technology. Image recognition technology includes, for example, an object detection algorithm using deep learning. For example, the remote camera can detect traffic accidents with high accuracy using deep learning. Furthermore, the remote camera can detect the occurrence of traffic accidents in real time using an object detection algorithm. Furthermore, the remote camera can quickly detect the occurrence of traffic accidents using image recognition technology. For example, when the remote camera detects the occurrence of a traffic accident, it can send information to a reporting unit. As a result, the use of image recognition technology improves the accuracy of traffic accident detection.

[0076] The reporting unit can quickly report a traffic accident when it detects it. For example, the reporting unit can report a traffic accident within one second of detecting it. The reporting unit can also automatically select the reporting destination and report content, and provide appropriate information. For example, the reporting unit can automatically report to the emergency response organization closest to the location of the traffic accident. The reporting unit can also automatically generate report content that includes detailed information about the traffic accident (location, time, scale, etc.). This allows for a rapid response by quickly reporting a traffic accident when it is detected.

[0077] The response department can investigate the scene based on the reported information. The response department can, for example, take photographs of the scene, collect evidence, and analyze the situation. The response department can also take appropriate action based on the results of the scene investigation. For example, the response department can analyze the situation of the traffic accident in real time and generate detailed response content. The response department can also automatically generate response content including detailed information about the traffic accident (location, time, scale, etc.). This makes it possible to grasp the detailed situation of the accident by investigating the scene based on the reported information.

[0078] By converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills. Converting traffic lights to LED includes, for example, energy-saving design and optimization of lighting times. For example, by converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills. In addition, by converting traffic lights to LED, it is possible to minimize power consumption and improve energy efficiency. Furthermore, by converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills. As a result, by converting traffic lights to LED, it is possible to reduce installation and maintenance costs as well as electricity bills.

[0079] Millimeter wave 5G base stations can provide a communications environment suitable for Level 4 and 5 autonomous driving. Level 4 and 5 autonomous driving includes, for example, fully autonomous driving and automation of all driving operations. For example, millimeter wave 5G base stations can provide latency of less than 1 millisecond and communications speeds of more than 1 Gbps. Furthermore, millimeter wave 5G base stations can provide the communications environment necessary for Level 4 and 5 autonomous driving. By providing the communications environment necessary for Level 4 and 5 autonomous driving, the safety and efficiency of autonomous vehicles will be improved.

[0080] A millimeter wave 5G base station can estimate a user's emotions and adjust communication priorities based on the estimated user emotions. A millimeter wave 5G base station can estimate a user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if a user is facing an emergency, a generation AI can estimate the user's emotions and set communication priority to the highest level. If the user is relaxed, the generation AI can estimate the user's emotions and return communication priority to a normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the user's emotions and set communication priority to a medium level. This allows communication priorities to be adjusted based on the user's emotions, giving priority to emergency communications. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The installation location of mmWave 5G base stations can be optimized based on traffic volume and accident rates. For example, mmWave 5G base stations can be installed at intersections with heavy traffic to meet communication demands. Also, mmWave 5G base stations can be installed in locations with high accident rates to enable rapid accident response. Furthermore, traffic volume and accident rate data can be analyzed to select the optimal installation location. By optimizing installation locations based on traffic volume and accident rates, communication demands can be met and accidents can be quickly responded to.

[0082] A mmWave 5G base station can adjust its communication range appropriately depending on surrounding buildings and terrain. For example, a mmWave 5G base station can dynamically adjust its communication range using criteria for expanding or contracting the communication range and the timing of adjustment. For example, in areas with many high-rise buildings, the communication range can be narrowed to prevent signal interference. On flat terrain, the communication range can be widened to expand the coverage area. Furthermore, the communication range can be adjusted in real time in response to changes in surrounding buildings and terrain. This allows for dynamic adjustment of the communication range depending on surrounding buildings and terrain, preventing communication interference and optimizing the coverage area.

[0083] A millimeter wave 5G base station can optimize power consumption according to the operating status of a traffic signal. For example, a millimeter wave 5G base station can optimize power consumption using a power consumption reduction rate or a specific reduction method. For example, it can reduce power consumption when the traffic signal is red. It can also increase power consumption when the traffic signal is green to meet communication demands. It can also monitor the operating status of traffic signals in real time and optimize power consumption. This improves energy efficiency by optimizing power consumption according to the operating status of traffic signals.

[0084] A millimeter wave 5G base station can estimate a user's emotions and adjust the communication bandwidth based on the estimated user emotions. A millimeter wave 5G base station can estimate a user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if a user is facing an emergency, a generation AI can estimate the user's emotions and set the communication bandwidth to maximum. If the user is relaxed, the generation AI can estimate the user's emotions and return the communication bandwidth to a normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the user's emotions and set the communication bandwidth to medium. This allows priority to be given to emergency communications by adjusting the communication bandwidth 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.

[0085] mmWave 5G base stations can select installation locations to avoid interference with surrounding Wi-Fi networks. For example, mmWave 5G base stations can analyze the signal strength of surrounding Wi-Fi networks and select locations with minimal interference. For example, they can monitor Wi-Fi network usage in real time and select the optimal installation location. They can also dynamically adjust the installation location of mmWave 5G base stations to minimize interference with Wi-Fi networks. This avoids interference with surrounding Wi-Fi networks and improves communication quality.

[0086] A millimeter wave 5G base station can expand or contract its communication range appropriately according to the surrounding traffic conditions. For example, a millimeter wave 5G base station can dynamically adjust its communication range using an adjustment method according to changes in traffic conditions. For example, it can expand the communication range during times of heavy traffic to meet communication demand. It can also reduce the communication range during times of light traffic to reduce power consumption. Furthermore, it can monitor surrounding traffic conditions in real time and dynamically adjust the communication range. This allows it to dynamically adjust the communication range according to the surrounding traffic conditions to meet communication demand and reduce power consumption.

[0087] Millimeter-wave 5G base stations can minimize power consumption by using renewable energy sources such as solar power and wind power. Millimeter-wave 5G base stations can minimize power consumption by using renewable energy sources such as solar power and wind power. For example, solar power can be used to minimize power consumption during the day. Wind power can also be used to minimize power consumption on windy days. Furthermore, the usage status of renewable energy can be monitored in real time to minimize power consumption. This makes it possible to minimize power consumption and reduce environmental impact by using renewable energy.

[0088] The remote camera can estimate the user's emotions and adjust the camera's shooting angle based on the estimated user emotions. The remote camera can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is nervous, the generation AI can estimate the emotion and capture the image with a wide angle to widen the field of view. If the user is relaxed, the generation AI can estimate the emotion and return the camera to a normal shooting angle. Furthermore, if the user is excited, the generation AI can estimate the emotion and focus on a specific subject. This allows the camera's shooting angle to be adjusted based on the user's emotions, providing an appropriate field of view. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The remote camera can change the resolution as appropriate depending on the occurrence of a traffic accident. The remote camera can dynamically change the resolution, for example, using a resolution range or the timing of the change. For example, if a traffic accident occurs, the resolution can be increased to record detailed video. If no traffic accident occurs, the resolution can be decreased to reduce the amount of data. Furthermore, the occurrence of traffic accidents can be monitored in real time and the resolution can be dynamically changed. This makes it possible to record detailed video and optimize the amount of data by dynamically changing the resolution depending on the occurrence of a traffic accident.

[0090] The installation location of a remote camera can be optimized based on the frequency of traffic accidents. The installation location of a remote camera can be optimized using, for example, a data analysis method of accident frequency and installation location selection criteria. For example, remote cameras can be installed at intersections where traffic accidents occur frequently to enhance accident detection. Also, installation of remote cameras can be avoided in locations where traffic accidents occur less frequently. Furthermore, data on the frequency of traffic accidents can be analyzed to select the optimal installation location. In this way, optimizing installation locations based on the frequency of traffic accidents improves the accuracy of accident detection.

[0091] Remote cameras can instantly upload data to the cloud for analysis. Remote cameras can upload data to the cloud in real time, for example, using the timing of upload and the type of data. For example, video captured by a remote camera can be uploaded to the cloud in real time, and analyzed by a generating AI. In addition, traffic accidents can be detected quickly based on the data analyzed on the cloud. Furthermore, data uploaded in real time can be analyzed to respond to changes in traffic conditions. This allows data to be uploaded to the cloud in real time and analyzed, enabling quick accident detection and response.

[0092] The remote camera can estimate the user's emotions and adjust the zoom level of the camera based on the estimated user emotions. The remote camera can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is nervous, the generation AI can estimate the emotion and zoom out to provide a wider field of view. If the user is relaxed, the generation AI can estimate the emotion and return to a normal zoom level. Furthermore, if the user is excited, the generation AI can estimate the emotion and zoom in on a specific object. This allows for an appropriate field of view to be provided by adjusting the camera's zoom level based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The remote camera can change the resolution appropriately depending on the ambient lighting conditions. The remote camera can dynamically change the resolution, for example, using a resolution range or the timing of the change. For example, when the lighting is low, the resolution can be increased to record detailed images. When the lighting is bright, the resolution can be decreased to reduce the amount of data. Furthermore, the ambient lighting conditions can be monitored in real time and the resolution can be dynamically changed. This allows for detailed video recording and optimization of the amount of data by dynamically changing the resolution depending on the ambient lighting conditions.

[0094] The installation location of the remote camera can be selected so as to avoid surrounding buildings and obstacles. The installation location of the remote camera can be optimized using, for example, building and obstacle avoidance criteria and installation location selection criteria. For example, the remote camera can be installed in a high position to avoid surrounding buildings and obstacles. Also, a location that is not affected by buildings and obstacles can be selected and the remote camera can be installed. Furthermore, the arrangement of surrounding buildings and obstacles can be analyzed to select the optimal installation location. This allows the camera's field of view to be optimized by avoiding surrounding buildings and obstacles, improving the quality of the video.

[0095] Remote cameras can analyze data using AI to predict traffic accidents. Remote cameras can analyze data using, for example, machine learning algorithms or data analysis methods. For example, AI can analyze footage captured by remote cameras to predict the occurrence of traffic accidents. Furthermore, based on the data analyzed by AI, it is possible to identify locations with a high risk of traffic accidents. Furthermore, AI can analyze data from remote cameras in real time to predict traffic accidents. As a result, using AI to analyze data improves the accuracy of traffic accident predictions.

[0096] The notification unit can estimate the user's emotions and adjust the urgency of the notification based on the estimated user emotions. The notification unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is facing an emergency, the generation AI can estimate the emotion and set the urgency of the notification to the highest level. If the user is relaxed, the generation AI can estimate the emotion and return the urgency of the notification to the normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the emotion and set the urgency of the notification to a medium level. This allows for a faster response in an emergency by adjusting the urgency of the notification based on the user's emotions. The 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.

[0097] The reporting unit can optimize the reporting method according to the scale of the traffic accident. The reporting unit can optimize the reporting method, for example, by using the type of reporting method and optimization criteria. For example, when a serious traffic accident occurs, a report can be made quickly by using a combination of voice and text reporting. Also, when a minor traffic accident occurs, a report can be made only by text reporting. Furthermore, the scale of the traffic accident can be analyzed in real time and the optimal reporting method can be selected. As a result, appropriate reporting can be made by optimizing the reporting method according to the scale of the traffic accident.

[0098] The reporting unit can automatically select a reporting destination based on the location of the accident. The reporting unit can automatically select a reporting destination using, for example, a list of reporting destinations or selection criteria. For example, the reporting unit can automatically report to the emergency response organization closest to the location of the accident. The reporting unit can also select an appropriate reporting destination based on the location of the accident. Furthermore, the reporting unit can analyze data on the location of the accident and automatically select the optimal reporting destination. This allows for a rapid response by automatically selecting a reporting destination based on the location of the accident.

[0099] The reporting unit can appropriately generate report content including detailed information about the accident. The reporting unit can dynamically generate report content using, for example, the type of report content and the timing of generation. For example, the reporting unit can analyze the accident occurrence situation in real time and generate detailed report content. In addition, the reporting unit can automatically generate report content including detailed information about the accident (location, time, scale, etc.). Furthermore, the reporting unit can dynamically generate report content according to the accident situation and provide appropriate information. As a result, by dynamically generating report content including detailed information about the accident, appropriate information can be provided.

[0100] The notification unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated user emotions. The notification unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is facing an emergency, the generation AI can estimate the emotion and set the notification frequency high. Also, if the user is relaxed, the generation AI can estimate the emotion and return the notification frequency to a normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the emotion and set the notification frequency to a medium level. This allows for a faster response in an emergency by adjusting the notification frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The reporting unit can report using multiple methods, such as voice reporting and text reporting. The reporting unit can report using multiple methods, for example, using voice reporting, text reporting, and the timing of reporting. For example, when a serious traffic accident occurs, a report can be made quickly using both voice reporting and text reporting. Also, when a minor traffic accident occurs, a report can be made using only text reporting. Furthermore, the scale of the traffic accident can be analyzed in real time and the optimal reporting method can be selected. As a result, using multiple reporting methods enables quick and reliable reporting.

[0102] The reporting unit can automatically notify surrounding emergency response organizations. The reporting unit can automatically notify surrounding emergency response organizations using, for example, a list of notification destinations and notification timing. For example, the reporting unit can automatically notify the emergency response organization closest to the location where the accident occurred. In addition, the reporting unit can select an appropriate reporting destination based on the location of the accident. Furthermore, the reporting unit can analyze data on the location of the accident and automatically select the optimal reporting destination. This allows for a rapid response by automatically notifying surrounding emergency response organizations.

[0103] The reporting unit can appropriately generate report content including video data of the accident. The reporting unit can dynamically generate report content using, for example, the type of video data and the timing of generation. For example, the reporting unit can analyze the accident occurrence situation in real time and generate report content including video data. In addition, the reporting unit can automatically generate report content including detailed information about the accident (location, time, scale, etc.). Furthermore, the reporting unit can dynamically generate report content including video data according to the accident situation and provide appropriate information. As a result, by generating report content including video data of the accident, detailed information can be provided.

[0104] The response unit can estimate the user's emotions and adjust the response priority based on the estimated user emotions. The response unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is facing an emergency, the generation AI can estimate the emotion and set the response priority to the highest. If the user is relaxed, the generation AI can estimate the emotion and return the response priority to a normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the emotion and set the response priority to a medium level. This speeds up emergency responses by adjusting the response priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The response unit can optimize the response method according to the scale of the traffic accident. The response unit can optimize the response method using, for example, the type of response method and optimization criteria. For example, when a serious traffic accident occurs, on-site investigation and first aid can be carried out quickly. On the other hand, when a minor traffic accident occurs, only on-site investigation can be carried out. Furthermore, the scale of the traffic accident can be analyzed in real time and the optimal response method can be selected. As a result, an appropriate response can be taken by optimizing the response method according to the scale of the traffic accident.

[0106] The response unit can automatically select a response destination based on the location of the accident. The response unit can automatically select a response destination using, for example, a list of response destinations or selection criteria. For example, the response unit can automatically notify the emergency response organization closest to the location of the accident. The response unit can also select an appropriate response destination based on the location of the accident. Furthermore, the response unit can analyze data on the location of the accident and automatically select the optimal response destination. This enables a rapid response by automatically selecting a response destination based on the location of the accident.

[0107] The response unit can appropriately generate response content including detailed information about the accident. The response unit can dynamically generate response content using, for example, the type of response content and the timing of generation. For example, the response unit can analyze the accident occurrence situation in real time and generate detailed response content. In addition, the response unit can automatically generate response content including detailed information about the accident (location, time, scale, etc.). Furthermore, the response content can be dynamically generated according to the accident situation and appropriate information can be provided. As a result, by dynamically generating response content including detailed information about the accident, appropriate information can be provided.

[0108] The response unit can estimate the user's emotions and adjust the frequency of responses based on the estimated user emotions. The response unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is facing an emergency, the generation AI can estimate the emotion and set the frequency of responses high. Also, if the user is relaxed, the generation AI can estimate the emotion and return the frequency of responses to a normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the emotion and set the frequency of responses to a medium level. This speeds up emergency responses by adjusting the frequency of responses based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The response unit can respond in multiple ways, such as by conducting on-site investigation and first aid. The response unit can respond in multiple ways, for example, by using on-site investigation, first aid, and the timing of the response. For example, when a serious traffic accident occurs, on-site investigation and first aid can be carried out quickly. Also, when a minor traffic accident occurs, only on-site investigation can be carried out. Furthermore, the scale of the traffic accident can be analyzed in real time, and the optimal response method can be selected. This makes it possible to respond quickly and appropriately by using multiple response methods.

[0110] The response unit can automatically notify surrounding emergency response organizations. The response unit can automatically notify surrounding emergency response organizations using, for example, a list of notification destinations and notification timing. For example, the response unit can automatically notify the emergency response organization closest to the location where the accident occurred. Also, the response unit can select an appropriate response destination based on the location of the accident. Furthermore, the response unit can analyze data on the location of the accident and automatically select the most appropriate response destination. This allows for a rapid response by automatically notifying surrounding emergency response organizations.

[0111] The response unit can appropriately generate response content including video data of the accident. The response unit can dynamically generate response content using, for example, the type of video data and the timing of generation. For example, the response unit can analyze the accident occurrence situation in real time and generate response content including video data. In addition, the response unit can automatically generate response content including detailed information about the accident (location, time, scale, etc.). Furthermore, response content including video data can be dynamically generated according to the accident situation and appropriate information can be provided. As a result, by generating response content including video data of the accident, detailed information can be provided.

[0112] The LED light switch on traffic lights can estimate the user's emotions and adjust the priority based on the estimated user emotions. The LED light switch on traffic lights can estimate the user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if the user is facing an emergency, the generation AI can estimate the user's emotions and set the priority of LED light switch to the highest. If the user is relaxed, the generation AI can estimate the user's emotions and return the priority of LED light switch to the normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the user's emotions and set the priority of LED light switch to a medium level. This speeds up emergency response by adjusting the priority of LED light switch based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The conversion of traffic lights to LED can be optimized based on traffic volume and accident rates. The conversion of traffic lights to LED can be optimized, for example, using data analysis methods on traffic volume and accident rates. For example, LED conversion can be prioritized at intersections with heavy traffic volume. Also, LED conversion can be prioritized at locations with high accident rates. Furthermore, data on traffic volume and accident rates can be analyzed to select the optimal locations for LED conversion. This allows for efficient installation by optimizing the conversion to LED based on traffic volume and accident rates.

[0114] The use of LED traffic lights can be adjusted appropriately according to the ambient lighting conditions. For example, the brightness of the LEDs can be dynamically adjusted using an adjustment method according to changes in lighting conditions. For example, when the lighting is dim, the LED brightness can be set high. When the lighting is bright, the LED brightness can be set low. Furthermore, the ambient lighting conditions can be monitored in real time and the LED brightness can be dynamically adjusted. This improves visibility by dynamically adjusting the LED brightness according to the ambient lighting conditions.

[0115] The conversion of traffic lights to LEDs can be designed to minimize power consumption. For example, the conversion of traffic lights to LEDs can minimize power consumption using a power consumption reduction rate or specific reduction methods. For example, energy-saving designs can be adopted to minimize the power consumption of LEDs. Furthermore, the lighting time of LEDs can be optimized to reduce power consumption. Furthermore, power consumption can be monitored in real time and adjustments can be made to minimize it. This minimizes power consumption, improving energy efficiency.

[0116] The LED traffic light conversion can estimate a user's emotions and adjust the schedule based on the estimated user emotions. The LED traffic light conversion can estimate a user's emotions using, for example, facial expression recognition, voice analysis, or heart rate measurement. For example, if a user is facing an emergency, the generation AI can estimate the user's emotions and advance the LED conversion schedule. Also, if the user is relaxed, the generation AI can estimate the user's emotions and return the LED conversion schedule to the normal setting. Furthermore, if the user is feeling stressed, the generation AI can estimate the user's emotions and adjust the LED conversion schedule. This speeds up emergency response by adjusting the LED conversion schedule based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The conversion of traffic lights to LED can be done using renewable energy such as solar power and wind power. The conversion of traffic lights to LED can minimize power consumption by using renewable energy such as solar power and wind power. For example, solar power can be used to minimize power consumption during the day. Also, wind power can be used to minimize power consumption on windy days. Furthermore, the usage status of renewable energy can be monitored in real time to minimize power consumption. In this way, by using renewable energy, power consumption can be minimized and the environmental burden can be reduced.

[0118] LED traffic lights can be designed to avoid surrounding buildings and obstacles. LED traffic lights can be designed using building and obstacle avoidance criteria and design details, for example. For example, LED traffic lights can be installed in high positions to avoid surrounding buildings and obstacles. LED traffic lights can also be installed in locations that are not affected by buildings and obstacles. Furthermore, the layout of surrounding buildings and obstacles can be analyzed to select the optimal installation location. This improves visibility and installation efficiency by avoiding surrounding buildings and obstacles.

[0119] The use of LED traffic lights can be adjusted appropriately according to the traffic signal's operating status. For example, the brightness of the LEDs can be dynamically adjusted using an adjustment method according to the traffic signal's operating status. For example, the brightness of the LEDs can be reduced when the traffic signal is red. Also, the brightness of the LEDs can be increased when the traffic signal is green, improving visibility. Furthermore, the operating status of the traffic signal can be monitored in real time and the brightness of the LEDs can be dynamically adjusted. This improves visibility by dynamically adjusting the brightness of the LEDs according to the traffic signal's operating status. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned millimeter wave 5G base station, remote camera, reporting unit, and response unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the millimeter wave 5G base station is installed in either the data processing device 12 or the smart device 14. For example, the remote camera is connected to either the data processing device 12 or the smart device 14 and detects traffic accidents. The reporting unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. The response unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned millimeter wave 5G base station, remote camera, reporting unit, and response unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the millimeter wave 5G base station is installed in either the data processing device 12 or the smart glasses 214. For example, the remote camera is connected to either the data processing device 12 or the smart glasses 214 and detects traffic accidents. The reporting unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. The response unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned millimeter wave 5G base station, remote camera, reporting unit, and response unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the millimeter wave 5G base station is installed in either the data processing device 12 or the headset type terminal 314. For example, the remote camera is connected to either the data processing device 12 or the headset type terminal 314 and detects traffic accidents. The reporting unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. The response unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned millimeter wave 5G base station, remote camera, reporting unit, and response unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the millimeter wave 5G base station is installed in either the data processing device 12 or the robot 414. For example, the remote camera is connected to either the data processing device 12 or the robot 414 and detects traffic accidents. The reporting unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. The response unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] Millimeter-wave 5G base stations can analyze ambient noise and adjust communication priorities according to noise levels. For example, if the ambient noise level is high, communication priority can be set high to ensure important information is transmitted. If the noise level is low, communication priority can be returned to normal. Furthermore, changes in noise levels can be monitored in real time and communication priorities can be dynamically adjusted. This allows important information to be transmitted reliably by adjusting communication priorities according to the ambient noise level.

[0122] The remote camera can estimate the user's emotions and adjust the camera's exposure settings based on the estimated user emotions. For example, if the user is nervous, the generation AI can estimate the emotion and set the exposure low to reduce visual stimulation. If the user is relaxed, the generation AI can estimate the emotion and return the exposure to normal settings. Furthermore, if the user is excited, the generation AI can estimate the emotion and set the exposure high to emphasize visual information. This makes it possible to provide appropriate visual information by adjusting the camera's exposure settings based on the user's emotions.

[0123] The reporting unit can acquire surrounding weather information when a traffic accident occurs and reflect it in the report content. For example, slippery road conditions can be included in the report content when it is raining. In addition, information about poor visibility can be added when there is fog. Furthermore, when strong winds are blowing, report content can be generated that takes the effects of wind into account. As a result, by reflecting surrounding weather information in the report content when a traffic accident occurs, more accurate information can be provided.

[0124] The response unit can estimate the user's emotions and adjust the response method based on the estimated user emotions. For example, if the user is in a panic, the generation AI can estimate the emotions and prioritize responses to calm the user. If the user is calm, the generation AI can estimate the emotions and respond quickly. Furthermore, if the user is confused, the generation AI can estimate the emotions and respond to organize the situation. This makes it possible to respond appropriately by adjusting the response method based on the user's emotions.

[0125] The conversion of traffic lights to LEDs makes it possible to dynamically change lighting patterns according to the volume of surrounding traffic. For example, the lights can be turned on for shorter periods during times of heavy traffic, allowing for smoother traffic flow. The lights can also be turned on for longer periods during times of light traffic, reducing energy consumption. Furthermore, changes in traffic volume can be monitored in real time and lighting patterns can be dynamically changed. This makes it possible to optimize traffic flow and improve energy efficiency by dynamically changing lighting patterns according to the volume of surrounding traffic.

[0126] Millimeter wave 5G base stations can estimate a user's emotions and adjust the communication security level based on the estimated user emotions. For example, if a user feels anxious, the generation AI can estimate their emotions and set the communication security level high. Alternatively, if the user feels relieved, the generation AI can estimate their emotions and return the communication security level to the normal setting. Furthermore, if the user feels nervous, the generation AI can estimate their emotions and set the security level to medium. This allows appropriate security measures to be taken by adjusting the communication security level based on the user's emotions.

[0127] The remote camera can obtain ambient temperature information and optimize its operation. For example, if the ambient temperature is high, the camera's cooling function can be strengthened to stabilize operation. Also, if the ambient temperature is low, the camera's operating speed can be adjusted and it can switch to energy-saving mode. Furthermore, temperature changes can be monitored in real time and camera operation can be dynamically optimized. This allows for stable video recording by optimizing camera operation based on ambient temperature information.

[0128] The reporting unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated user emotions. For example, if the user is facing an emergency, the generation AI can estimate the user's emotions and write detailed report content. If the user is relaxed, the generation AI can estimate the user's emotions and summarize the report content concisely. Furthermore, if the user is feeling stressed, the generation AI can estimate the user's emotions and set the level of detail in the report to a medium level. This makes it possible to provide appropriate information by adjusting the level of detail in the report content based on the user's emotions.

[0129] The response unit can obtain the status of surrounding traffic signals when a traffic accident occurs and optimize the response method. For example, if the traffic signal is red, the response unit adjusts the response method taking into account the time it takes to arrive at the scene. Also, if the traffic signal is green, a quick response can be made. Furthermore, the status of traffic signals can be monitored in real time and the response method can be dynamically optimized. This makes it possible to respond to a traffic accident taking into account the status of surrounding traffic signals.

[0130] LED traffic lights can estimate the user's emotions and adjust the color temperature of the LEDs based on the estimated user's emotions. For example, if the user is nervous, the generation AI can estimate the emotion and set the color temperature to warm colors to enhance the relaxation effect. If the user is relaxed, the generation AI can estimate the emotion and return the color temperature to the normal setting. Furthermore, if the user is excited, the generation AI can estimate the emotion and set the color temperature to cool colors to enhance concentration. This makes it possible to provide an appropriate visual environment by adjusting the color temperature of the LEDs based on the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: Millimeter-wave 5G base stations will be installed at traffic lights to provide a low-latency, high-speed communication network. For example, millimeter-wave 5G base stations can provide latency of less than 1 millisecond and communication speeds of more than 1 Gbps. In addition, millimeter-wave 5G base stations can provide the communication environment required for Level 4 and 5 autonomous driving. Step 2: The remote camera automatically detects traffic accidents using image recognition technology. For example, the remote camera can use deep learning-based object detection algorithms to detect traffic accidents with high accuracy. The remote camera can also detect traffic accidents in real time and send information to the reporting center. Step 3: The reporting unit promptly reports a traffic accident when it detects it. For example, the reporting unit can report a traffic accident within one second of detecting it. The reporting unit can also automatically select the contact person and content of the report, and provide appropriate information. Step 4: The response department conducts an on-site investigation based on the reported information. For example, the response department can take photographs of the scene, collect evidence, and analyze the situation. The response department can also take appropriate action based on the results of the on-site investigation.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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. Millimeter wave 5G base stations installed in traffic lights, A remote camera that automatically detects traffic accidents based on the low-latency, high-speed communication network provided by the millimeter wave 5G base station; a reporting unit that reports a traffic accident detected by the remote camera; a response unit that takes action based on the information reported by the reporting unit; Equipped with A system characterized by:

2. The remote camera is Automatically detect traffic accidents using image recognition technology 2. The system of claim 1.

3. The reporting unit Prompt reporting of detected traffic accidents 2. The system of claim 1.

4. The corresponding part is Conduct an on-site investigation based on the reported information 2. The system of claim 1.

5. By promoting the conversion of traffic lights to LED, Reduce installation and maintenance costs, as well as electricity bills 2. The system of claim 1.

6. The millimeter wave 5G base station Providing a communications environment suitable for Level 4 and 5 autonomous driving 2. The system of claim 1.

7. The millimeter wave 5G base station Estimate user emotions and adjust communication priorities based on the estimated user emotions 2. The system of claim 1.

8. The millimeter wave 5G base station Optimize installation locations based on traffic volume and accident rates 2. The system of claim 1.

9. The millimeter wave 5G base station Adapts communication range according to surrounding buildings and terrain 2. The system of claim 1.

Images