system

Abstract

The system according to this embodiment aims to detect abnormal situations and human behavior in real time during urban area monitoring and to respond quickly. [Solution] The system according to the embodiment comprises a monitoring unit, a transmitting unit, and a control unit. The monitoring unit is equipped with an AI camera and sensors for monitoring urban areas. The transmitting unit transmits abnormal situations and human behavior detected by the monitoring unit to a management center in real time. The control unit controls IoT devices and robots based on the information transmitted by the transmitting unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to detect abnormal situations and human behaviors in real time in the monitoring of urban areas and respond quickly.

[0005] The system according to the embodiment aims to detect abnormal situations and human behaviors in real time in the monitoring of urban areas and respond quickly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a transmission unit, and a control unit. The monitoring unit is equipped with an AI camera and sensors for monitoring urban areas. The transmission unit transmits abnormal situations and human behavior detected by the monitoring unit to a management center in real time. The control unit controls IoT devices and robots based on the information transmitted by the transmission unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect abnormal situations and human behavior in real time during urban area monitoring and respond quickly. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The urban monitoring system according to an embodiment of the present invention is an innovative system that combines communication infrastructure and AI technology. This urban monitoring system monitors urban areas using AI cameras and sensors attached to existing infrastructure, detecting abnormal situations and human behavior in real time. This system transmits the detected information to a management center in real time and, by coordinating with IoT devices and robots, enables a rapid and appropriate response, significantly improving urban safety. For example, the urban monitoring system uses AI cameras to analyze video footage and detect suspicious movements, group behavior, traffic accidents, etc. The detected information is transmitted to the management center in real time, and appropriate actions are taken. IoT devices and robots are dispatched to the scene to confirm the situation and take necessary actions. For example, a robot arrives at the scene, confirms the situation with a camera, and contacts the police or fire department as needed. In this way, urban safety can be enhanced. As a result, the urban monitoring system can monitor urban areas, detect abnormal situations and human behavior in real time, and enable a rapid and appropriate response.

[0029] The urban monitoring system according to this embodiment comprises a monitoring unit, a transmission unit, and a control unit. The monitoring unit is equipped with an AI camera and sensors for monitoring an urban area. The monitoring unit can, for example, analyze video in real time using the AI ​​camera to detect suspicious movements, group behavior, traffic accidents, etc. The monitoring unit can, for example, have the AI ​​camera detect abnormal behavior in a specific area. The monitoring unit can also have the AI ​​camera detect specific patterns of movement. Furthermore, the monitoring unit can have the AI ​​camera detect traffic accidents. The transmission unit transmits abnormal situations and human behavior detected by the monitoring unit to a management center in real time. The transmission unit can, for example, transmit detected information to the management center in real time. The transmission unit can, for example, transmit information in seconds. Furthermore, the transmission unit can also transmit information in milliseconds. Furthermore, the transmission unit can also transmit information in real time. The control unit controls IoT devices and robots based on the information transmitted by the transmission unit. The control unit can, for example, control IoT devices and robots based on instructions from the management center. The control unit can, for example, control IoT devices. Furthermore, the control unit can also control robots. Furthermore, the control unit can also control IoT devices and robots. As a result, the urban monitoring system according to this embodiment can monitor urban areas, detect abnormal situations and human behavior in real time, and enable a quick and appropriate response.

[0030] The monitoring unit is equipped with AI cameras and sensors for monitoring urban areas. Specifically, the AI ​​cameras acquire high-resolution video in real time and analyze the video using a built-in AI processor. The AI ​​processor uses deep learning algorithms to recognize objects and people in the video and detect anomalies such as suspicious movements, group behavior, and traffic accidents. For example, the AI ​​cameras can detect abnormal behavior in a specific area by comparing it with pre-set behavior patterns and issuing an alert if an anomaly is detected. The AI ​​cameras can also detect specific patterns of movement, such as gatherings of people in a specific area or unusual movements during a specific time period. Furthermore, the AI ​​cameras can detect traffic accidents, such as sudden vehicle stops or collisions, and immediately notify the management center. The sensors collect environmental data such as sound, vibration, and temperature, and work in conjunction with the AI ​​cameras to detect anomalies. As a result, the monitoring unit can comprehensively monitor the entire urban area and quickly detect abnormal situations.

[0031] The transmission unit transmits abnormal situations and human behavior detected by the monitoring unit to the management center in real time. Specifically, the transmission unit can transmit detected information to the management center in seconds, or even milliseconds, using high-speed communication protocols. For example, if the monitoring unit detects abnormal behavior, that information is immediately passed to the transmission unit, which then transmits that information to the management center in real time. The transmission unit utilizes high-speed communication technologies such as 5G and Wi-Fi 6 to transmit large amounts of data with low latency. This allows the management center to immediately grasp abnormal situations occurring in urban areas and take rapid action. Furthermore, the transmission unit is equipped with data encryption and authentication functions to ensure the security of transmitted information. As a result, the transmission unit can achieve highly reliable communication and improve the overall security of the urban monitoring system.

[0032] The control unit controls IoT devices and robots based on information transmitted by the transmitter. Specifically, the control unit can control IoT devices and robots deployed within a city based on instructions from the management center. For example, if an abnormal situation is detected, the control unit can adjust the brightness of streetlights or change the direction of surveillance cameras. The control unit can also control robots and dispatch them to the site to assess the situation. The robots are equipped with cameras and sensors and transmit video and data from the site to the management center in real time. This allows the management center to understand the situation in detail and take appropriate action. Furthermore, the control unit can automate the operation of IoT devices and robots. For example, it can set up programs to automatically control devices and robots when certain conditions are met. This allows the control unit to quickly and efficiently control devices and robots within the city and optimize responses to abnormal situations.

[0033] The monitoring unit can analyze video in real time and detect suspicious movements, group behavior, traffic accidents, etc. For example, the monitoring unit can use an AI camera to analyze video in real time and detect suspicious movements. The monitoring unit can also detect abnormal behavior in a specific area. Furthermore, the monitoring unit can detect specific patterns of movement. In addition, the monitoring unit can detect traffic accidents. As a result, abnormal situations can be quickly detected by analyzing video in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input video data into a generative AI and have the generative AI perform the detection of abnormal situations.

[0034] The transmitting unit can send detected information to the management center in real time. For example, the transmitting unit can send detected information to the management center in real time. The transmitting unit can also send information in seconds, for example. Furthermore, the transmitting unit can send information in milliseconds. In addition, the transmitting unit can send information in real time. This enables a rapid response by sending detected information in real time. Some or all of the above processing in the transmitting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the transmitting unit can input detected information into a generation AI and have the generation AI execute the transmission of the information.

[0035] The control unit can control IoT devices and robots based on instructions from the management center. For example, the control unit can control IoT devices based on instructions from the management center. The control unit can also control robots, for example. Furthermore, the control unit can control IoT devices and robots. This allows for appropriate responses by controlling IoT devices and robots based on instructions from the management center. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input instructions from the management center to a generative AI and have the generative AI execute the control of IoT devices and robots.

[0036] The control unit can dispatch a robot to the scene, check the situation with a camera, and contact the police or fire department as needed. For example, the control unit can dispatch a robot to the scene and check the situation with a camera. The control unit can also contact the police as needed. The control unit can also contact the fire department as needed. This allows for a quick and appropriate response by dispatching a robot to the scene and checking the situation. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input instructions to dispatch a robot to a generative AI and have the generative AI execute the control of the robot.

[0037] The monitoring unit can improve detection accuracy by learning patterns of anomalies by referring to past data in response to detected abnormal situations. For example, the monitoring unit can refer to past crime data to learn abnormal behavior patterns at specific times and locations. For example, the monitoring unit can refer to past traffic accident data to learn abnormal driving behavior at specific intersections. Furthermore, the monitoring unit can refer to past group behavior data to learn abnormal human movements during specific events. This improves detection accuracy by learning patterns of anomalies by referring to past data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input past data into a generative AI and have the generative AI perform the learning of anomaly patterns.

[0038] The monitoring unit can implement a multifaceted approach to anomaly detection by adding audio and temperature sensors to the detected abnormal situations. For example, the monitoring unit can add an audio sensor to detect abnormal sounds (e.g., screams or the sound of breaking glass). The monitoring unit can also add a temperature sensor to detect abnormal temperature changes (e.g., signs of a fire). Furthermore, the monitoring unit can combine audio and temperature sensors to more accurately detect abnormal situations. This improves the accuracy of anomaly detection by adding audio and temperature sensors. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input audio data and temperature data into a generative AI and have the generative AI perform the detection of abnormal situations.

[0039] The monitoring unit can determine the priority of anomalies by considering geographical information when detecting abnormal situations. For example, the monitoring unit may prioritize detecting abnormal behavior in high-crime areas. It may also prioritize detecting abnormal driving at busy intersections. Furthermore, it may prioritize detecting abnormal behavior at locations where large-scale events are being held. This allows for the appropriate determination of anomaly priorities by considering geographical information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input geographical information into a generative AI and have the generative AI perform the determination of anomaly priorities.

[0040] The monitoring unit can evaluate the relevance of detected abnormal situations by referring to social media data. For example, the monitoring unit can refer to posts on social media about abnormal behavior and detect abnormalities in real time. The monitoring unit can also refer to event information on social media and evaluate the possibility of abnormal behavior. Furthermore, the monitoring unit can refer to traffic information on social media and evaluate the possibility of abnormal driving. This allows for more accurate detection by evaluating the relevance of abnormalities by referring to social media data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input social media data into a generative AI and have the generative AI perform the evaluation of the relevance of abnormalities.

[0041] The sending unit can select the optimal sending method for the information to be sent by referring to past sending history. For example, the sending unit may prioritize sending methods that have been effective in the past (e.g., email, SMS). The sending unit may also select a sending method that is effective for a specific time period from past sending history. Furthermore, the sending unit may analyze past sending history and select the sending method that will deliver the information most quickly. In this way, the optimal sending method can be selected by referring to past sending history. Some or all of the above processing in the sending unit may be performed using, for example, a generation AI, or without a generation AI. For example, the sending unit can input past sending history into a generation AI and have the generation AI select the optimal sending method.

[0042] The transmitting unit can enhance the security of the information it transmits by using encryption technology. For example, the transmitting unit may use AES encryption technology when transmitting important information. The transmitting unit may also use RSA encryption technology when transmitting confidential information. Furthermore, the transmitting unit may use TLS encryption technology when transmitting personal information. In this way, the security of the information is enhanced by using encryption technology. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input the information to be transmitted into a generative AI and have the generative AI perform the application of encryption technology.

[0043] The transmitting unit can optimize the destination of the information to be transmitted by taking geographical information into consideration. For example, if the destinations are concentrated in a specific area, the transmitting unit can select the most suitable transmission method for that area. If the destinations are spread over a wide area, the transmitting unit can also select the most suitable transmission method for each area. Furthermore, if the destinations are located within a specific building, the transmitting unit can select the most suitable transmission method for that building. In this way, the destination can be optimized by taking geographical information into consideration. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmitting unit can input geographical information into a generative AI and have the generative AI perform the optimization of the destinations.

[0044] The transmitting unit can customize the content of the information to be transmitted by referring to social media data. For example, the transmitting unit can customize the content by referring to trending information on social media. The transmitting unit can also customize the content by referring to user interests on social media. Furthermore, the transmitting unit can customize the content by referring to local information on social media. In this way, the content of the transmission can be customized by referring to social media data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input social media data into a generative AI and have the generative AI perform the customization of the content of the transmission.

[0045] The control unit can select the optimal control method for the IoT device or robot it controls by referring to past control history. For example, the control unit can prioritize selecting control methods that have been effective in the past. For example, the control unit can also select the optimal control method for a specific situation from past control history. Furthermore, the control unit can analyze past control history and select the control method that can respond most quickly. In this way, the optimal control method can be selected by referring to past control history. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input past control history into a generative AI and have the generative AI select the optimal control method.

[0046] The control unit can implement voice-based control of IoT devices and robots using voice recognition technology. For example, the control unit can control IoT devices based on user voice commands using voice recognition technology. The control unit can also control robots based on user voice commands using voice recognition technology. Furthermore, the control unit can coordinate the control of multiple IoT devices and robots based on user voice commands using voice recognition technology. This makes voice-based control possible by using voice recognition technology. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input voice data into a generative AI and have the generative AI execute voice-based control.

[0047] The control unit can optimize the control range for the IoT devices and robots it controls, taking geographical information into consideration. For example, if an anomaly is detected in a specific area, the control unit will prioritize controlling IoT devices and robots within that area. For example, if an anomaly is detected over a wide area, the control unit can also set an optimal control range for each area. Furthermore, if an anomaly is detected within a specific building, the control unit can prioritize controlling IoT devices and robots within that building. In this way, the control range can be optimized by taking geographical information into consideration. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input geographical information into a generative AI and have the generative AI perform the optimization of the control range.

[0048] The control unit can customize the control of the IoT device or robot it controls by referring to social media data. For example, the control unit can customize the control by referring to trend information on social media. The control unit can also customize the control by referring to user interests on social media. Furthermore, the control unit can customize the control by referring to local information on social media. In this way, the control unit can customize the control by referring to social media data. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input social media data into a generative AI and have the generative AI perform the customization of the control.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The urban monitoring system may also include a predictive unit. This unit can analyze past data and predict future abnormal situations. For example, it can analyze past crime data to predict the likelihood of crime occurring at specific times and locations. It can also analyze past traffic accident data to predict the likelihood of accidents at specific intersections. Furthermore, it can analyze past group behavior data to predict unusual human movements during specific events. This allows the predictive unit to anticipate future abnormal situations and take preventative measures.

[0051] The urban monitoring system may also include a notification unit. This unit can notify relevant parties in response to detected abnormal situations. For example, it can notify the police or fire department if an abnormal situation is detected. It can also arrange for an ambulance if, for instance, a traffic accident is detected. Furthermore, it can notify local residents if unusual behavior is detected in a specific area. This allows the notification unit to quickly notify relevant parties of abnormal situations and encourage appropriate responses.

[0052] The urban monitoring system may also include a feedback unit. The feedback unit can collect the results of responses to detected abnormal situations and use this information to improve the system. For example, the feedback unit can collect the results of responses to abnormal situations and evaluate the effectiveness of those responses. The feedback unit can also collect the results of responses to traffic accidents, for example, and evaluate the speed and effectiveness of those responses. Furthermore, the feedback unit can collect the results of responses to abnormal behavior in specific areas and evaluate the appropriateness of those responses. This allows the feedback unit to collect response results and use them to improve the system.

[0053] The urban surveillance system can also be equipped with an energy management unit. This unit can optimize the overall energy consumption of the system. For example, it can monitor the energy consumption of surveillance cameras and sensors in real time and adjust energy consumption as needed. The energy management unit can also reduce energy consumption by, for example, lowering the resolution of surveillance cameras during nighttime or low-activity periods. Furthermore, the energy management unit can supplement the system's energy consumption by utilizing renewable energy sources such as solar and wind power. This allows the energy management unit to optimize the overall energy consumption of the system and achieve sustainable operation.

[0054] The urban surveillance system may also include a data anonymization unit. This unit can anonymize the collected data and protect privacy. For example, it can remove personally identifiable information from video footage captured by surveillance cameras. It can also remove personally identifiable information from audio data, for instance. Furthermore, it can remove personally identifiable information from location data. In this way, the data anonymization unit can anonymize the collected data and protect privacy.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The monitoring unit is equipped with AI cameras and sensors for monitoring urban areas. The monitoring unit can analyze video in real time using the AI ​​cameras to detect suspicious movements, group behavior, traffic accidents, etc. For example, the AI ​​cameras can detect abnormal behavior or specific patterns of movement in a particular area. Step 2: The transmitting unit sends abnormal situations and human behavior detected by the monitoring unit to the management center in real time. The transmitting unit can transmit the detected information in real time, down to the second or millisecond. Step 3: The control unit controls IoT devices and robots based on the information transmitted by the transmitter. The control unit can control IoT devices and robots based on instructions from the management center.

[0057] (Example of form 2) The urban monitoring system according to an embodiment of the present invention is an innovative system that combines communication infrastructure and AI technology. This urban monitoring system monitors urban areas using AI cameras and sensors attached to existing infrastructure, detecting abnormal situations and human behavior in real time. This system transmits the detected information to a management center in real time and, by coordinating with IoT devices and robots, enables a rapid and appropriate response, significantly improving urban safety. For example, the urban monitoring system uses AI cameras to analyze video footage and detect suspicious movements, group behavior, traffic accidents, etc. The detected information is transmitted to the management center in real time, and appropriate actions are taken. IoT devices and robots are dispatched to the scene to confirm the situation and take necessary actions. For example, a robot arrives at the scene, confirms the situation with a camera, and contacts the police or fire department as needed. In this way, urban safety can be enhanced. As a result, the urban monitoring system can monitor urban areas, detect abnormal situations and human behavior in real time, and enable a rapid and appropriate response.

[0058] The urban monitoring system according to this embodiment comprises a monitoring unit, a transmission unit, and a control unit. The monitoring unit is equipped with an AI camera and sensors for monitoring an urban area. The monitoring unit can, for example, analyze video in real time using the AI ​​camera to detect suspicious movements, group behavior, traffic accidents, etc. The monitoring unit can, for example, have the AI ​​camera detect abnormal behavior in a specific area. The monitoring unit can also have the AI ​​camera detect specific patterns of movement. Furthermore, the monitoring unit can have the AI ​​camera detect traffic accidents. The transmission unit transmits abnormal situations and human behavior detected by the monitoring unit to a management center in real time. The transmission unit can, for example, transmit detected information to the management center in real time. The transmission unit can, for example, transmit information in seconds. Furthermore, the transmission unit can also transmit information in milliseconds. Furthermore, the transmission unit can also transmit information in real time. The control unit controls IoT devices and robots based on the information transmitted by the transmission unit. The control unit can, for example, control IoT devices and robots based on instructions from the management center. The control unit can, for example, control IoT devices. Furthermore, the control unit can also control robots. Furthermore, the control unit can also control IoT devices and robots. As a result, the urban monitoring system according to this embodiment can monitor urban areas, detect abnormal situations and human behavior in real time, and enable a quick and appropriate response.

[0059] The monitoring unit is equipped with AI cameras and sensors for monitoring urban areas. Specifically, the AI ​​cameras acquire high-resolution video in real time and analyze the video using a built-in AI processor. The AI ​​processor uses deep learning algorithms to recognize objects and people in the video and detect anomalies such as suspicious movements, group behavior, and traffic accidents. For example, the AI ​​cameras can detect abnormal behavior in a specific area by comparing it with pre-set behavior patterns and issuing an alert if an anomaly is detected. The AI ​​cameras can also detect specific patterns of movement, such as gatherings of people in a specific area or unusual movements during a specific time period. Furthermore, the AI ​​cameras can detect traffic accidents, such as sudden vehicle stops or collisions, and immediately notify the management center. The sensors collect environmental data such as sound, vibration, and temperature, and work in conjunction with the AI ​​cameras to detect anomalies. As a result, the monitoring unit can comprehensively monitor the entire urban area and quickly detect abnormal situations.

[0060] The transmission unit transmits abnormal situations and human behavior detected by the monitoring unit to the management center in real time. Specifically, the transmission unit can transmit detected information to the management center in seconds, or even milliseconds, using high-speed communication protocols. For example, if the monitoring unit detects abnormal behavior, that information is immediately passed to the transmission unit, which then transmits that information to the management center in real time. The transmission unit utilizes high-speed communication technologies such as 5G and Wi-Fi 6 to transmit large amounts of data with low latency. This allows the management center to immediately grasp abnormal situations occurring in urban areas and take rapid action. Furthermore, the transmission unit is equipped with data encryption and authentication functions to ensure the security of transmitted information. As a result, the transmission unit can achieve highly reliable communication and improve the overall security of the urban monitoring system.

[0061] The control unit controls IoT devices and robots based on information transmitted by the transmitter. Specifically, the control unit can control IoT devices and robots deployed within a city based on instructions from the management center. For example, if an abnormal situation is detected, the control unit can adjust the brightness of streetlights or change the direction of surveillance cameras. The control unit can also control robots and dispatch them to the site to assess the situation. The robots are equipped with cameras and sensors and transmit video and data from the site to the management center in real time. This allows the management center to understand the situation in detail and take appropriate action. Furthermore, the control unit can automate the operation of IoT devices and robots. For example, it can set up programs to automatically control devices and robots when certain conditions are met. This allows the control unit to quickly and efficiently control devices and robots within the city and optimize responses to abnormal situations.

[0062] The monitoring unit can analyze video in real time and detect suspicious movements, group behavior, traffic accidents, etc. For example, the monitoring unit can use an AI camera to analyze video in real time and detect suspicious movements. The monitoring unit can also detect abnormal behavior in a specific area. Furthermore, the monitoring unit can detect specific patterns of movement. In addition, the monitoring unit can detect traffic accidents. As a result, abnormal situations can be quickly detected by analyzing video in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input video data into a generative AI and have the generative AI perform the detection of abnormal situations.

[0063] The transmitting unit can send detected information to the management center in real time. For example, the transmitting unit can send detected information to the management center in real time. The transmitting unit can also send information in seconds, for example. Furthermore, the transmitting unit can send information in milliseconds. In addition, the transmitting unit can send information in real time. This enables a rapid response by sending detected information in real time. Some or all of the above processing in the transmitting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the transmitting unit can input detected information into a generation AI and have the generation AI execute the transmission of the information.

[0064] The control unit can control IoT devices and robots based on instructions from the management center. For example, the control unit can control IoT devices based on instructions from the management center. The control unit can also control robots, for example. Furthermore, the control unit can control IoT devices and robots. This allows for appropriate responses by controlling IoT devices and robots based on instructions from the management center. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input instructions from the management center to a generative AI and have the generative AI execute the control of IoT devices and robots.

[0065] The control unit can dispatch a robot to the scene, check the situation with a camera, and contact the police or fire department as needed. For example, the control unit can dispatch a robot to the scene and check the situation with a camera. The control unit can also contact the police as needed. The control unit can also contact the fire department as needed. This allows for a quick and appropriate response by dispatching a robot to the scene and checking the situation. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input instructions to dispatch a robot to a generative AI and have the generative AI execute the control of the robot.

[0066] The monitoring unit can estimate the user's emotions and adjust the viewpoint and focus of the surveillance camera based on the estimated emotions. For example, if the surveillance camera detects the user's anxiety, the monitoring unit can widen the camera's viewpoint to monitor the surrounding situation in detail. For example, if the surveillance camera detects the user's excitement, the monitoring unit can also focus on a specific area to provide detailed footage. Furthermore, if the surveillance camera detects the user's relaxation, the monitoring unit can return to the normal viewpoint and continue monitoring. This allows for more effective monitoring by adjusting the viewpoint and focus of the surveillance camera based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the viewpoint and focus of the surveillance camera.

[0067] The monitoring unit can improve detection accuracy by learning patterns of anomalies by referring to past data in response to detected abnormal situations. For example, the monitoring unit can refer to past crime data to learn abnormal behavior patterns at specific times and locations. For example, the monitoring unit can refer to past traffic accident data to learn abnormal driving behavior at specific intersections. Furthermore, the monitoring unit can refer to past group behavior data to learn abnormal human movements during specific events. This improves detection accuracy by learning patterns of anomalies by referring to past data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input past data into a generative AI and have the generative AI perform the learning of anomaly patterns.

[0068] The monitoring unit can implement a multifaceted approach to anomaly detection by adding audio and temperature sensors to the detected abnormal situations. For example, the monitoring unit can add an audio sensor to detect abnormal sounds (e.g., screams or the sound of breaking glass). The monitoring unit can also add a temperature sensor to detect abnormal temperature changes (e.g., signs of a fire). Furthermore, the monitoring unit can combine audio and temperature sensors to more accurately detect abnormal situations. This improves the accuracy of anomaly detection by adding audio and temperature sensors. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input audio data and temperature data into a generative AI and have the generative AI perform the detection of abnormal situations.

[0069] The monitoring unit can estimate the user's emotions and adjust the alert level of the surveillance camera based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can set a high alert level to prompt immediate action. If the user is relaxed, the monitoring unit can also set a low alert level and continue normal monitoring. Furthermore, if the user is agitated, the monitoring unit can set a medium alert level and respond accordingly. This allows for a more appropriate response by adjusting the alert level based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the alert level.

[0070] The monitoring unit can determine the priority of anomalies by considering geographical information when detecting abnormal situations. For example, the monitoring unit may prioritize detecting abnormal behavior in high-crime areas. It may also prioritize detecting abnormal driving at busy intersections. Furthermore, it may prioritize detecting abnormal behavior at locations where large-scale events are being held. This allows for the appropriate determination of anomaly priorities by considering geographical information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input geographical information into a generative AI and have the generative AI perform the determination of anomaly priorities.

[0071] The monitoring unit can evaluate the relevance of detected abnormal situations by referring to social media data. For example, the monitoring unit can refer to posts on social media about abnormal behavior and detect abnormalities in real time. The monitoring unit can also refer to event information on social media and evaluate the possibility of abnormal behavior. Furthermore, the monitoring unit can refer to traffic information on social media and evaluate the possibility of abnormal driving. This allows for more accurate detection by evaluating the relevance of abnormalities by referring to social media data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input social media data into a generative AI and have the generative AI perform the evaluation of the relevance of abnormalities.

[0072] The transmission unit can estimate the user's emotions and adjust the priority of the information it transmits based on the estimated emotions. For example, if the user is feeling anxious, the transmission unit will prioritize sending important information. For example, if the user is relaxed, the transmission unit can also send normal information. Furthermore, if the user is excited, the transmission unit can prioritize sending urgent information. This allows for the rapid transmission of important information by adjusting the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the transmission unit can input user emotion data into a generative AI and have the generative AI adjust the priority of information.

[0073] The sending unit can select the optimal sending method for the information to be sent by referring to past sending history. For example, the sending unit may prioritize sending methods that have been effective in the past (e.g., email, SMS). The sending unit may also select a sending method that is effective for a specific time period from past sending history. Furthermore, the sending unit may analyze past sending history and select the sending method that will deliver the information most quickly. In this way, the optimal sending method can be selected by referring to past sending history. Some or all of the above processing in the sending unit may be performed using, for example, a generation AI, or without a generation AI. For example, the sending unit can input past sending history into a generation AI and have the generation AI select the optimal sending method.

[0074] The transmitting unit can enhance the security of the information it transmits by using encryption technology. For example, the transmitting unit may use AES encryption technology when transmitting important information. The transmitting unit may also use RSA encryption technology when transmitting confidential information. Furthermore, the transmitting unit may use TLS encryption technology when transmitting personal information. In this way, the security of the information is enhanced by using encryption technology. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input the information to be transmitted into a generative AI and have the generative AI perform the application of encryption technology.

[0075] The transmitting unit can estimate the user's emotions and adjust the format of the information it transmits based on the estimated emotions. For example, if the user is feeling anxious, the transmitting unit will transmit information in a concise and easily readable format. If the user is relaxed, the transmitting unit may transmit information in a format that includes detailed information. If the user is excited, the transmitting unit may transmit information in a highly urgent format. By adjusting the format of information based on the user's emotions, more effective information transmission becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using a generative AI, or not using a generative AI. For example, the transmitting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the information format.

[0076] The transmitting unit can optimize the destination of the information to be transmitted by taking geographical information into consideration. For example, if the destinations are concentrated in a specific area, the transmitting unit can select the most suitable transmission method for that area. If the destinations are spread over a wide area, the transmitting unit can also select the most suitable transmission method for each area. Furthermore, if the destinations are located within a specific building, the transmitting unit can select the most suitable transmission method for that building. In this way, the destination can be optimized by taking geographical information into consideration. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the transmitting unit can input geographical information into a generative AI and have the generative AI perform the optimization of the destinations.

[0077] The transmitting unit can customize the content of the information to be transmitted by referring to social media data. For example, the transmitting unit can customize the content by referring to trending information on social media. The transmitting unit can also customize the content by referring to user interests on social media. Furthermore, the transmitting unit can customize the content by referring to local information on social media. In this way, the content of the transmission can be customized by referring to social media data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input social media data into a generative AI and have the generative AI perform the customization of the content of the transmission.

[0078] The control unit can estimate the user's emotions and adjust the operation of IoT devices and robots based on the estimated emotions. For example, if the user is feeling anxious, the control unit may quickly operate the IoT devices or robots. If the user is relaxed, the control unit may operate the IoT devices or robots normally. Furthermore, if the user is excited, the control unit may operate the IoT devices or robots cautiously. This allows for a more appropriate response by adjusting the operation of IoT devices and robots based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using or without a generative AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI adjust the operation of IoT devices and robots.

[0079] The control unit can select the optimal control method for the IoT device or robot it controls by referring to past control history. For example, the control unit can prioritize selecting control methods that have been effective in the past. For example, the control unit can also select the optimal control method for a specific situation from past control history. Furthermore, the control unit can analyze past control history and select the control method that can respond most quickly. In this way, the optimal control method can be selected by referring to past control history. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input past control history into a generative AI and have the generative AI select the optimal control method.

[0080] The control unit can implement voice-based control of IoT devices and robots using voice recognition technology. For example, the control unit can control IoT devices based on user voice commands using voice recognition technology. The control unit can also control robots based on user voice commands using voice recognition technology. Furthermore, the control unit can coordinate the control of multiple IoT devices and robots based on user voice commands using voice recognition technology. This makes voice-based control possible by using voice recognition technology. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input voice data into a generative AI and have the generative AI execute voice-based control.

[0081] The control unit can estimate the user's emotions and determine the priority of IoT devices and robots to control based on the estimated emotions. For example, if the user is feeling anxious, the control unit will prioritize the operation of important IoT devices and robots. If the user is relaxed, the control unit can also control IoT devices and robots with normal priority. Furthermore, if the user is agitated, the control unit can prioritize the operation of high-priority IoT devices and robots. This allows for a more appropriate response by determining the priority of IoT devices and robots based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using or without a generative AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI determine the priority of IoT devices and robots.

[0082] The control unit can optimize the control range for the IoT devices and robots it controls, taking geographical information into consideration. For example, if an anomaly is detected in a specific area, the control unit will prioritize controlling IoT devices and robots within that area. For example, if an anomaly is detected over a wide area, the control unit can also set an optimal control range for each area. Furthermore, if an anomaly is detected within a specific building, the control unit can prioritize controlling IoT devices and robots within that building. In this way, the control range can be optimized by taking geographical information into consideration. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input geographical information into a generative AI and have the generative AI perform the optimization of the control range.

[0083] The control unit can customize the control of the IoT device or robot it controls by referring to social media data. For example, the control unit can customize the control by referring to trend information on social media. The control unit can also customize the control by referring to user interests on social media. Furthermore, the control unit can customize the control by referring to local information on social media. In this way, the control unit can customize the control by referring to social media data. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the control unit can input social media data into a generative AI and have the generative AI perform the customization of the control.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The urban monitoring system may also include a predictive unit. This unit can analyze past data and predict future abnormal situations. For example, it can analyze past crime data to predict the likelihood of crime occurring at specific times and locations. It can also analyze past traffic accident data to predict the likelihood of accidents at specific intersections. Furthermore, it can analyze past group behavior data to predict unusual human movements during specific events. This allows the predictive unit to anticipate future abnormal situations and take preventative measures.

[0086] The urban monitoring system may also include a notification unit. This unit can notify relevant parties in response to detected abnormal situations. For example, it can notify the police or fire department if an abnormal situation is detected. It can also arrange for an ambulance if, for instance, a traffic accident is detected. Furthermore, it can notify local residents if unusual behavior is detected in a specific area. This allows the notification unit to quickly notify relevant parties of abnormal situations and encourage appropriate responses.

[0087] The urban monitoring system may also include a feedback unit. The feedback unit can collect the results of responses to detected abnormal situations and use this information to improve the system. For example, the feedback unit can collect the results of responses to abnormal situations and evaluate the effectiveness of those responses. The feedback unit can also collect the results of responses to traffic accidents, for example, and evaluate the speed and effectiveness of those responses. Furthermore, the feedback unit can collect the results of responses to abnormal behavior in specific areas and evaluate the appropriateness of those responses. This allows the feedback unit to collect response results and use them to improve the system.

[0088] The urban surveillance system can also be equipped with an energy management unit. This unit can optimize the overall energy consumption of the system. For example, it can monitor the energy consumption of surveillance cameras and sensors in real time and adjust energy consumption as needed. The energy management unit can also reduce energy consumption by, for example, lowering the resolution of surveillance cameras during nighttime or low-activity periods. Furthermore, the energy management unit can supplement the system's energy consumption by utilizing renewable energy sources such as solar and wind power. This allows the energy management unit to optimize the overall energy consumption of the system and achieve sustainable operation.

[0089] The urban surveillance system may also include a data anonymization unit. This unit can anonymize the collected data and protect privacy. For example, it can remove personally identifiable information from video footage captured by surveillance cameras. It can also remove personally identifiable information from audio data, for instance. Furthermore, it can remove personally identifiable information from location data. In this way, the data anonymization unit can anonymize the collected data and protect privacy.

[0090] The urban monitoring system can further estimate the user's emotions and customize notification content based on those emotions. For example, if the user is feeling anxious, the notification content can be concise and clear. If the user is relaxed, the notification can include detailed information. If the user is excited, urgent information can be prioritized. By customizing notification content based on the user's emotions, more effective information can be delivered. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI customize the notification content.

[0091] The urban surveillance system can further estimate the user's emotions and adjust the placement of surveillance cameras based on those emotions. For example, if the user is feeling anxious, additional cameras can be added to expand the surveillance area. If the user is relaxed, surveillance can be carried out with the normal placement. If the user is excited, surveillance cameras can be concentrated in a specific area. This allows for more effective surveillance by adjusting the placement of surveillance cameras based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the surveillance unit may be performed using generative AI, or not. For example, the surveillance unit can input user emotion data into the generative AI and have the generative AI adjust the placement of surveillance cameras.

[0092] The urban surveillance system can further estimate the user's emotions and adjust the resolution of the surveillance cameras based on the estimated emotions. For example, if the user is feeling anxious, the resolution of the surveillance cameras can be set higher to provide detailed images. If the user is relaxed, surveillance can be performed at a normal resolution. Also, if the user is excited, high-resolution images can be provided for specific areas. This allows for more effective surveillance by adjusting the resolution of the surveillance cameras based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the surveillance unit may be performed using, for example, generative AI, or not using generative AI. For example, the surveillance unit can input user emotion data into the generative AI and have the generative AI adjust the resolution of the surveillance cameras.

[0093] The urban surveillance system can further estimate the user's emotions and adjust the recording time of surveillance cameras based on the estimated emotions. For example, if the user is feeling anxious, the recording time of the surveillance cameras can be extended. If the user is relaxed, surveillance can be performed with the normal recording time. Also, if the user is excited, the recording time can be extended for a specific area. This allows for more effective surveillance by adjusting the recording time of surveillance cameras based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the surveillance unit may be performed using, for example, generative AI, or not using generative AI. For example, the surveillance unit can input user emotion data into the generative AI and have the generative AI adjust the recording time of the surveillance cameras.

[0094] The urban surveillance system can further estimate the user's emotions and adjust the alert volume of the surveillance cameras based on the estimated emotions. For example, if the user is feeling anxious, the alert volume can be set higher to prompt immediate action. If the user is relaxed, the alert can be set at a normal volume. Also, if the user is excited, the alert volume can be set higher for a specific area. This allows for a more appropriate response by adjusting the alert volume based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the surveillance unit may be performed using, for example, generative AI, or not using generative AI. For example, the surveillance unit can input user emotion data into the generative AI and have the generative AI adjust the alert volume.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The monitoring unit is equipped with AI cameras and sensors for monitoring urban areas. The monitoring unit can analyze video in real time using the AI ​​cameras to detect suspicious movements, group behavior, traffic accidents, etc. For example, the AI ​​cameras can detect abnormal behavior or specific patterns of movement in a particular area. Step 2: The transmitting unit sends abnormal situations and human behavior detected by the monitoring unit to the management center in real time. The transmitting unit can transmit the detected information in real time, down to the second or millisecond. Step 3: The control unit controls IoT devices and robots based on the information transmitted by the transmitter. The control unit can control IoT devices and robots based on instructions from the management center.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the monitoring unit, transmission unit, and control unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the urban area using the camera 42 and sensors of the smart device 14 and detects abnormal situations and human behavior. The transmission unit transmits the detected information to the management center in real time via the communication I / F 44 of the smart device 14. The control unit is implemented by the specific processing unit 290 of the data processing unit 12 and controls IoT devices and robots based on the information transmitted from the transmission unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the monitoring unit, transmission unit, and control unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors urban areas using the camera 42 and sensors of the smart glasses 214 and detects abnormal situations and human behavior. The transmission unit transmits the detected information to the management center in real time via the communication I / F 44 of the smart glasses 214. The control unit is implemented by the specific processing unit 290 of the data processing unit 12 and controls IoT devices and robots based on the information transmitted from the transmission unit. The correspondence between each unit and the devices and control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the monitoring unit, transmission unit, and control unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the urban area using the camera 42 and sensors of the headset terminal 314 and detects abnormal situations and human behavior. The transmission unit transmits the detected information to the management center in real time via the communication I / F 44 of the headset terminal 314. The control unit is implemented by the specific processing unit 290 of the data processing unit 12 and controls IoT devices and robots based on the information transmitted from the transmission unit. The correspondence between each unit and the devices and control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the monitoring unit, transmission unit, and control unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the urban area using the camera 42 and sensors of the robot 414 and detects abnormal situations and human behavior. The transmission unit transmits the detected information to the management center in real time via the communication I / F 44 of the robot 414. The control unit is implemented by the specific processing unit 290 of the data processing unit 12 and controls IoT devices and robots based on the information transmitted from the transmission unit. The correspondence between each unit and the devices and control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A monitoring unit equipped with AI cameras and sensors for monitoring urban areas, A transmission unit that transmits abnormal situations and human behavior detected by the aforementioned monitoring unit to the management center in real time, The system includes a control unit that controls IoT devices and robots based on information transmitted by the aforementioned transmission unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, The system analyzes video footage in real time to detect suspicious movements, group activities, traffic accidents, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned transmitting unit The detected information is sent to the management center in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, Based on instructions from the control center, control IoT devices and robots. The system described in Appendix 1, characterized by the features described herein. (Note 5) The control unit, A robot will be dispatched to the scene, the situation will be assessed using a camera, and the police and fire department will be contacted as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the viewpoint and focus of the surveillance camera based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, To detect abnormal situations, the system learns patterns of anomalies by referring to past data, thereby improving detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, By adding audio and temperature sensors to detect abnormal situations, a multifaceted approach to anomaly detection can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the alert level of the surveillance camera based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, When detecting anomalies, geographical information is taken into consideration to determine the priority of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, For the anomalous situations detected, we refer to social media data to evaluate the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned transmitting unit It estimates the user's emotions and adjusts the priority of the information sent based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned transmitting unit The system selects the optimal transmission method for the information to be sent by referring to past transmission history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned transmitting unit We enhance the security of information transmitted by using encryption technology. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned transmitting unit It estimates the user's emotions and adjusts the format of the information sent based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned transmitting unit The destination of the information being sent is optimized by taking geographical information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned transmitting unit The system customizes the content of the information being sent by referencing data from social media. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, It estimates user emotions and adjusts the behavior of IoT devices and robots based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, The system selects the optimal control method for the IoT devices and robots being controlled by referring to their past control history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, We will enable voice-based control of IoT devices and robots using voice recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, It estimates user emotions and determines the priority of IoT devices and robots to control based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, Optimize the control range for IoT devices and robots by taking geographical information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, The system customizes the control of IoT devices and robots by referencing data from social media. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit equipped with AI cameras and sensors for monitoring urban areas, A transmission unit that transmits abnormal situations and human behavior detected by the aforementioned monitoring unit to the management center in real time, The system includes a control unit that controls IoT devices and robots based on information transmitted by the aforementioned transmission unit. A system characterized by the following features.

2. The aforementioned monitoring unit, The system analyzes video footage in real time to detect suspicious movements, group activities, traffic accidents, and more. The system according to feature 1.

3. The aforementioned transmitting unit The detected information is sent to the management center in real time. The system according to feature 1.

4. The control unit, Based on instructions from the control center, control IoT devices and robots. The system according to feature 1.

5. The control unit, A robot will be dispatched to the scene, the situation will be assessed using a camera, and the police and fire department will be contacted as needed. The system according to feature 1.

6. The aforementioned monitoring unit, The system estimates the user's emotions and adjusts the viewpoint and focus of the surveillance camera based on those estimated emotions. The system according to feature 1.

7. The aforementioned monitoring unit, To detect abnormal situations, the system learns patterns of anomalies by referring to past data, thereby improving detection accuracy. The system according to feature 1.

8. The aforementioned monitoring unit, By adding audio and temperature sensors to detect abnormal situations, a multifaceted approach to anomaly detection can be achieved. The system according to feature 1.

9. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the alert level of the surveillance camera based on the estimated user emotions. The system according to feature 1.

10. The aforementioned monitoring unit, When detecting anomalies, geographical information is taken into consideration to determine the priority of the anomalies. The system according to feature 1.

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