system

Abstract

An object of a system according to an embodiment is to analyze video data of a site, collate the video data with legal information, and provide the video data to a police officer.SOLUTION: A system includes a camera mounted with a subscriber identity module (SIM) card, a generation AI, a cloud transmission unit, a legal information collation unit, and an information providing unit. The camera mounted with the SIM card captures an image of a site. The generation AI analyzes the video transmitted from the camera. The cloud transmission unit transmits the data transmitted from the camera to the cloud. A legal information collation part collates a result analyzed by the generation AI with legal information. The information providing unit provides the police officer with the information collated by the legal information collating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of inefficiently analyzing on-site video data, comparing it with legal information, and providing it to police officers.

[0005] The system according to the embodiment aims to analyze video data from the scene, compare it with legal information, and provide it to police officers. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera equipped with a SIM card, a generation AI, a cloud transmission unit, a legal information matching unit, and an information providing unit. The camera equipped with a SIM card captures video of the scene. The generation AI analyzes the video data transmitted from the camera. The cloud transmission unit transmits the data transmitted from the camera to the cloud. The legal information matching unit matches the results of the analysis by the generation AI with legal information. The information providing unit provides the information matched by the legal information matching unit to police officers. [Effects of the Invention]

[0007] The system according to the embodiment can analyze video data from the scene, compare it with legal information, and provide it to police officers. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The police officer support system according to an embodiment of the present invention uses a camera equipped with a SIM card to analyze video footage, and a generating AI combines the footage with legal information to provide police officers with information. This makes the police officer support system more efficient and supports police officers in making quick and accurate decisions on the scene.

[0029] A police officer support system according to an embodiment includes a camera equipped with a SIM card, a generation AI, a cloud transmission unit, a legal information collating unit, and an information providing unit. The camera equipped with a SIM card captures video of the scene in real time. For example, it can capture video of a traffic accident scene or a crime scene. The camera equipped with a SIM card is small enough to be carried by a police officer and can record the situation at the scene in detail. The generation AI analyzes the video data transmitted from the camera. For example, the generation AI can perform object recognition, facial recognition, and motion analysis. The generation AI analyzes the video data using a text generation AI (e.g., LLM) or a multimodal generation AI. The cloud transmission unit transmits the data transmitted from the camera to the cloud. For example, the cloud transmission unit can encrypt the data and transmit it securely to the cloud. The legal information collating unit collates the results of the analysis by the generation AI with legal information. For example, in the case of a traffic accident, it determines responsibility for the accident based on traffic laws. The information providing unit provides the information collated by the legal information collating unit to police officers. For example, at the scene of a traffic accident, it provides information indicating the cause of the accident and the location of responsibility. As a result, the police officer support system according to the embodiment can improve the efficiency of police officers' work and support their quick and accurate decision-making at the scene. For example, at the scene of a traffic accident, the cause of the accident and the location of responsibility can be quickly identified, and appropriate measures can be taken. Also, at the scene of a crime, the characteristics and behavior of the perpetrator can be quickly identified, and the direction of the investigation can be clarified.

[0030] A camera equipped with a SIM card can simultaneously acquire environmental information (temperature, humidity, and sound) and integrate it with video data. A camera equipped with a SIM card can, for example, use a temperature sensor built into the camera to acquire the environmental temperature at the time of shooting in real time. For example, temperature data at the time of the accident can be recorded along with video of a traffic accident scene to help with analysis. A humidity sensor built into the camera can also acquire the environmental humidity at the time of shooting in real time. For example, humidity data at the scene can be recorded along with video of a crime scene to help with analysis. A sound sensor built into the camera can also acquire the environmental sound at the time of shooting in real time. For example, sound data at the time of the accident can be recorded along with video of the traffic accident scene to help with analysis. This integration of video data and environmental information improves the accuracy of analysis.

[0031] Cameras equipped with SIM cards can perform noise removal and image quality improvement processes, improving the accuracy of video data analysis. Cameras equipped with SIM cards, for example, are equipped with a real-time noise removal function that automatically removes noise that occurs during recording. For example, this makes video footage of nighttime traffic accidents clearer. Cameras also have a real-time image quality improvement function that automatically improves the image quality of the video being recorded. For example, it converts low-resolution video to high-resolution video. Cameras also have a color correction function that automatically corrects the color of the video being recorded. For example, it brightens video footage in dark places. This noise removal and image quality improvement improves analysis accuracy.

[0032] A camera equipped with a SIM card can be mounted on a drone to capture wide-area footage. A camera equipped with a SIM card can be mounted on a drone, for example, to capture aerial footage of a traffic accident scene. This can be used, for example, to understand the overall picture of an accident involving multiple vehicles. A camera can also be mounted on a drone to capture aerial footage of a crime scene. This can be used, for example, to understand the situation at a wide-area crime scene. A camera can also be mounted on a drone to capture aerial footage of a disaster scene. This can be used, for example, to understand the situation at a wide-area disaster site. This allows the drone to automatically capture wide-area footage.

[0033] A camera equipped with a SIM card can add a GPS function and add geographic information of the shooting location to the video data. A camera equipped with a SIM card can, for example, be equipped with a GPS function and add latitude and longitude information of the shooting location to the video data. For example, it can record the exact location information of a traffic accident scene. A camera equipped with a GPS function can also add map information of the shooting location to the video data. For example, it can record the exact location information of a crime scene. A camera equipped with a GPS function can also add altitude information of the shooting location to the video data. For example, it can record the exact location information of a disaster scene. In this way, the GPS function can add geographic information of the shooting location to the video data.

[0034] When analyzing video data, the generation AI can improve the accuracy of the analysis results by referring to a database of similar past incidents. The generation AI, for example, refers to a database of past traffic accidents to analyze current accident footage. For example, it identifies the cause based on past accident patterns. The generation AI also refers to a database of past crimes to analyze current crime footage. For example, it identifies the characteristics of the perpetrator based on past crime patterns. The generation AI also refers to a database of past disasters to analyze current disaster footage. For example, it identifies the damage situation based on past disaster patterns. In this way, by referring to a database of similar past incidents, the accuracy of the analysis results is improved.

[0035] The generation AI analyzes audio data along with video data and can identify detailed situations based on the correlation between video and audio. For example, the generation AI analyzes audio data along with video data to identify detailed situations of a traffic accident. For example, it analyzes collision sounds and braking sounds to identify the moment of the accident. The generation AI also analyzes audio data along with video data to identify detailed situations of a crime. For example, it analyzes the voice of the perpetrator and background sounds to identify the moment of the crime. The generation AI also analyzes audio data along with video data to identify detailed situations of a disaster. For example, it analyzes the sound of an earthquake or the sound of a building collapsing to identify the moment of damage. In this way, detailed situations can be identified by analyzing the correlation between video and audio.

[0036] Generative AI reconstructs the results of video data analysis as a 3D model, allowing for a three-dimensional understanding of the situation at the scene. For example, generative AI analyzes video of a traffic accident scene and reconstructs it as a 3D model. This can be used, for example, to understand the overall picture of the accident in three dimensions. Generative AI also analyzes video of a crime scene and reconstructs it as a 3D model. This can be used, for example, to understand the overall picture of the crime in three dimensions. Generative AI also analyzes video of a disaster scene and reconstructs it as a 3D model. This can be used, for example, to understand the overall picture of the damage in three dimensions. This allows for a three-dimensional understanding of the situation at the scene using the 3D model.

[0037] When collating legal information, the generation AI automatically incorporates the latest legal amendment information, allowing it to provide the latest legal information. For example, the generation AI automatically incorporates legal amendment information and reflects it in the analysis results of traffic accidents. For example, it determines responsibility for an accident based on the latest traffic laws. The generation AI also automatically incorporates legal amendment information and reflects it in the analysis results of crimes. For example, it evaluates the details of a crime based on the latest criminal code. The generation AI also automatically incorporates legal amendment information and reflects it in the analysis results of disasters. For example, it evaluates the damage situation based on the latest disaster prevention law. In this way, by automatically incorporating the latest legal amendment information, it is possible to always provide the latest legal information.

[0038] The generation AI can compare the results of legal information matching with a database of past precedents and provide specific legal advice. For example, the generation AI can compare the analysis results of a traffic accident with a database of past precedents and provide specific legal advice. For example, it can determine responsibility for the accident based on similar precedents. The generation AI can also compare the analysis results of a crime with a database of past precedents and provide specific legal advice. For example, it can evaluate the details of the crime based on similar precedents. The generation AI can also compare the analysis results of a disaster with a database of past precedents and provide specific legal advice. For example, it can evaluate the damage situation based on similar precedents. This makes it possible to provide specific legal advice by comparing with a database of past precedents.

[0039] Generative AI can compare the results of legal information matching with other jurisdictions to provide a broad legal perspective. For example, generative AI can compare the results of traffic accident analysis with international law to provide a broad legal perspective. For example, it can determine responsibility for the accident based on international traffic laws. Generative AI can also compare the results of crime analysis with state law to provide a broad legal perspective. For example, it can evaluate the nature of the crime based on state law. Generative AI can also compare the results of disaster analysis with other jurisdictions to provide a broad legal perspective. For example, it can evaluate the damage situation based on the disaster response laws of other jurisdictions. This allows it to provide a broad legal perspective by comparing with other jurisdictions.

[0040] The generation AI can provide the results of legal information matching in visually easy-to-understand infographics, thereby facilitating police officers' understanding. For example, the generation AI can provide the analysis results of traffic accidents as infographics to facilitate police officers' understanding. For example, it can visually display the cause of the accident and who is responsible. The generation AI can also provide the analysis results of crimes as infographics to facilitate police officers' understanding. For example, it can visually display the details of the crime and the characteristics of the perpetrator. The generation AI can also provide the analysis results of disasters as infographics to facilitate police officers' understanding. For example, it can visually display the damage situation and countermeasures. In this way, the infographics can facilitate police officers' understanding.

[0041] The information provision unit can convey information provided by the generation AI to police officers via the voice assistant. For example, the information provision unit conveys the analysis results of a traffic accident from the generation AI to police officers in real time via the voice assistant. For example, the cause of the accident and who is responsible are provided via voice. The information provision unit also conveys the analysis results of a crime from the generation AI to police officers in real time via the voice assistant. For example, the details of the crime and the characteristics of the perpetrator are provided via voice. The information provision unit also conveys the analysis results of a disaster from the generation AI to police officers in real time via the voice assistant. For example, the damage situation and countermeasures are provided via voice. This allows information to be conveyed to police officers in real time via the voice assistant.

[0042] The information provision unit can compare the information provided by the generation AI with the police officer's past action history and provide customized advice. For example, the generation AI compares the analysis results of a traffic accident with the police officer's past action history and provides customized advice. For example, it proposes the optimal response method based on past response experience. The information provision unit also compares the analysis results of a crime with the police officer's past action history and provides customized advice. For example, it proposes the optimal response method based on past response experience. The information provision unit also compares the analysis results of a disaster with the police officer's past action history and provides customized advice. For example, it proposes the optimal response method based on past response experience. This makes it possible to provide individually customized advice based on the police officer's past action history.

[0043] The information provision unit can visually display the information provided by the generation AI using AR technology to support decision-making at the scene. For example, the information provision unit uses AR technology to visually display the analysis results of a traffic accident by the generation AI, supporting police officers in making quick decisions. For example, the cause of the accident and who is responsible are displayed using AR. The information provision unit also uses AR technology to visually display the analysis results of a crime by the generation AI, supporting police officers in making quick decisions. For example, the details of the crime and the characteristics of the perpetrator are displayed using AR. The information provision unit also uses AR technology to visually display the analysis results of a disaster by the generation AI, supporting police officers in making quick decisions. For example, the damage situation and countermeasures are displayed using AR. In this way, AR technology can support quick decision-making at the scene.

[0044] When storing data, the Generating AI can use blockchain technology to prevent data tampering and ensure reliability. For example, the Generating AI can store the analysis results of traffic accidents using blockchain technology, ensuring data tampering prevention and reliability. For example, the cause of the accident and the location of responsibility can be recorded on the blockchain. The Generating AI can also store the analysis results of crimes using blockchain technology, ensuring data tampering prevention and reliability. For example, the details of the crime and the characteristics of the perpetrator can be recorded on the blockchain. The Generating AI can also store the analysis results of disasters using blockchain technology, ensuring data tampering prevention and reliability. For example, the damage situation and countermeasures can be recorded on the blockchain. In this way, blockchain technology can prevent data tampering and ensure reliability.

[0045] The generation AI can manage the stored data using an AI-based automatic tagging function. For example, the generation AI automatically tags the analysis results of traffic accidents to efficiently manage data. For example, it attaches tags related to the cause of the accident and who is responsible. The generation AI also automatically tags the analysis results of crimes to efficiently manage data. For example, it attaches tags related to the details of the crime and the characteristics of the perpetrator. The generation AI also automatically tags the analysis results of disasters to efficiently manage data. For example, it attaches tags related to the damage situation and countermeasures. This allows the automatic tagging function to efficiently manage stored data.

[0046] Generative AI can share the stored data in collaboration with other investigative agencies and international organizations, thereby realizing investigative cooperation. For example, generative AI can share the analysis results of traffic accidents with other investigative agencies and international organizations, thereby realizing global investigative cooperation. For example, it can access an international traffic accident database. Generative AI can also share the analysis results of crimes with other investigative agencies and international organizations, thereby realizing global investigative cooperation. For example, it can access an international crime database. Generative AI can also share the analysis results of disasters with other investigative agencies and international organizations, thereby realizing global investigative cooperation. For example, it can access an international disaster database. This allows it to collaborate with other investigative agencies and international organizations and share data, thereby realizing global investigative cooperation.

[0047] Generative AI can incorporate automatic anonymization technology when sharing data. For example, when sharing the analysis results of a traffic accident, generative AI incorporates automatic anonymization technology to protect privacy. For example, personal information is automatically anonymized. Generative AI also incorporates automatic anonymization technology to protect privacy when sharing the analysis results of a crime. For example, the personal information of the perpetrator is automatically anonymized. Generative AI also incorporates automatic anonymization technology to protect privacy when sharing the analysis results of a disaster. For example, the personal information of victims is automatically anonymized. In this way, automatic anonymization technology allows data to be shared while protecting privacy.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The police officer support system also has a voice recognition unit that can analyze the voice instructions of police officers at the scene and provide appropriate information. For example, if a police officer gives a voice command such as "Tell me the cause of the accident," the voice recognition unit analyzes the command and provides information on the cause of the accident analyzed by the generation AI. Also, if a police officer gives a voice command such as "Tell me the characteristics of the perpetrator," the voice recognition unit analyzes the command and provides information on the characteristics of the perpetrator analyzed by the generation AI. Furthermore, if a police officer gives a voice command such as "Tell me the situation at the scene," the voice recognition unit analyzes the command and provides information on the situation at the scene analyzed by the generation AI. This allows the voice recognition unit to quickly provide information based on the police officer's voice commands.

[0050] The police officer support system also has an emergency notification unit that can automatically detect emergencies at the scene and quickly report them. For example, if a large-scale collision is detected at the scene of a traffic accident, the emergency notification unit will automatically make an emergency call and dispatch an ambulance or fire engine. Similarly, if gunshots or screams are detected at a crime scene, the emergency notification unit will automatically notify police headquarters and request reinforcements. Furthermore, if a building collapse or fire is detected at a disaster site, the emergency notification unit will automatically make an emergency call and dispatch a rescue team. This allows the emergency notification unit to respond quickly to emergencies at the scene.

[0051] The police officer support system can also be equipped with a translation unit to support multilingual support on-site. For example, when communicating with foreign drivers at the scene of a traffic accident, the translation unit translates audio in real time to support communication between the police officer and the driver. When communicating with foreign victims or witnesses at a crime scene, the translation unit translates audio in real time to support communication between the police officer and the victim or witness. Furthermore, when communicating with foreign victims at a disaster scene, the translation unit translates audio in real time to support communication between the police officer and the victim. In this way, the translation unit enables multilingual support on-site.

[0052] The police officer support system further includes a data analysis unit that can analyze data collected at the scene in real time and provide statistical information to police officers. For example, data from a traffic accident scene can be analyzed and compared with past accident data to provide statistical information. Data from a crime scene can also be analyzed and compared with past crime data to provide statistical information. Data from a disaster scene can also be analyzed and compared with past disaster data to provide statistical information. In this way, the data analysis unit can analyze data from the scene in real time and provide useful statistical information to police officers.

[0053] The police officer support system further includes a prediction unit that can predict future risks based on on-site data and issue warnings to police officers. For example, the system analyzes data from traffic accident scenes to predict future accident risks and issue warnings. It also analyzes data from crime scenes to predict future crime risks and issue warnings. It also analyzes data from disaster scenes to predict future disaster risks and issue warnings. This allows the prediction unit to predict future risks based on on-site data and issue appropriate warnings to police officers.

[0054] The police officer support system further includes an education department, which can educate police officers on how to respond at the scene. For example, it can simulate how to respond at the scene of a traffic accident and provide education to police officers. It can also simulate how to respond at a crime scene and provide education to police officers. It can also simulate how to respond at a disaster scene and provide education to police officers. In this way, the education department can educate police officers on how to respond at the scene and improve their skills.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The camera equipped with a SIM card captures real-time footage of the scene. For example, it can capture footage of a traffic accident or crime scene. The camera equipped with a SIM card is small enough for police officers to carry and can record the situation at the scene in detail. Step 2: The generative AI analyzes the video data sent from the camera. For example, the generative AI can perform object recognition, facial recognition, and motion analysis. The generative AI analyzes the video data using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The cloud transmission unit transmits the data transmitted from the camera to the cloud. For example, the cloud transmission unit can encrypt the data and transmit it securely to the cloud. Step 4: The legal information matching unit matches the results of the analysis by the generation AI with legal information. For example, in the case of a traffic accident, it determines responsibility for the accident based on traffic laws. Step 5: The information provision unit provides police officers with the information collated by the legal information collation unit. For example, at the scene of a traffic accident, information indicating the cause of the accident and who is responsible is provided. This makes police officers' work more efficient and supports their quick and accurate judgments at the scene.

[0057] (Example 2) The police officer support system according to an embodiment of the present invention uses a camera equipped with a SIM card to analyze video footage, and a generating AI combines the footage with legal information to provide police officers with information. This makes the police officer support system more efficient and supports police officers in making quick and accurate decisions on the scene.

[0058] A police officer support system according to an embodiment includes a camera equipped with a SIM card, a generation AI, a cloud transmission unit, a legal information collating unit, and an information providing unit. The camera equipped with a SIM card captures video of the scene in real time. For example, it can capture video of a traffic accident scene or a crime scene. The camera equipped with a SIM card is small enough to be carried by a police officer and can record the situation at the scene in detail. The generation AI analyzes the video data transmitted from the camera. For example, the generation AI can perform object recognition, facial recognition, and motion analysis. The generation AI analyzes the video data using a text generation AI (e.g., LLM) or a multimodal generation AI. The cloud transmission unit transmits the data transmitted from the camera to the cloud. For example, the cloud transmission unit can encrypt the data and transmit it securely to the cloud. The legal information collating unit collates the results of the analysis by the generation AI with legal information. For example, in the case of a traffic accident, it determines responsibility for the accident based on traffic laws. The information providing unit provides the information collated by the legal information collating unit to police officers. For example, at the scene of a traffic accident, it provides information indicating the cause of the accident and the location of responsibility. As a result, the police officer support system according to the embodiment can improve the efficiency of police officers' work and support their quick and accurate decision-making at the scene. For example, at the scene of a traffic accident, the cause of the accident and the location of responsibility can be quickly identified, and appropriate measures can be taken. Also, at the scene of a crime, the characteristics and behavior of the perpetrator can be quickly identified, and the direction of the investigation can be clarified.

[0059] A camera equipped with a SIM card can simultaneously acquire environmental information (temperature, humidity, and sound) and integrate it with video data. A camera equipped with a SIM card can, for example, use a temperature sensor built into the camera to acquire the environmental temperature at the time of shooting in real time. For example, temperature data at the time of the accident can be recorded along with video of a traffic accident scene to help with analysis. A humidity sensor built into the camera can also acquire the environmental humidity at the time of shooting in real time. For example, humidity data at the scene can be recorded along with video of a crime scene to help with analysis. A sound sensor built into the camera can also acquire the environmental sound at the time of shooting in real time. For example, sound data at the time of the accident can be recorded along with video of the traffic accident scene to help with analysis. This integration of video data and environmental information improves the accuracy of analysis.

[0060] Cameras equipped with SIM cards can perform noise removal and image quality improvement processes, improving the accuracy of video data analysis. Cameras equipped with SIM cards, for example, are equipped with a real-time noise removal function that automatically removes noise that occurs during recording. For example, this makes video footage of nighttime traffic accidents clearer. Cameras also have a real-time image quality improvement function that automatically improves the image quality of the video being recorded. For example, it converts low-resolution video to high-resolution video. Cameras also have a color correction function that automatically corrects the color of the video being recorded. For example, it brightens video footage in dark places. This noise removal and image quality improvement improves analysis accuracy.

[0061] A camera equipped with a SIM card is equipped with an emotion estimation function, which can estimate the emotional state of a person being photographed and record it as additional information in the video data. For example, a camera equipped with a SIM card incorporates an emotion estimation algorithm and analyzes the facial expressions of the person being photographed to estimate the emotional state. For example, it records the emotional state of a driver at the scene of a traffic accident. The camera also uses voice analysis technology to analyze the tone and speed of the person's voice to estimate the emotional state. For example, it records the emotional state of a criminal at a crime scene. The camera also collects biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotional state using an emotion estimation algorithm. For example, it records the emotional state based on heart rate fluctuations. This allows the emotion estimation function to add additional information to the video data.

[0062] A camera equipped with a SIM card can be mounted on a drone to capture wide-area footage. A camera equipped with a SIM card can be mounted on a drone, for example, to capture aerial footage of a traffic accident scene. This can be used, for example, to understand the overall picture of an accident involving multiple vehicles. A camera can also be mounted on a drone to capture aerial footage of a crime scene. This can be used, for example, to understand the situation at a wide-area crime scene. A camera can also be mounted on a drone to capture aerial footage of a disaster scene. This can be used, for example, to understand the situation at a wide-area disaster site. This allows the drone to automatically capture wide-area footage.

[0063] A camera equipped with a SIM card can add a GPS function and add geographic information of the shooting location to the video data. A camera equipped with a SIM card can, for example, be equipped with a GPS function and add latitude and longitude information of the shooting location to the video data. For example, it can record the exact location information of a traffic accident scene. A camera equipped with a GPS function can also add map information of the shooting location to the video data. For example, it can record the exact location information of a crime scene. A camera equipped with a GPS function can also add altitude information of the shooting location to the video data. For example, it can record the exact location information of a disaster scene. In this way, the GPS function can add geographic information of the shooting location to the video data.

[0064] A camera equipped with a SIM card can use an emotion estimation function to monitor the emotional state of a police officer while filming and detect stress or fatigue. A camera equipped with a SIM card, for example, can analyze a police officer's facial expression to estimate their stress level. For example, it can record the fatigue state of a police officer during a long patrol. The camera can also use voice analysis technology to analyze the tone and speed of a police officer's voice to estimate their stress level. For example, it can record the stress state of a police officer responding to an emergency. The camera can also collect biometric data (heart rate and electrodermal activity) using sensors and analyze the stress level using an emotion estimation algorithm. For example, it can record the stress state based on fluctuations in heart rate. This allows the emotion estimation function to detect the stress or fatigue of a police officer.

[0065] When analyzing video data, the generation AI can improve the accuracy of the analysis results by referring to a database of similar past incidents. The generation AI, for example, refers to a database of past traffic accidents to analyze current accident footage. For example, it identifies the cause based on past accident patterns. The generation AI also refers to a database of past crimes to analyze current crime footage. For example, it identifies the characteristics of the perpetrator based on past crime patterns. The generation AI also refers to a database of past disasters to analyze current disaster footage. For example, it identifies the damage situation based on past disaster patterns. In this way, by referring to a database of similar past incidents, the accuracy of the analysis results is improved.

[0066] The generation AI analyzes audio data along with video data and can identify detailed situations based on the correlation between video and audio. For example, the generation AI analyzes audio data along with video data to identify detailed situations of a traffic accident. For example, it analyzes collision sounds and braking sounds to identify the moment of the accident. The generation AI also analyzes audio data along with video data to identify detailed situations of a crime. For example, it analyzes the voice of the perpetrator and background sounds to identify the moment of the crime. The generation AI also analyzes audio data along with video data to identify detailed situations of a disaster. For example, it analyzes the sound of an earthquake or the sound of a building collapsing to identify the moment of damage. In this way, detailed situations can be identified by analyzing the correlation between video and audio.

[0067] Using its emotion estimation function, the generative AI can analyze changes in the emotions of people in video data and detect abnormal behavior. For example, the generative AI analyzes the facial expressions of people in video to detect changes in emotions. For example, it can quickly identify tension or anxiety in a criminal at a crime scene. The generative AI can also analyze the tone and speed of a person's voice in video to detect changes in emotions. For example, it can quickly identify a driver's agitation at the scene of a traffic accident. The generative AI can also analyze the biometric data (heart rate and electrodermal activity) of people in video to detect changes in emotions. For example, it can quickly identify fear in victims at a disaster site. This allows the emotion estimation function to detect abnormal behavior early on.

[0068] Generative AI reconstructs the results of video data analysis as a 3D model, allowing for a three-dimensional understanding of the situation at the scene. For example, generative AI analyzes video of a traffic accident scene and reconstructs it as a 3D model. This can be used, for example, to understand the overall picture of the accident in three dimensions. Generative AI also analyzes video of a crime scene and reconstructs it as a 3D model. This can be used, for example, to understand the overall picture of the crime in three dimensions. Generative AI also analyzes video of a disaster scene and reconstructs it as a 3D model. This can be used, for example, to understand the overall picture of the damage in three dimensions. This allows for a three-dimensional understanding of the situation at the scene using the 3D model.

[0069] The generation AI can use its emotion estimation function to improve the accuracy of analysis by feeding back the emotional reactions of police officers based on the analysis results. For example, the generation AI can improve analysis accuracy by feeding back the emotional reactions of police officers based on the analysis results. For example, it can adjust the analysis algorithm based on the emotional data of police officers. The generation AI can also analyze the emotional reactions of police officers in real time and provide feedback. For example, it can analyze the facial expressions and tone of voice of police officers and provide feedback on their emotional reactions. The generation AI can also regularly evaluate the emotional reactions of police officers to improve analysis accuracy. For example, it can regularly collect emotional data of police officers and improve the analysis algorithm. In this way, analysis accuracy can be continuously improved by feeding back the emotional reactions of police officers.

[0070] When collating legal information, the generation AI automatically incorporates the latest legal amendment information, allowing it to provide the latest legal information. For example, the generation AI automatically incorporates legal amendment information and reflects it in the analysis results of traffic accidents. For example, it determines responsibility for an accident based on the latest traffic laws. The generation AI also automatically incorporates legal amendment information and reflects it in the analysis results of crimes. For example, it evaluates the details of a crime based on the latest criminal code. The generation AI also automatically incorporates legal amendment information and reflects it in the analysis results of disasters. For example, it evaluates the damage situation based on the latest disaster prevention law. In this way, by automatically incorporating the latest legal amendment information, it is possible to always provide the latest legal information.

[0071] The generation AI can compare the results of legal information matching with a database of past precedents and provide specific legal advice. For example, the generation AI can compare the analysis results of a traffic accident with a database of past precedents and provide specific legal advice. For example, it can determine responsibility for the accident based on similar precedents. The generation AI can also compare the analysis results of a crime with a database of past precedents and provide specific legal advice. For example, it can evaluate the details of the crime based on similar precedents. The generation AI can also compare the analysis results of a disaster with a database of past precedents and provide specific legal advice. For example, it can evaluate the damage situation based on similar precedents. This makes it possible to provide specific legal advice by comparing with a database of past precedents.

[0072] The generation AI can use its emotion estimation function to analyze the emotional reactions of police officers to the results of legal information matching and provide information in an easy-to-understand format. The generation AI, for example, analyzes the emotional reactions of police officers to the results of legal information matching and provides information in an easy-to-understand format. For example, it adjusts the way information is presented based on the emotional data of police officers. The generation AI also analyzes the emotional reactions of police officers in real time and provides information. For example, it analyzes the facial expressions and tone of voice of police officers and provides information based on their emotional reactions. The generation AI also regularly evaluates the emotional reactions of police officers and improves the way information is provided. For example, it regularly collects emotional data of police officers and adjusts the way information is provided. In this way, by analyzing the emotional reactions of police officers, it is possible to provide information in an easy-to-understand format.

[0073] Generative AI can compare the results of legal information matching with other jurisdictions to provide a broad legal perspective. For example, generative AI can compare the results of traffic accident analysis with international law to provide a broad legal perspective. For example, it can determine responsibility for the accident based on international traffic laws. Generative AI can also compare the results of crime analysis with state law to provide a broad legal perspective. For example, it can evaluate the nature of the crime based on state law. Generative AI can also compare the results of disaster analysis with other jurisdictions to provide a broad legal perspective. For example, it can evaluate the damage situation based on the disaster response laws of other jurisdictions. This allows it to provide a broad legal perspective by comparing with other jurisdictions.

[0074] The generation AI can provide the results of legal information matching in visually easy-to-understand infographics, thereby facilitating police officers' understanding. For example, the generation AI can provide the analysis results of traffic accidents as infographics to facilitate police officers' understanding. For example, it can visually display the cause of the accident and who is responsible. The generation AI can also provide the analysis results of crimes as infographics to facilitate police officers' understanding. For example, it can visually display the details of the crime and the characteristics of the perpetrator. The generation AI can also provide the analysis results of disasters as infographics to facilitate police officers' understanding. For example, it can visually display the damage situation and countermeasures. In this way, the infographics can facilitate police officers' understanding.

[0075] The generative AI uses its emotion estimation function to predict citizens' emotional reactions to the matching results, thereby supporting police officers in taking appropriate action. For example, the generative AI can predict citizens' emotional reactions to the matching results of legal information, thereby supporting police officers in taking appropriate action. For example, it can suggest a response method based on citizens' emotional data. The generative AI can also analyze citizens' emotional reactions in real time and provide information to police officers. For example, it can analyze citizens' facial expressions and tone of voice and suggest a response method based on their emotional reaction. The generative AI can also regularly evaluate citizens' emotional reactions and improve police officers' response methods. For example, it can regularly collect citizens' emotional data and adjust response methods. In this way, it can predict citizens' emotional reactions and support police officers in taking appropriate action.

[0076] The information provision unit can convey information provided by the generation AI to police officers via the voice assistant. For example, the information provision unit conveys the analysis results of a traffic accident from the generation AI to police officers in real time via the voice assistant. For example, the cause of the accident and who is responsible are provided via voice. The information provision unit also conveys the analysis results of a crime from the generation AI to police officers in real time via the voice assistant. For example, the details of the crime and the characteristics of the perpetrator are provided via voice. The information provision unit also conveys the analysis results of a disaster from the generation AI to police officers in real time via the voice assistant. For example, the damage situation and countermeasures are provided via voice. This allows information to be conveyed to police officers in real time via the voice assistant.

[0077] The information provision unit can compare the information provided by the generation AI with the police officer's past action history and provide customized advice. For example, the generation AI compares the analysis results of a traffic accident with the police officer's past action history and provides customized advice. For example, it proposes the optimal response method based on past response experience. The information provision unit also compares the analysis results of a crime with the police officer's past action history and provides customized advice. For example, it proposes the optimal response method based on past response experience. The information provision unit also compares the analysis results of a disaster with the police officer's past action history and provides customized advice. For example, it proposes the optimal response method based on past response experience. This makes it possible to provide individually customized advice based on the police officer's past action history.

[0078] The information provision unit allows the generation AI to use the emotion estimation function to adjust the timing or method of providing information according to the emotional state of the police officer. For example, the information provision unit adjusts the timing of providing information by having the generation AI analyze the emotional state of the police officer in real time. For example, the information provision unit delays the provision of information if the police officer's stress level is high. The information provision unit also adjusts the method of providing information by having the generation AI analyze the emotional state of the police officer in real time. For example, the information provision unit selects a method such as audio notification, text message, or visual alert depending on the emotional state of the police officer. The information provision unit also allows the generation AI to regularly evaluate the emotional state of the police officer and improve the timing and method of providing information. For example, the information provision unit regularly collects emotional data of the police officer and adjusts the method of providing information. This makes it possible to adjust the timing and method of providing information according to the emotional state of the police officer.

[0079] The information provision unit can visually display the information provided by the generation AI using AR technology to support decision-making at the scene. For example, the information provision unit uses AR technology to visually display the analysis results of a traffic accident by the generation AI, supporting police officers in making quick decisions. For example, the cause of the accident and who is responsible are displayed using AR. The information provision unit also uses AR technology to visually display the analysis results of a crime by the generation AI, supporting police officers in making quick decisions. For example, the details of the crime and the characteristics of the perpetrator are displayed using AR. The information provision unit also uses AR technology to visually display the analysis results of a disaster by the generation AI, supporting police officers in making quick decisions. For example, the damage situation and countermeasures are displayed using AR. In this way, AR technology can support quick decision-making at the scene.

[0080] In the information provision unit, the generation AI uses the emotion estimation function to monitor the stress level of the police officer when providing information and can suggest appropriate breaks or support. In the information provision unit, for example, the generation AI monitors the stress level of the police officer in real time and suggests appropriate breaks. For example, encouraging a break if the stress level is high. In addition, in the information provision unit, the generation AI monitors the stress level of the police officer in real time and suggests appropriate support. For example, providing mental health support if the stress level is high. In addition, in the information provision unit, the generation AI regularly evaluates the stress level of the police officer and improves break and support methods. For example, the generation AI regularly collects stress data of the police officer and adjusts break and support methods. This makes it possible to monitor the stress level of the police officer and suggest appropriate breaks and support.

[0081] When storing data, the Generating AI can use blockchain technology to prevent data tampering and ensure reliability. For example, the Generating AI can store the analysis results of traffic accidents using blockchain technology, ensuring data tampering prevention and reliability. For example, the cause of the accident and the location of responsibility can be recorded on the blockchain. The Generating AI can also store the analysis results of crimes using blockchain technology, ensuring data tampering prevention and reliability. For example, the details of the crime and the characteristics of the perpetrator can be recorded on the blockchain. The Generating AI can also store the analysis results of disasters using blockchain technology, ensuring data tampering prevention and reliability. For example, the damage situation and countermeasures can be recorded on the blockchain. In this way, blockchain technology can prevent data tampering and ensure reliability.

[0082] The generation AI can manage the stored data using an AI-based automatic tagging function. For example, the generation AI automatically tags the analysis results of traffic accidents to efficiently manage data. For example, it attaches tags related to the cause of the accident and who is responsible. The generation AI also automatically tags the analysis results of crimes to efficiently manage data. For example, it attaches tags related to the details of the crime and the characteristics of the perpetrator. The generation AI also automatically tags the analysis results of disasters to efficiently manage data. For example, it attaches tags related to the damage situation and countermeasures. This allows the automatic tagging function to efficiently manage stored data.

[0083] The generative AI can use its emotion estimation function to predict the recipient's emotional response when sharing data and select the sharing method. For example, the generative AI can predict the recipient's emotional response when sharing data and select an appropriate sharing method. For example, it can adjust the sharing method based on the recipient's emotional data. The generative AI can also analyze the recipient's emotional response in real time and select the sharing method. For example, it can analyze the recipient's facial expressions and tone of voice and adjust the sharing method based on the emotional response. The generative AI can also regularly evaluate the recipient's emotional response and improve the sharing method. For example, it can regularly collect the recipient's emotional data and adjust the sharing method. This allows it to predict the recipient's emotional response and select an appropriate sharing method.

[0084] Generative AI can share the stored data in collaboration with other investigative agencies and international organizations, thereby realizing investigative cooperation. For example, generative AI can share the analysis results of traffic accidents with other investigative agencies and international organizations, thereby realizing global investigative cooperation. For example, it can access an international traffic accident database. Generative AI can also share the analysis results of crimes with other investigative agencies and international organizations, thereby realizing global investigative cooperation. For example, it can access an international crime database. Generative AI can also share the analysis results of disasters with other investigative agencies and international organizations, thereby realizing global investigative cooperation. For example, it can access an international disaster database. This allows it to collaborate with other investigative agencies and international organizations and share data, thereby realizing global investigative cooperation.

[0085] Generative AI can incorporate automatic anonymization technology when sharing data. For example, when sharing the analysis results of a traffic accident, generative AI incorporates automatic anonymization technology to protect privacy. For example, personal information is automatically anonymized. Generative AI also incorporates automatic anonymization technology to protect privacy when sharing the analysis results of a crime. For example, the personal information of the perpetrator is automatically anonymized. Generative AI also incorporates automatic anonymization technology to protect privacy when sharing the analysis results of a disaster. For example, the personal information of victims is automatically anonymized. In this way, automatic anonymization technology allows data to be shared while protecting privacy.

[0086] The generative AI can use its emotion estimation function to analyze the receiver's emotional response to the shared data and improve the shared content. For example, the generative AI analyzes the receiver's emotional response to the shared data and uses this information to improve the shared content. For example, it adjusts the shared content based on the receiver's emotional data. The generative AI also analyzes the receiver's emotional response in real time and improves the shared content. For example, it analyzes the receiver's facial expressions and tone of voice and adjusts the shared content based on the emotional response. The generative AI also regularly evaluates the receiver's emotional response and improves the shared content. For example, it regularly collects the receiver's emotional data and adjusts the shared content. In this way, analyzing the receiver's emotional response can be used to improve the shared content.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The police officer support system also has a voice recognition unit that can analyze the voice instructions of police officers at the scene and provide appropriate information. For example, if a police officer gives a voice command such as "Tell me the cause of the accident," the voice recognition unit analyzes the command and provides information on the cause of the accident analyzed by the generation AI. Also, if a police officer gives a voice command such as "Tell me the characteristics of the perpetrator," the voice recognition unit analyzes the command and provides information on the characteristics of the perpetrator analyzed by the generation AI. Furthermore, if a police officer gives a voice command such as "Tell me the situation at the scene," the voice recognition unit analyzes the command and provides information on the situation at the scene analyzed by the generation AI. This allows the voice recognition unit to quickly provide information based on the police officer's voice commands.

[0089] The police officer support system also has an emergency notification unit that can automatically detect emergencies at the scene and quickly report them. For example, if a large-scale collision is detected at the scene of a traffic accident, the emergency notification unit will automatically make an emergency call and dispatch an ambulance or fire engine. Similarly, if gunshots or screams are detected at a crime scene, the emergency notification unit will automatically notify police headquarters and request reinforcements. Furthermore, if a building collapse or fire is detected at a disaster site, the emergency notification unit will automatically make an emergency call and dispatch a rescue team. This allows the emergency notification unit to respond quickly to emergencies at the scene.

[0090] The police officer support system can also be equipped with a translation unit to support multilingual support on-site. For example, when communicating with foreign drivers at the scene of a traffic accident, the translation unit translates audio in real time to support communication between the police officer and the driver. When communicating with foreign victims or witnesses at a crime scene, the translation unit translates audio in real time to support communication between the police officer and the victim or witness. Furthermore, when communicating with foreign victims at a disaster scene, the translation unit translates audio in real time to support communication between the police officer and the victim. In this way, the translation unit enables multilingual support on-site.

[0091] The police officer support system can also use emotion estimation to monitor the emotional state of police officers on the scene and provide appropriate support. For example, by analyzing the officer's facial expression and determining if their stress level is high, the system can provide relaxation advice. It can also analyze the tone and speed of the officer's voice and, if fatigue is detected, suggest taking a break. Furthermore, by analyzing the officer's biometric data (heart rate and electrodermal activity), the system can provide mental health support if their emotional state is unstable. This allows the emotion estimation function to provide appropriate support according to the officer's emotional state.

[0092] The police officer support system can also use its emotion estimation function to monitor the emotional state of citizens at the scene and support appropriate responses. For example, it can analyze a citizen's facial expression and, if tension or anxiety is detected, suggest a calm response to the police officer. It can also analyze the tone and speed of a citizen's voice and, if anger or excitement is detected, suggest a calm response to the police officer. It can also analyze a citizen's biometric data (heart rate and electrodermal activity) and, if their emotional state is unstable, suggest an appropriate response to the police officer. In this way, the emotion estimation function can support appropriate responses according to the citizen's emotional state.

[0093] The police officer support system can also use its emotion estimation function to monitor the criminal's emotional state at the scene and clarify the direction of the investigation. For example, it can analyze the criminal's facial expressions and, if it detects nervousness or impatience, report the criminal's emotional state to the police officer. It can also analyze the criminal's tone and speed of voice and, if it detects agitation or excitement, report the criminal's emotional state to the police officer. It can also analyze the criminal's biometric data (heart rate and electrodermal activity) and, if the criminal's emotional state is unstable, report the criminal's emotional state to the police officer. In this way, the emotion estimation function can clarify the direction of the investigation based on the criminal's emotional state.

[0094] The police officer support system can also use emotion estimation to monitor the emotional state of victims at the scene and provide appropriate support. For example, it can analyze the victim's facial expressions and, if fear or anxiety is observed, report the victim's emotional state to the police officer and suggest appropriate support. It can also analyze the victim's tone and speed of voice and, if agitation or confusion is observed, report the victim's emotional state to the police officer and suggest appropriate support. It can also analyze the victim's biometric data (heart rate and electrodermal activity) and, if the victim's emotional state is unstable, report the victim's emotional state to the police officer and suggest appropriate support. In this way, the emotion estimation function can provide appropriate support according to the victim's emotional state.

[0095] The police officer support system further includes a data analysis unit that can analyze data collected at the scene in real time and provide statistical information to police officers. For example, data from a traffic accident scene can be analyzed and compared with past accident data to provide statistical information. Data from a crime scene can also be analyzed and compared with past crime data to provide statistical information. Data from a disaster scene can also be analyzed and compared with past disaster data to provide statistical information. In this way, the data analysis unit can analyze data from the scene in real time and provide useful statistical information to police officers.

[0096] The police officer support system further includes a prediction unit that can predict future risks based on on-site data and issue warnings to police officers. For example, the system analyzes data from traffic accident scenes to predict future accident risks and issue warnings. It also analyzes data from crime scenes to predict future crime risks and issue warnings. It also analyzes data from disaster scenes to predict future disaster risks and issue warnings. This allows the prediction unit to predict future risks based on on-site data and issue appropriate warnings to police officers.

[0097] The police officer support system further includes an education department, which can educate police officers on how to respond at the scene. For example, it can simulate how to respond at the scene of a traffic accident and provide education to police officers. It can also simulate how to respond at a crime scene and provide education to police officers. It can also simulate how to respond at a disaster scene and provide education to police officers. In this way, the education department can educate police officers on how to respond at the scene and improve their skills.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The camera equipped with a SIM card captures real-time footage of the scene. For example, it can capture footage of a traffic accident or crime scene. The camera equipped with a SIM card is small enough for police officers to carry and can record the situation at the scene in detail. Step 2: The generative AI analyzes the video data sent from the camera. For example, the generative AI can perform object recognition, facial recognition, and motion analysis. The generative AI analyzes the video data using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The cloud transmission unit transmits the data transmitted from the camera to the cloud. For example, the cloud transmission unit can encrypt the data and transmit it securely to the cloud. Step 4: The legal information matching unit matches the results of the analysis by the generation AI with legal information. For example, in the case of a traffic accident, it determines responsibility for the accident based on traffic laws. Step 5: The information provision unit provides police officers with the information collated by the legal information collation unit. For example, at the scene of a traffic accident, information indicating the cause of the accident and who is responsible is provided. This makes police officers' work more efficient and supports their quick and accurate judgments at the scene.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A camera with a SIM card, Generative AI and a cloud transmission unit; a legal information collation unit; an information providing unit, The camera equipped with the SIM card is Taking video of the scene, The generated AI is Analyzing the video data transmitted from the camera; The cloud transmission unit Sending the data transmitted from the camera to a cloud; The legal information matching unit The results of the analysis by the generating AI are compared with legal information, The information providing unit The information collated by the legal information collation unit is provided to police officers. A system characterized by:

2. The camera equipped with the SIM card is Emotion estimation function is provided, and the emotional state of the person being photographed is estimated and recorded as additional information in the video data.

2. The system of claim 1.

3. The camera equipped with the SIM card is Mounted on a drone, it captures wide-area footage.

2. The system of claim 1.

4. The generated AI is When analyzing the video data, the accuracy of the analysis results is improved based on a database of past similar incidents.

2. The system of claim 1.

5. The generated AI is When collating legal information, the latest legal amendment information is automatically incorporated to provide the latest legal information.

2. The system of claim 1.

6. The information providing unit The information provided by the AI ​​is conveyed to police officers via a voice assistant.

2. The system of claim 1.

7. The generated AI is When storing data, blockchain technology is used to ensure data tamper-proofing and reliability.

2. The system of claim 1.

8. The generated AI is Using emotion estimation function, the emotional changes of people in the video data are analyzed to detect abnormal behavior.

2. The system of claim 1.

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