System

Abstract

An object of the system according to the embodiment is to extract necessary information from a monitoring camera video and automatically notify the police.SOLUTION: A system includes a monitoring camera video acquisition unit, a real-time analysis unit, an information extraction unit, and a police reporting unit. The surveillance camera image acquisition unit acquires a surveillance camera image. The real-time analysis unit analyzes the monitoring camera video acquired by the monitoring camera video acquisition unit in real time. The information extraction unit extracts necessary information from the information analyzed by the real-time analysis unit. The police reporting unit transmits the information extracted by the information extraction unit to the police.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 has had the problem of making it difficult to quickly and accurately extract information about incidents and accidents from surveillance camera footage and report it to the police.

[0005] The system according to the embodiment aims to extract necessary information from surveillance camera footage and automatically report it to the police. [Means for solving the problem]

[0006] The system according to the embodiment includes a surveillance camera video acquisition unit, a real-time analysis unit, an information extraction unit, and a police notification unit. The surveillance camera video acquisition unit acquires surveillance camera video. The real-time analysis unit analyzes the surveillance camera video acquired by the surveillance camera video acquisition unit in real time. The information extraction unit extracts necessary information from the information analyzed by the real-time analysis unit. The police notification unit transmits the information extracted by the information extraction unit to the police. [Effects of the Invention]

[0007] The system according to the embodiment can extract necessary information from surveillance camera footage and automatically notify the police. [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 surveillance camera analysis system according to an embodiment of the present invention uses AI to analyze videos of incidents and accidents captured on surveillance cameras and automatically transmits the necessary information to the police. This enables the surveillance camera analysis system to quickly detect the occurrence of an incident or accident and automatically transmit the necessary information to the police.

[0029] A surveillance camera analysis system according to an embodiment includes a surveillance camera video acquisition unit, a real-time analysis unit, an information extraction unit, and a police notification unit. The surveillance camera video acquisition unit acquires surveillance camera video. For example, it can acquire video from fixed cameras, mobile cameras, and infrared cameras. The surveillance camera video acquisition unit acquires video data in real time and provides it for analysis. The real-time analysis unit analyzes the acquired surveillance camera video in real time. For example, AI can detect movement and abnormal behavior in the video and determine whether an incident or accident has occurred. The real-time analysis unit can analyze the movement of people and vehicles in the video to detect abnormal behavior, collisions, etc. The information extraction unit extracts necessary information from the information analyzed by the real-time analysis unit. For example, it can identify the location and time of the incident, characteristics of those involved, etc. The information extraction unit extracts necessary information using text in the video, license plates, facial recognition, and other technologies. The police notification unit transmits the information extracted by the information extraction unit to the police. For example, it can convert the information extracted by AI into text format and transmit it to the police's system. The police reporting unit generates a report based on the extracted information and transmits it to the police. As a result, the surveillance camera analysis system according to the embodiment analyzes surveillance camera footage in real time and automatically transmits necessary information to the police, enabling a prompt response.

[0030] The real-time analysis unit analyzes audio in the video and can detect abnormal sounds to determine whether an incident or accident has occurred. For example, the real-time analysis unit uses AI to analyze audio in the video in real time and detect abnormal sounds. For example, it can identify screams or collision sounds and compare the location of the sound with the video to determine whether an incident or accident has occurred. This allows for more accurate determination of the occurrence of an incident or accident through audio analysis.

[0031] The real-time analysis unit analyzes changes in light within the video and can detect sudden changes in brightness or movement in the dark. For example, the real-time analysis unit uses AI to analyze changes in light within the video in real time and detect sudden changes in brightness. For example, it can detect sudden flashes or blinking lights and determine whether they are signs of an incident or accident. This allows for more accurate determination of the occurrence of an incident or accident by analyzing changes in light.

[0032] The real-time analysis unit can add footage from drones to the analysis of surveillance camera footage, enabling wide-area monitoring. For example, the real-time analysis unit can integrate footage from surveillance cameras and drones to enable wide-area monitoring. For example, it can analyze footage taken from the sky by a drone in real time and combine it with footage from surveillance cameras on the ground to detect abnormalities. In this way, adding drone footage makes it possible to monitor a wide area.

[0033] The real-time analysis unit analyzes the movements of animals in the video and can detect abnormal animal behavior. For example, AI can analyze the movements of animals in the video and detect abnormal behavior. For example, if an animal suddenly starts running or behaves unnaturally, it can determine whether this is a sign of an incident or accident. This allows for more accurate detection of abnormal animal behavior, making it possible to determine whether an incident or accident has occurred.

[0034] The information extraction unit can identify the type of object in the video and detect dangerous objects or weapons. For example, the information extraction unit uses AI to analyze objects in the video and identify dangerous objects or weapons. For example, it can detect weapons such as knives or guns and determine whether they are a sign of an incident or accident. This allows for more accurate detection of incidents or accidents by detecting dangerous objects and weapons.

[0035] The information extraction unit can detect specific keywords from the audio in the video and determine whether an incident or accident has occurred. For example, the information extraction unit uses AI to analyze the audio in the video and detect specific keywords. For example, it can detect keywords that indicate an emergency, such as "help" or "fire," and determine whether they are a sign of an incident or accident. By detecting specific keywords, it is possible to more accurately determine whether an incident or accident has occurred.

[0036] The information extraction unit can analyze environmental information in the video and evaluate the risk of incidents or accidents occurring. For example, AI can analyze environmental information in the video and evaluate the risk of incidents or accidents occurring. For example, it can determine the risk under specific conditions based on weather and time of day. In this way, the risk of incidents or accidents occurring can be evaluated by analyzing environmental information.

[0037] The information extraction unit can integrate multiple camera footage within a video and extract more accurate information. For example, AI can analyze footage from different angles to identify details of an incident or accident. This allows more accurate information to be extracted by integrating multiple camera footage.

[0038] The police reporting unit can convert the extracted information into audio format and send an audio report to the police. For example, the police reporting unit can build a system that converts information extracted by AI into audio format and automatically reports to the police. For example, the location and time of the incident and the characteristics of those involved can be communicated by audio. This audio report can encourage the police to respond quickly.

[0039] The police reporting unit can display the extracted information on a map, visually indicating the location of an incident or accident. For example, the police reporting unit can build a system that displays the information extracted by AI on a map, visually indicating the location of an incident or accident. For example, it can display a marker on the map to identify the location of the incident. This can support a quick response by the police by displaying it on a map.

[0040] The police reporting unit can send the extracted information to the police via social media or messaging apps. For example, the police reporting unit will build a system that sends the information extracted by AI to the police via social media or messaging apps. For example, emergency calls can be made via social media or messaging apps. In this way, sending information via social media or messaging apps can encourage the police to respond more quickly.

[0041] The police reporting unit can automatically register the extracted information in a police database so that it can be referenced later. For example, the police reporting unit will build a system that automatically registers information extracted by AI in a police database. For example, detailed information about incidents and accidents will be saved in the database. By automatically registering the information in the database, it will be possible to reference it later.

[0042] The recording and archiving unit can automatically extract important parts of a video and create and save a shortened version. For example, the recording and archiving unit will build a system in which AI automatically extracts important parts of a video and creates and saves a shortened version. For example, it can extract and save scenes where an incident or accident occurs. This makes it easier to review later by extracting and saving important parts.

[0043] The recording and archiving unit can automatically add metadata to videos to make them easier to search. For example, the recording and archiving unit will build a system in which AI automatically adds metadata to videos to make them easier to search. For example, the location and time of occurrence can be added as metadata. By adding metadata, videos can be more easily searched.

[0044] The recording and archiving unit can automatically back up video to cloud storage, ensuring data safety. For example, the recording and archiving unit will build a system in which AI automatically backs up video to cloud storage. For example, video of incidents and accidents can be stored in the cloud to ensure data safety. By backing up to cloud storage, data safety can be ensured.

[0045] The recording and storage unit can automatically encrypt video to protect the confidentiality of data. For example, the recording and storage unit will build a system in which AI automatically encrypts video. For example, it will encrypt video of incidents and accidents to protect the confidentiality of data. By encrypting the video, the confidentiality of data can be protected.

[0046] The Police Collaboration Department will work with the police database to predict the current situation based on data from past incidents and accidents. For example, the Police Collaboration Department will build a system where AI works with the police database to predict the current situation based on data from past incidents and accidents. For example, it will analyze past data and evaluate current risks. This will enable the police to support their response by predicting the current situation based on past data.

[0047] The Police Liaison Department will work with police systems to share information in real time, enabling rapid responses. For example, the Police Liaison Department will build a system in which AI will work with police systems to share information in real time. For example, information on incidents and accidents will be sent to the police in real time, encouraging a rapid response. This will support the police's rapid response by sharing information in real time.

[0048] The Police Collaboration Department will work with the police database to predict the current situation based on data from past incidents and accidents. For example, the Police Collaboration Department will build a system where AI works with the police database to predict the current situation based on data from past incidents and accidents. For example, it will analyze past data and evaluate current risks. This will enable the police to support their response by predicting the current situation based on past data.

[0049] The Police Liaison Department will work with police systems to share information in real time, enabling rapid responses. For example, the Police Liaison Department will build a system in which AI will work with police systems to share information in real time. For example, information on incidents and accidents will be sent to the police in real time, encouraging a rapid response. This will support the police's rapid response by sharing information in real time.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The surveillance camera analysis system can further include an environmental sensor unit. The environmental sensor unit acquires environmental data such as temperature, humidity, and air pressure and provides it to the real-time analysis unit. For example, it can monitor sudden temperature increases to detect the outbreak of a fire. It can also detect sudden changes in humidity to determine signs of flood damage. This allows for more accurate detection of incidents and accidents by analyzing environmental data.

[0052] The surveillance camera analysis system can further include a crowd analysis unit. The crowd analysis unit analyzes the movement of crowds in the video and detects abnormal behavior. For example, if a crowd suddenly starts running or gathers in a particular direction, it can determine whether this is a sign of an incident or accident. It can also analyze the density and movement patterns of crowds to predict dangerous situations. This allows for more accurate determination of the occurrence of incidents or accidents by analyzing crowd movement.

[0053] The surveillance camera analysis system can further include an abnormal behavior prediction unit. The abnormal behavior prediction unit learns abnormal behavior patterns based on past data and predicts future abnormal behavior. For example, it can calculate the probability of abnormal behavior occurring at a specific time or location and send an advance warning to the police. It can also provide the predicted abnormal behavior results to the real-time analysis unit to improve analysis accuracy. In this way, by predicting abnormal behavior, it is possible to prevent incidents and accidents from occurring.

[0054] The surveillance camera analysis system can also be equipped with a voice recognition unit. The voice recognition unit converts the audio in the video into text and provides it to the real-time analysis unit. For example, by converting screams or collision sounds into text and analyzing the content, it is possible to determine whether an incident or accident has occurred. The voice recognition unit can also detect specific keywords and automatically report them to the police. By converting audio into text, it is possible to more accurately determine whether an incident or accident has occurred.

[0055] The surveillance camera analysis system can further include a facial recognition unit. The facial recognition unit recognizes the faces of people in the video and compares them with a database to identify specific individuals. For example, it can detect wanted criminals or missing persons and automatically report them to the police. The facial recognition unit can also track the movements of people in the video and detect abnormal behavior. This allows for more accurate determination of the occurrence of incidents or accidents.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The surveillance camera video acquisition unit acquires surveillance camera video. For example, it can acquire video from a fixed camera, a mobile camera, or an infrared camera. The surveillance camera video acquisition unit also acquires video data in real time and provides it for analysis. Step 2: The real-time analysis unit analyzes the captured surveillance camera footage in real time. For example, AI can detect movement and abnormal behavior in the footage and determine whether an incident or accident has occurred. The real-time analysis unit also analyzes the movements of people and vehicles in the footage to detect abnormal behavior and collisions. Step 3: The information extraction unit extracts necessary information from the information analyzed by the real-time analysis unit. For example, it identifies the location and time of the incident, the characteristics of those involved, etc. The information extraction unit also extracts necessary information using technologies such as text in the video, license plate numbers, and facial recognition. Step 4: The police reporting unit sends the information extracted by the information extraction unit to the police. For example, the AI ​​converts the extracted information into text format and sends it to the police system. The police reporting unit also generates a report based on the extracted information and sends it to the police.

[0058] (Example 2) The surveillance camera analysis system according to an embodiment of the present invention uses AI to analyze videos of incidents and accidents captured on surveillance cameras and automatically transmits the necessary information to the police. This enables the surveillance camera analysis system to quickly detect the occurrence of an incident or accident and automatically transmit the necessary information to the police.

[0059] A surveillance camera analysis system according to an embodiment includes a surveillance camera video acquisition unit, a real-time analysis unit, an information extraction unit, and a police notification unit. The surveillance camera video acquisition unit acquires surveillance camera video. For example, it can acquire video from fixed cameras, mobile cameras, and infrared cameras. The surveillance camera video acquisition unit acquires video data in real time and provides it for analysis. The real-time analysis unit analyzes the acquired surveillance camera video in real time. For example, AI can detect movement and abnormal behavior in the video and determine whether an incident or accident has occurred. The real-time analysis unit can analyze the movement of people and vehicles in the video to detect abnormal behavior, collisions, etc. The information extraction unit extracts necessary information from the information analyzed by the real-time analysis unit. For example, it can identify the location and time of the incident, characteristics of those involved, etc. The information extraction unit extracts necessary information using text in the video, license plates, facial recognition, and other technologies. The police notification unit transmits the information extracted by the information extraction unit to the police. For example, it can convert the information extracted by AI into text format and transmit it to the police's system. The police reporting unit generates a report based on the extracted information and transmits it to the police. As a result, the surveillance camera analysis system according to the embodiment analyzes surveillance camera footage in real time and automatically transmits necessary information to the police, enabling a prompt response.

[0060] The real-time analysis unit analyzes audio in the video and can detect abnormal sounds to determine whether an incident or accident has occurred. For example, the real-time analysis unit uses AI to analyze audio in the video in real time and detect abnormal sounds. For example, it can identify screams or collision sounds and compare the location of the sound with the video to determine whether an incident or accident has occurred. This allows for more accurate determination of the occurrence of an incident or accident through audio analysis.

[0061] The real-time analysis unit analyzes changes in light within the video and can detect sudden changes in brightness or movement in the dark. For example, the real-time analysis unit uses AI to analyze changes in light within the video in real time and detect sudden changes in brightness. For example, it can detect sudden flashes or blinking lights and determine whether they are signs of an incident or accident. This allows for more accurate determination of the occurrence of an incident or accident by analyzing changes in light.

[0062] The real-time analysis unit uses an emotion estimation function to infer emotions from the facial expressions and movements of people in the video and detect abnormal behavior. For example, the real-time analysis unit uses AI to analyze the facial expressions of people in the video and infer emotions. For example, it can detect expressions of anger or fear and determine whether they are signs of an incident or accident. This allows for more accurate emotion estimation to detect abnormal behavior.

[0063] The real-time analysis unit can add footage from drones to the analysis of surveillance camera footage, enabling wide-area monitoring. For example, the real-time analysis unit can integrate footage from surveillance cameras and drones to enable wide-area monitoring. For example, it can analyze footage taken from the sky by a drone in real time and combine it with footage from surveillance cameras on the ground to detect abnormalities. In this way, adding drone footage makes it possible to monitor a wide area.

[0064] The real-time analysis unit analyzes the movements of animals in the video and can detect abnormal animal behavior. For example, AI can analyze the movements of animals in the video and detect abnormal behavior. For example, if an animal suddenly starts running or behaves unnaturally, it can determine whether this is a sign of an incident or accident. This allows for more accurate detection of abnormal animal behavior, making it possible to determine whether an incident or accident has occurred.

[0065] The real-time analysis unit uses an emotion estimation function to analyze the emotions of people in the video in real time and detect abnormal emotional changes. For example, the real-time analysis unit uses AI to analyze the facial expressions of people in the video and estimate their emotions in real time. For example, if a person's facial expression suddenly changes from normal to anger or fear, it determines whether this is an abnormal emotional change. This allows for more accurate detection of abnormal behavior by analyzing emotional changes in real time.

[0066] The information extraction unit can identify the type of object in the video and detect dangerous objects or weapons. For example, the information extraction unit uses AI to analyze objects in the video and identify dangerous objects or weapons. For example, it can detect weapons such as knives or guns and determine whether they are a sign of an incident or accident. This allows for more accurate detection of incidents or accidents by detecting dangerous objects and weapons.

[0067] The information extraction unit can detect specific keywords from the audio in the video and determine whether an incident or accident has occurred. For example, the information extraction unit uses AI to analyze the audio in the video and detect specific keywords. For example, it can detect keywords that indicate an emergency, such as "help" or "fire," and determine whether they are a sign of an incident or accident. By detecting specific keywords, it is possible to more accurately determine whether an incident or accident has occurred.

[0068] The information extraction unit uses the emotion estimation function to analyze the emotions of people in the video and can detect incidents and accidents based on changes in emotion. For example, the information extraction unit uses AI to analyze the facial expressions of people in the video and detect incidents and accidents based on changes in emotion. For example, if a person's facial expression suddenly changes from normal to one of anger or fear, it determines whether this is a sign of an incident or accident. By analyzing changes in emotion, it is possible to more accurately determine the occurrence of an incident or accident.

[0069] The information extraction unit can analyze environmental information in the video and evaluate the risk of incidents or accidents occurring. For example, AI can analyze environmental information in the video and evaluate the risk of incidents or accidents occurring. For example, it can determine the risk under specific conditions based on weather and time of day. In this way, the risk of incidents or accidents occurring can be evaluated by analyzing environmental information.

[0070] The information extraction unit can integrate multiple camera footage within a video and extract more accurate information. For example, AI can analyze footage from different angles to identify details of an incident or accident. This allows more accurate information to be extracted by integrating multiple camera footage.

[0071] The information extraction unit uses the emotion estimation function to analyze the emotions of people in the video and can identify the characteristics of participants based on changes in emotion. For example, the information extraction unit uses AI to analyze the facial expressions of people in the video and identify the characteristics of participants based on changes in emotion. For example, if a person's facial expression suddenly changes from a normal expression to one of anger or fear, it determines whether this is a characteristic of a participant. This makes it possible to identify the characteristics of participants by analyzing changes in emotion.

[0072] The police reporting unit can convert the extracted information into audio format and send an audio report to the police. For example, the police reporting unit can build a system that converts information extracted by AI into audio format and automatically reports to the police. For example, the location and time of the incident and the characteristics of those involved can be communicated by audio. This audio report can encourage the police to respond quickly.

[0073] The police reporting unit can display the extracted information on a map, visually indicating the location of an incident or accident. For example, the police reporting unit can build a system that displays the information extracted by AI on a map, visually indicating the location of an incident or accident. For example, it can display a marker on the map to identify the location of the incident. This can support a quick response by the police by displaying it on a map.

[0074] The police reporting unit can use the emotion estimation function to add emotional elements to the report content and attract the attention of the police. The police reporting unit, for example, uses the emotion estimation function to build a system that adds emotional elements to the report content. For example, emotional expressions are used to emphasize urgency. In this way, adding emotional elements can attract the attention of the police and encourage a quick response.

[0075] The police reporting unit can send the extracted information to the police via social media or messaging apps. For example, the police reporting unit will build a system that sends the information extracted by AI to the police via social media or messaging apps. For example, emergency calls can be made via social media or messaging apps. In this way, sending information via social media or messaging apps can encourage the police to respond more quickly.

[0076] The police reporting unit can automatically register the extracted information in a police database so that it can be referenced later. For example, the police reporting unit will build a system that automatically registers information extracted by AI in a police database. For example, detailed information about incidents and accidents will be saved in the database. By automatically registering the information in the database, it will be possible to reference it later.

[0077] The police reporting unit can use the emotion estimation function to add emotional elements to the report content, thereby encouraging a quick response by the police. The police reporting unit, for example, uses the emotion estimation function to build a system that adds emotional elements to the report content. For example, emotional expressions are used to emphasize urgency. In this way, adding emotional elements can encourage a quick response by the police.

[0078] The recording and archiving unit can automatically extract important parts of a video and create and save a shortened version. For example, the recording and archiving unit will build a system in which AI automatically extracts important parts of a video and creates and saves a shortened version. For example, it can extract and save scenes where an incident or accident occurs. This makes it easier to review later by extracting and saving important parts.

[0079] The recording and archiving unit can automatically add metadata to videos to make them easier to search. For example, the recording and archiving unit will build a system in which AI automatically adds metadata to videos to make them easier to search. For example, the location and time of occurrence can be added as metadata. By adding metadata, videos can be more easily searched.

[0080] The record storage unit can use the emotion estimation function to record the emotions of people in the video so that changes in emotions can be analyzed later. The record storage unit, for example, uses the emotion estimation function to build a system that records the emotions of people in the video. For example, emotions at the time of an incident or accident can be recorded so that they can be analyzed later. In this way, by recording changes in emotions, detailed analysis can be performed later.

[0081] The recording and archiving unit can automatically back up video to cloud storage, ensuring data safety. For example, the recording and archiving unit will build a system in which AI automatically backs up video to cloud storage. For example, video of incidents and accidents can be stored in the cloud to ensure data safety. By backing up to cloud storage, data safety can be ensured.

[0082] The recording and storage unit can automatically encrypt video to protect the confidentiality of data. For example, the recording and storage unit will build a system in which AI automatically encrypts video. For example, it will encrypt video of incidents and accidents to protect the confidentiality of data. By encrypting the video, the confidentiality of data can be protected.

[0083] The record storage unit can use the emotion estimation function to record the emotions of people in the video so that changes in emotions can be analyzed later. The record storage unit, for example, uses the emotion estimation function to build a system that records the emotions of people in the video. For example, emotions at the time of an incident or accident can be recorded so that they can be analyzed later. In this way, by recording changes in emotions, detailed analysis can be performed later.

[0084] The Police Collaboration Department will work with the police database to predict the current situation based on data from past incidents and accidents. For example, the Police Collaboration Department will build a system where AI works with the police database to predict the current situation based on data from past incidents and accidents. For example, it will analyze past data and evaluate current risks. This will enable the police to support their response by predicting the current situation based on past data.

[0085] The Police Liaison Department will work with police systems to share information in real time, enabling rapid responses. For example, the Police Liaison Department will build a system in which AI will work with police systems to share information in real time. For example, information on incidents and accidents will be sent to the police in real time, encouraging a rapid response. This will support the police's rapid response by sharing information in real time.

[0086] The police cooperation unit can use the emotion estimation function to analyze the emotions of police officers, detect stress and fatigue, and provide support for response. The police cooperation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of police officers. For example, it can analyze the facial expressions and voice of police officers to detect stress and fatigue. In this way, by analyzing the emotions of police officers, it can detect stress and fatigue and support appropriate response.

[0087] The Police Collaboration Department will work with the police database to predict the current situation based on data from past incidents and accidents. For example, the Police Collaboration Department will build a system where AI works with the police database to predict the current situation based on data from past incidents and accidents. For example, it will analyze past data and evaluate current risks. This will enable the police to support their response by predicting the current situation based on past data.

[0088] The Police Liaison Department will work with police systems to share information in real time, enabling rapid responses. For example, the Police Liaison Department will build a system in which AI will work with police systems to share information in real time. For example, information on incidents and accidents will be sent to the police in real time, encouraging a rapid response. This will support the police's rapid response by sharing information in real time.

[0089] The police cooperation unit can use the emotion estimation function to analyze the emotions of police officers, detect stress and fatigue, and provide support for response. The police cooperation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of police officers. For example, it can analyze the facial expressions and voice of police officers to detect stress and fatigue. In this way, by analyzing the emotions of police officers, it can detect stress and fatigue and support appropriate response.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The surveillance camera analysis system can further include an environmental sensor unit. The environmental sensor unit acquires environmental data such as temperature, humidity, and air pressure and provides it to the real-time analysis unit. For example, it can monitor sudden temperature increases to detect the outbreak of a fire. It can also detect sudden changes in humidity to determine signs of flood damage. This allows for more accurate detection of incidents and accidents by analyzing environmental data.

[0092] The surveillance camera analysis system can further include a crowd analysis unit. The crowd analysis unit analyzes the movement of crowds in the video and detects abnormal behavior. For example, if a crowd suddenly starts running or gathers in a particular direction, it can determine whether this is a sign of an incident or accident. It can also analyze the density and movement patterns of crowds to predict dangerous situations. This allows for more accurate determination of the occurrence of incidents or accidents by analyzing crowd movement.

[0093] The surveillance camera analysis system can further include an abnormal behavior prediction unit. The abnormal behavior prediction unit learns abnormal behavior patterns based on past data and predicts future abnormal behavior. For example, it can calculate the probability of abnormal behavior occurring at a specific time or location and send an advance warning to the police. It can also provide the predicted abnormal behavior results to the real-time analysis unit to improve analysis accuracy. In this way, by predicting abnormal behavior, it is possible to prevent incidents and accidents from occurring.

[0094] The surveillance camera analysis system can also be equipped with a voice recognition unit. The voice recognition unit converts the audio in the video into text and provides it to the real-time analysis unit. For example, by converting screams or collision sounds into text and analyzing the content, it is possible to determine whether an incident or accident has occurred. The voice recognition unit can also detect specific keywords and automatically report them to the police. By converting audio into text, it is possible to more accurately determine whether an incident or accident has occurred.

[0095] The surveillance camera analysis system can further include a facial recognition unit. The facial recognition unit recognizes the faces of people in the video and compares them with a database to identify specific individuals. For example, it can detect wanted criminals or missing persons and automatically report them to the police. The facial recognition unit can also track the movements of people in the video and detect abnormal behavior. This allows for more accurate determination of the occurrence of incidents or accidents.

[0096] The real-time analysis unit uses an emotion estimation function to analyze the emotions of people in the video and can detect abnormal behavior based on changes in emotion. For example, if a person's facial expression suddenly changes from normal to anger or fear, it can determine whether this is a sign of an incident or accident. It can also analyze changes in emotion in real time and automatically report the incident to the police. This allows for more accurate detection of abnormal behavior by analyzing changes in emotion.

[0097] The information extraction unit uses the emotion estimation function to analyze the emotions of people in the video and identify the characteristics of participants based on changes in emotion. For example, if a person's facial expression suddenly changes from normal to one of anger or fear, it can determine whether this is a characteristic of a participant. It can also analyze changes in emotion to identify the participant's behavioral patterns. This makes it possible to identify the characteristics of participants by analyzing changes in emotion.

[0098] The police reporting department can use the emotion estimation function to add emotional elements to the report content to attract the police's attention. For example, emotional expressions can be used to emphasize urgency. In addition, the report content can be generated based on changes in emotion and sent to the police. By adding emotional elements, the report content can attract the police's attention and encourage a quick response.

[0099] The recording and storage unit can use its emotion estimation function to record the emotions of people in the video so that changes in emotion can be analyzed later. For example, it can record emotions during incidents or accidents so that they can be analyzed later. It can also automatically extract important parts of the video based on changes in emotion and create and save shortened versions. This allows for detailed analysis later by recording changes in emotion.

[0100] The Police Liaison Department can use the emotion estimation function to analyze the emotions of police officers, detect stress and fatigue, and provide support for responding appropriately. For example, it can analyze the facial expressions and voice of police officers to detect stress and fatigue. It can also adjust the officers' work schedules and provide appropriate rest based on changes in their emotions. This makes it possible to detect stress and fatigue by analyzing the emotions of police officers and support appropriate responses.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The surveillance camera video acquisition unit acquires surveillance camera video. For example, it can acquire video from a fixed camera, a mobile camera, or an infrared camera. The surveillance camera video acquisition unit also acquires video data in real time and provides it for analysis. Step 2: The real-time analysis unit analyzes the captured surveillance camera footage in real time. For example, AI can detect movement and abnormal behavior in the footage and determine whether an incident or accident has occurred. The real-time analysis unit also analyzes the movements of people and vehicles in the footage to detect abnormal behavior and collisions. Step 3: The information extraction unit extracts necessary information from the information analyzed by the real-time analysis unit. For example, it identifies the location and time of the incident, the characteristics of those involved, etc. The information extraction unit also extracts necessary information using technologies such as text in the video, license plate numbers, and facial recognition. Step 4: The police reporting unit sends the information extracted by the information extraction unit to the police. For example, the AI ​​converts the extracted information into text format and sends it to the police system. The police reporting unit also generates a report based on the extracted information and sends it to the police.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 surveillance camera image acquisition unit that acquires surveillance camera images; a real-time analysis unit that analyzes the surveillance camera video acquired by the surveillance camera video acquisition unit in real time; an information extraction unit that extracts necessary information from the information analyzed by the real-time analysis unit; a police reporting unit that transmits the information extracted by the information extraction unit to the police. A system characterized by:

2. The real-time analysis unit Analyzes audio in video and detects abnormal sounds to determine whether an incident or accident has occurred 2. The system of claim 1.

3. The real-time analysis unit By adding drone footage to the analysis of surveillance camera footage, wide-area monitoring can be achieved.

2. The system of claim 1.

4. The information extraction unit Identify the type of object in the video and detect dangerous objects and weapons 2. The system of claim 1.

5. The police reporting unit Converting the extracted information into an audio format and sending an audio report to the police.

2. The system of claim 1.

6. The Records and Archives Department Emotion estimation function allows recording of emotions of people in a video and later analyzing changes in those emotions.

2. The system of claim 1.

7. The Police Liaison Department Using emotion estimation functionality, the system analyzes the emotions of police officers, detects stress and fatigue, and provides support for responding to the situation.

2. The system of claim 1.

Images