System

Abstract

An object of the system according to the embodiment is to monitor a monitoring camera video in real time and immediately report a suspicious behavior or crime.SOLUTION: A system according to an embodiment includes a monitoring camera video acquisition unit, a real-time monitoring unit, a reporting unit, a storage unit, and a search unit. The surveillance camera image acquisition unit acquires a surveillance camera image. The real-time monitoring unit monitors the monitoring camera video acquired by the monitoring camera video acquisition unit in real time. The reporting unit immediately reports the suspicious behavior or crime detected by the real-time monitoring unit. The storage unit stores the gray behavior detected by the real-time monitoring unit. The search unit searches for a corresponding image when the user transmits the image.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately monitor surveillance camera footage in real time or immediately report suspicious behavior, leaving room for improvement.

[0005] The system according to the embodiment aims to monitor surveillance camera footage in real time and immediately report suspicious behavior or crimes. [Means for solving the problem]

[0006] The system according to the embodiment includes a surveillance camera video acquisition unit, a real-time monitoring unit, a reporting unit, a storage unit, and a search unit. The surveillance camera video acquisition unit acquires surveillance camera video. The real-time monitoring unit monitors the surveillance camera video acquired by the surveillance camera video acquisition unit in real time. The reporting unit immediately reports suspicious behavior or crime detected by the real-time monitoring unit. The storage unit stores questionable behavior detected by the real-time monitoring unit. The search unit searches for relevant video when the user provides an image. [Effects of the Invention]

[0007] The system according to the embodiment can monitor surveillance camera footage in real time and immediately report suspicious behavior or crimes. [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) A surveillance system according to an embodiment of the present invention monitors surveillance camera footage in real time, immediately reports suspicious behavior and crimes, stores questionable behavior, and allows users to quickly search for relevant footage by providing an image. This allows the surveillance system to efficiently and effectively utilize surveillance camera footage, thereby preventing crimes and providing a rapid response.

[0029] A surveillance system according to an embodiment includes a surveillance camera video acquisition unit, a real-time monitoring unit, a storage unit, a search unit, and a reporting unit. The surveillance camera video acquisition unit acquires surveillance camera video. For example, the surveillance camera video acquisition unit acquires video using a high-resolution camera. The surveillance camera video acquisition unit can also acquire integrated video from multiple cameras. The surveillance camera video acquisition unit can also use an infrared camera to acquire video at night or in low-light environments. The real-time monitoring unit monitors the surveillance camera video acquired by the surveillance camera video acquisition unit in real time. For example, the real-time monitoring unit can analyze movements in the video using AI to detect suspicious behavior or crime. The real-time monitoring unit can also use AI to learn human movements and behavioral patterns and detect unusual movements. The real-time monitoring unit can also use AI to detect behaviors such as "a person suddenly running," "hiding an object," or "staying in a specific area for a long time." The reporting unit immediately reports suspicious behavior or crime detected by the real-time monitoring unit. For example, if the AI ​​detects behavior such as "a person committing an act of violence" or "a theft in progress," the reporting unit automatically notifies the police or a security company. The reporting unit can also report detected activity including a portion of the video and location information. Furthermore, the reporting unit can transmit video in real time when reporting. The storage unit stores questionable activity detected by the real-time monitoring unit. For example, if the AI ​​detects behavior such as "a person frequently passing through the same location" or "an unnatural delivery of a package," the storage unit stores the video without overwriting it. The storage unit can also add metadata to the stored video for later analysis. Furthermore, the storage unit can encrypt and protect the stored video. The search unit searches for relevant video when the user provides an image. For example, when the user provides an image such as "a person wearing a red hat" or "an event occurring in a specific location at a specific time," the AI ​​uses that information to search for and quickly display the relevant video. The search unit can also use generative AI (e.g., text generation AI or multimodal generation AI) to search for video based on user instructions.Furthermore, the search unit can also provide an interface for visually displaying search results to the user. This allows the surveillance system according to the embodiment to monitor surveillance camera footage in real time, immediately report suspicious behavior or crimes, store questionable behavior, and allow the user to quickly search for relevant footage by providing an image. For example, this is expected to improve safety in commercial facilities and public places and have a deterrent effect on crime. Furthermore, facilitating later video search will enable faster resolution of cases and the collection of evidence.

[0030] The real-time monitoring unit simultaneously analyzes the audio in the video, making it possible to detect suspicious behavior from both audio and video. For example, when AI analyzes surveillance camera video, the real-time monitoring unit simultaneously analyzes the audio in the video. For example, AI analyzes conversations and environmental sounds in the video and detects abnormal audio patterns (for example, screaming or the sound of breaking glass). This makes it possible to detect suspicious behavior from both audio and video. This makes it possible to detect suspicious behavior from both audio and video.

[0031] The real-time monitoring unit performs facial recognition of people in the video, identifies those with a criminal record, and automatically raises the alert level. For example, the real-time monitoring unit uses AI to perform facial recognition of people in the video and identify those with a criminal record. For example, the AI ​​compares the images with a criminal database and raises the alert level if a matching person appears in the video. This makes it possible to identify people with a criminal record and automatically raise the alert level.

[0032] In addition to analyzing surveillance camera footage, the real-time monitoring unit also integrates data from surrounding sensors, enabling more accurate detection of suspicious behavior. For example, in addition to using AI to analyze surveillance camera footage, the real-time monitoring unit integrates data from surrounding sensors (temperature, vibration, sound, etc.). For example, data from a temperature sensor is analyzed, and suspicious behavior is identified if an abnormal temperature change is detected. This allows data from surrounding sensors to be integrated, enabling more accurate detection of suspicious behavior.

[0033] When analyzing surveillance camera footage, the real-time monitoring unit recognizes objects in the footage and can report to the reporting unit if it detects a dangerous object. For example, the real-time monitoring unit uses AI to analyze surveillance camera footage and recognize objects in the footage. For example, AI can detect dangerous objects such as weapons or explosives and immediately report them. This makes it possible to detect and report dangerous objects.

[0034] When the storage unit detects shady behavior, it automatically saves the video footage before and after the behavior, making it easier to understand the context of the behavior. For example, when AI detects shady behavior, the storage unit automatically saves the video footage before and after the behavior. For example, when AI detects suspicious behavior, it saves the video footage for several minutes before and after the behavior, making it easier to understand the context of the behavior. In this way, by saving the video footage before and after the behavior, it makes it easier to understand the context of the behavior.

[0035] When storing gray behavior, the storage unit analyzes the frequency and patterns of behavior and can issue a warning if an abnormal pattern is repeated. For example, when storing gray behavior, the storage unit uses AI to analyze the frequency and patterns of behavior. For example, the AI ​​detects a pattern of a particular person frequently traveling to the same place and issues a warning if an abnormal pattern is repeated. This makes it possible to analyze the frequency and patterns of behavior and issue a warning if an abnormal pattern is repeated.

[0036] In addition to storing gray actions, the storage unit can also integrate and store other data related to the actions. For example, in addition to storing gray actions, the storage unit can integrate and store other data related to the actions. For example, the storage unit can integrate surrounding sensor information and past footage to understand the background of the actions. This allows other data related to the actions to be integrated and stored.

[0037] When storing gray behavior, the storage unit can analyze the social media activity of a person related to the behavior and store relevant information. For example, when AI stores gray behavior, the storage unit analyzes the social media activity of a person related to the behavior. For example, the AI ​​analyzes the person's social media posts and stores relevant information. This makes it possible to analyze social media activity and store relevant information.

[0038] When a user searches for video, the search unit uses natural language processing technology to generate a more detailed search query, enabling highly accurate video searches. For example, when a user searches for video, the search unit uses natural language processing technology to generate a detailed search query. For example, if a user enters "video of a person wearing a red hat running," the AI ​​analyzes the query and searches for corresponding video. This allows the use of natural language processing technology to generate a more detailed search query, enabling highly accurate video searches.

[0039] When performing a video search, the search unit uses AI to learn past search history and user behavior patterns, enabling it to provide optimal search results. For example, when performing a video search, the search unit uses AI to learn past search history and user behavior patterns. For example, the AI ​​provides optimal search results based on queries the user has previously searched for and videos they have viewed. This allows it to learn past search history and user behavior patterns and provide optimal search results.

[0040] When performing a video search, the search unit can also analyze the audio data in the video and provide search results based on the audio information. For example, when AI performs a video search, the search unit analyzes the audio data in the video. For example, the AI ​​analyzes conversations and environmental sounds in the video and provides search results based on the audio information. This makes it possible to provide search results based on audio information.

[0041] The search unit can analyze the movements of objects and people in the video during a video search and provide search results based on the movements. For example, when an AI is used to search a video, the search unit can analyze the movements of objects and people in the video. For example, the AI ​​can analyze the movements in the video and provide search results based on specific actions or behaviors. This makes it possible to provide search results based on the movements of objects and people.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The surveillance system may further include an environmental monitoring unit. In addition to analyzing surveillance camera footage, the environmental monitoring unit can acquire surrounding environmental data (e.g., temperature, humidity, air pressure, etc.) and detect abnormal environmental changes. For example, if a temperature sensor detects a sudden rise in temperature, it can warn of the possibility of a fire. Also, if a humidity sensor detects an abnormal humidity change, it can warn of the possibility of a water leak or flooding. Furthermore, if an air pressure sensor detects an abnormal air pressure change, it can warn of the possibility of an explosion. This allows the surveillance system to analyze not only video footage but also environmental data, enabling more multifaceted surveillance.

[0044] The surveillance system can further include a behavior prediction unit. The behavior prediction unit can analyze past surveillance camera video data and learn specific behavioral patterns. For example, AI can analyze past video data and learn behavioral patterns that frequently occur in specific locations at specific times of the day. This allows the behavior prediction unit to predict future behavior based on the learned behavioral patterns and issue a warning before abnormal behavior occurs. For example, if suspicious behavior is predicted in a specific location at a specific time of day, vigilance can be strengthened in advance. This allows the surveillance system to utilize past data to predict future behavior and achieve more effective surveillance.

[0045] The surveillance system can further include an emergency response department. When the reporting department detects suspicious behavior or a crime, the emergency response department can immediately issue instructions to respond to the scene. For example, the emergency response department can automatically contact the police or a security company and instruct them to rush to the scene. The emergency response department can also propose appropriate response measures depending on the situation at the scene. For example, in the event of a fire, it can issue instructions for firefighting activities. Furthermore, the emergency response department can monitor the situation at the scene in real time and check the progress of the response. This allows the surveillance system to achieve quick and appropriate emergency response.

[0046] The surveillance system can further include an abnormal behavior prediction unit. The abnormal behavior prediction unit can analyze past surveillance camera video data and learn abnormal behavior patterns. For example, AI can analyze past video data and learn abnormal behavior patterns that frequently occur in specific locations during specific times of day. This allows the abnormal behavior prediction unit to predict future abnormal behavior based on the learned abnormal behavior patterns and issue a warning before abnormal behavior occurs. For example, if abnormal behavior is predicted in a specific location during a specific time of day, vigilance can be strengthened in advance. This allows the surveillance system to utilize past data to predict future abnormal behavior and achieve more effective surveillance.

[0047] The surveillance system can further include a drone monitoring unit. In addition to analyzing surveillance camera footage, the drone monitoring unit can also monitor a wide area using drones. For example, AI can analyze drone camera footage and monitor a wide area. This allows the surveillance system to not only use fixed cameras but also perform dynamic monitoring using drones, enabling wider-area surveillance. For example, using drones to monitor large-scale events or facilities with large grounds enables quick and effective surveillance. Furthermore, if the drone monitoring unit detects abnormal behavior, it can dispatch a drone to the scene and capture detailed footage. This allows the surveillance system to utilize drones to achieve more flexible and effective surveillance.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The surveillance camera image acquisition unit acquires surveillance camera images. For example, high-resolution cameras or infrared cameras can be used, and images from multiple cameras can be integrated and acquired. Step 2: The real-time monitoring unit monitors the surveillance camera footage acquired by the surveillance camera footage acquisition unit in real time. For example, it uses AI to analyze movements in the footage and detect suspicious behavior or crimes. Step 3: The reporting department immediately reports any suspicious behavior or crime detected by the real-time monitoring department. For example, if the AI ​​detects an act of violence or theft, it will automatically notify the police or security company. Step 4: The storage unit stores any questionable behavior detected by the real-time monitoring unit. For example, if the storage unit detects frequent movement to the same location or an unnatural delivery of a package, it stores the video. Step 5: The search unit searches for the relevant video when the user provides an image. For example, if the user provides an image such as "a person wearing a red hat" or "an event at a specific time and place," the AI ​​will use that information to search for the relevant video and quickly display it.

[0050] (Example 2) A surveillance system according to an embodiment of the present invention monitors surveillance camera footage in real time, immediately reports suspicious behavior and crimes, stores questionable behavior, and allows users to quickly search for relevant footage by providing an image. This allows the surveillance system to efficiently and effectively utilize surveillance camera footage, thereby preventing crimes and providing a rapid response.

[0051] A surveillance system according to an embodiment includes a surveillance camera video acquisition unit, a real-time monitoring unit, a storage unit, a search unit, and a reporting unit. The surveillance camera video acquisition unit acquires surveillance camera video. For example, the surveillance camera video acquisition unit acquires video using a high-resolution camera. The surveillance camera video acquisition unit can also acquire integrated video from multiple cameras. The surveillance camera video acquisition unit can also use an infrared camera to acquire video at night or in low-light environments. The real-time monitoring unit monitors the surveillance camera video acquired by the surveillance camera video acquisition unit in real time. For example, the real-time monitoring unit can analyze movements in the video using AI to detect suspicious behavior or crime. The real-time monitoring unit can also use AI to learn human movements and behavioral patterns and detect unusual movements. The real-time monitoring unit can also use AI to detect behaviors such as "a person suddenly running," "hiding an object," or "staying in a specific area for a long time." The reporting unit immediately reports suspicious behavior or crime detected by the real-time monitoring unit. For example, if the AI ​​detects behavior such as "a person committing an act of violence" or "a theft in progress," the reporting unit automatically notifies the police or a security company. The reporting unit can also report detected activity including a portion of the video and location information. Furthermore, the reporting unit can transmit video in real time when reporting. The storage unit stores questionable activity detected by the real-time monitoring unit. For example, if the AI ​​detects behavior such as "a person frequently passing through the same location" or "an unnatural delivery of a package," the storage unit stores the video without overwriting it. The storage unit can also add metadata to the stored video for later analysis. Furthermore, the storage unit can encrypt and protect the stored video. The search unit searches for relevant video when the user provides an image. For example, when the user provides an image such as "a person wearing a red hat" or "an event occurring in a specific location at a specific time," the AI ​​uses that information to search for and quickly display the relevant video. The search unit can also use generative AI (e.g., text generation AI or multimodal generation AI) to search for video based on user instructions.Furthermore, the search unit can also provide an interface for visually displaying search results to the user. This allows the surveillance system according to the embodiment to monitor surveillance camera footage in real time, immediately report suspicious behavior or crimes, store questionable behavior, and allow the user to quickly search for relevant footage by providing an image. For example, this is expected to improve safety in commercial facilities and public places and have a deterrent effect on crime. Furthermore, facilitating later video search will enable faster resolution of cases and the collection of evidence.

[0052] The real-time monitoring unit simultaneously analyzes the audio in the video, making it possible to detect suspicious behavior from both audio and video. For example, when AI analyzes surveillance camera video, the real-time monitoring unit simultaneously analyzes the audio in the video. For example, AI analyzes conversations and environmental sounds in the video and detects abnormal audio patterns (for example, screaming or the sound of breaking glass). This makes it possible to detect suspicious behavior from both audio and video. This makes it possible to detect suspicious behavior from both audio and video.

[0053] The real-time monitoring unit performs facial recognition of people in the video, identifies those with a criminal record, and automatically raises the alert level. For example, the real-time monitoring unit uses AI to perform facial recognition of people in the video and identify those with a criminal record. For example, the AI ​​compares the images with a criminal database and raises the alert level if a matching person appears in the video. This makes it possible to identify people with a criminal record and automatically raise the alert level.

[0054] The real-time monitoring unit uses an emotion estimation function to estimate emotions from the facial expressions and movements of people in the video, and can report to the reporting unit if it detects abnormal stress or tension. For example, the real-time monitoring unit uses AI to analyze the facial expressions and movements of people in the video and estimate emotions using the emotion estimation function. For example, the AI ​​detects stress or tension from facial expressions and body movements, and reports if an abnormal emotional state is confirmed. This makes it possible to detect and report abnormal stress or tension.

[0055] In addition to analyzing surveillance camera footage, the real-time monitoring unit also integrates data from surrounding sensors, enabling more accurate detection of suspicious behavior. For example, in addition to using AI to analyze surveillance camera footage, the real-time monitoring unit integrates data from surrounding sensors (temperature, vibration, sound, etc.). For example, data from a temperature sensor is analyzed, and suspicious behavior is identified if an abnormal temperature change is detected. This allows data from surrounding sensors to be integrated, enabling more accurate detection of suspicious behavior.

[0056] When analyzing surveillance camera footage, the real-time monitoring unit recognizes objects in the footage and can report to the reporting unit if it detects a dangerous object. For example, the real-time monitoring unit uses AI to analyze surveillance camera footage and recognize objects in the footage. For example, AI can detect dangerous objects such as weapons or explosives and immediately report them. This makes it possible to detect and report dangerous objects.

[0057] When the storage unit detects shady behavior, it automatically saves the video footage before and after the behavior, making it easier to understand the context of the behavior. For example, when AI detects shady behavior, the storage unit automatically saves the video footage before and after the behavior. For example, when AI detects suspicious behavior, it saves the video footage for several minutes before and after the behavior, making it easier to understand the context of the behavior. In this way, by saving the video footage before and after the behavior, it makes it easier to understand the context of the behavior.

[0058] When storing gray behavior, the storage unit analyzes the frequency and patterns of behavior and can issue a warning if an abnormal pattern is repeated. For example, when storing gray behavior, the storage unit uses AI to analyze the frequency and patterns of behavior. For example, the AI ​​detects a pattern of a particular person frequently traveling to the same place and issues a warning if an abnormal pattern is repeated. This makes it possible to analyze the frequency and patterns of behavior and issue a warning if an abnormal pattern is repeated.

[0059] The storage unit uses the emotion estimation function to analyze the emotions of a person who engages in questionable behavior, and can store the behavior if a specific emotional pattern is repeated. For example, the storage unit uses the emotion estimation function to analyze the emotions of a person who engages in questionable behavior. For example, the AI ​​analyzes the person's facial expressions and movements, and stores the behavior if a specific emotional pattern is repeated. This makes it possible to store the behavior if a specific emotional pattern is repeated.

[0060] In addition to storing gray actions, the storage unit can also integrate and store other data related to the actions. For example, in addition to storing gray actions, the storage unit can integrate and store other data related to the actions. For example, the storage unit can integrate surrounding sensor information and past footage to understand the background of the actions. This allows other data related to the actions to be integrated and stored.

[0061] When storing gray behavior, the storage unit can analyze the social media activity of a person related to the behavior and store relevant information. For example, when AI stores gray behavior, the storage unit analyzes the social media activity of a person related to the behavior. For example, the AI ​​analyzes the person's social media posts and stores relevant information. This makes it possible to analyze social media activity and store relevant information.

[0062] When a user searches for video, the search unit uses natural language processing technology to generate a more detailed search query, enabling highly accurate video searches. For example, when a user searches for video, the search unit uses natural language processing technology to generate a detailed search query. For example, if a user enters "video of a person wearing a red hat running," the AI ​​analyzes the query and searches for corresponding video. This allows the use of natural language processing technology to generate a more detailed search query, enabling highly accurate video searches.

[0063] When performing a video search, the search unit uses AI to learn past search history and user behavior patterns, enabling it to provide optimal search results. For example, when performing a video search, the search unit uses AI to learn past search history and user behavior patterns. For example, the AI ​​provides optimal search results based on queries the user has previously searched for and videos they have viewed. This allows it to learn past search history and user behavior patterns and provide optimal search results.

[0064] The search unit uses the emotion estimation function to analyze the emotion a user feels when searching, and can provide search results based on that emotion. For example, the search unit uses the emotion estimation function to analyze the emotion a user feels when searching. For example, the AI ​​analyzes the user's facial expressions and voice, and provides search results based on that emotion. This makes it possible to provide search results based on the user's emotion.

[0065] When performing a video search, the search unit can also analyze the audio data in the video and provide search results based on the audio information. For example, when AI performs a video search, the search unit analyzes the audio data in the video. For example, the AI ​​analyzes conversations and environmental sounds in the video and provides search results based on the audio information. This makes it possible to provide search results based on audio information.

[0066] The search unit can analyze the movements of objects and people in the video during a video search and provide search results based on the movements. For example, when an AI is used to search a video, the search unit can analyze the movements of objects and people in the video. For example, the AI ​​can analyze the movements in the video and provide search results based on specific actions or behaviors. This makes it possible to provide search results based on the movements of objects and people.

[0067] The search unit uses the emotion estimation function to analyze the emotion a user feels when searching, and can provide search results based on that emotion. For example, the search unit uses the emotion estimation function to analyze the emotion a user feels when searching. For example, the AI ​​analyzes the user's facial expressions and voice, and provides search results based on that emotion. This makes it possible to provide search results based on the user's emotion.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The surveillance system may further include an environmental monitoring unit. In addition to analyzing surveillance camera footage, the environmental monitoring unit can acquire surrounding environmental data (e.g., temperature, humidity, air pressure, etc.) and detect abnormal environmental changes. For example, if a temperature sensor detects a sudden rise in temperature, it can warn of the possibility of a fire. Also, if a humidity sensor detects an abnormal humidity change, it can warn of the possibility of a water leak or flooding. Furthermore, if an air pressure sensor detects an abnormal air pressure change, it can warn of the possibility of an explosion. This allows the surveillance system to analyze not only video footage but also environmental data, enabling more multifaceted surveillance.

[0070] The surveillance system can further include a behavior prediction unit. The behavior prediction unit can analyze past surveillance camera video data and learn specific behavioral patterns. For example, AI can analyze past video data and learn behavioral patterns that frequently occur in specific locations at specific times of the day. This allows the behavior prediction unit to predict future behavior based on the learned behavioral patterns and issue a warning before abnormal behavior occurs. For example, if suspicious behavior is predicted in a specific location at a specific time of day, vigilance can be strengthened in advance. This allows the surveillance system to utilize past data to predict future behavior and achieve more effective surveillance.

[0071] The surveillance system can further include an emergency response department. When the reporting department detects suspicious behavior or a crime, the emergency response department can immediately issue instructions to respond to the scene. For example, the emergency response department can automatically contact the police or a security company and instruct them to rush to the scene. The emergency response department can also propose appropriate response measures depending on the situation at the scene. For example, in the event of a fire, it can issue instructions for firefighting activities. Furthermore, the emergency response department can monitor the situation at the scene in real time and check the progress of the response. This allows the surveillance system to achieve quick and appropriate emergency response.

[0072] The surveillance system can further use the emotion estimation function to analyze the emotions of people in the video and detect abnormal emotional states. For example, AI can analyze the facial expressions and movements of people in the video and use the emotion estimation function to infer their emotions. For example, AI can detect abnormal emotional states such as anger or fear from facial expressions and body movements, and report if an abnormal emotional state is confirmed. This allows the surveillance system to use the emotion estimation function to detect abnormal emotional states and respond quickly.

[0073] The surveillance system can further use an emotion estimation function to automatically adjust the settings of the surveillance cameras based on the user's emotions. For example, AI analyzes the user's facial expressions and voice and estimates their emotions using the emotion estimation function. For example, if the user is nervous, the sensitivity of the surveillance cameras can be increased. On the other hand, if the user is relaxed, the sensitivity of the surveillance cameras can be decreased. This allows the surveillance system to automatically adjust the settings of the surveillance cameras based on the user's emotions and achieve more effective surveillance.

[0074] The monitoring system can further use an emotion estimation function to adjust the priority of reports based on the user's emotions. For example, AI analyzes the user's facial expressions and voice and uses the emotion estimation function to estimate their emotions. For example, if the user is feeling very anxious, the priority of the report can be increased. On the other hand, if the user is calm, the priority of the report can be decreased. This allows the monitoring system to adjust the priority of reports based on the user's emotions and achieve more appropriate responses.

[0075] The monitoring system can further use an emotion estimation function to filter search results based on the user's emotions. For example, AI can analyze the user's facial expressions and voice and use the emotion estimation function to estimate their emotions. For example, if the user is feeling stressed, search results can be displayed in a concise manner. On the other hand, if the user is relaxed, detailed search results can be displayed. This allows the monitoring system to filter search results based on the user's emotions and provide a more user-friendly interface.

[0076] The monitoring system can further use an emotion estimation function to adjust the content of notifications based on the user's emotions. For example, AI can analyze the user's facial expressions and voice and use the emotion estimation function to estimate their emotions. For example, if the user is nervous, the notification content can be simplified and only important information can be conveyed. On the other hand, if the user is relaxed, a notification containing detailed information can be sent. This allows the monitoring system to adjust the content of notifications based on the user's emotions and achieve more effective information communication.

[0077] The surveillance system can further include an abnormal behavior prediction unit. The abnormal behavior prediction unit can analyze past surveillance camera video data and learn abnormal behavior patterns. For example, AI can analyze past video data and learn abnormal behavior patterns that frequently occur in specific locations during specific times of day. This allows the abnormal behavior prediction unit to predict future abnormal behavior based on the learned abnormal behavior patterns and issue a warning before abnormal behavior occurs. For example, if abnormal behavior is predicted in a specific location during a specific time of day, vigilance can be strengthened in advance. This allows the surveillance system to utilize past data to predict future abnormal behavior and achieve more effective surveillance.

[0078] The surveillance system can further include a drone monitoring unit. In addition to analyzing surveillance camera footage, the drone monitoring unit can also monitor a wide area using drones. For example, AI can analyze drone camera footage and monitor a wide area. This allows the surveillance system to not only use fixed cameras but also perform dynamic monitoring using drones, enabling wider-area surveillance. For example, using drones to monitor large-scale events or facilities with large grounds enables quick and effective surveillance. Furthermore, if the drone monitoring unit detects abnormal behavior, it can dispatch a drone to the scene and capture detailed footage. This allows the surveillance system to utilize drones to achieve more flexible and effective surveillance.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The surveillance camera image acquisition unit acquires surveillance camera images. For example, high-resolution cameras or infrared cameras can be used, and images from multiple cameras can be integrated and acquired. Step 2: The real-time monitoring unit monitors the surveillance camera footage acquired by the surveillance camera footage acquisition unit in real time. For example, it uses AI to analyze movements in the footage and detect suspicious behavior or crimes. Step 3: The reporting department immediately reports any suspicious behavior or crime detected by the real-time monitoring department. For example, if the AI ​​detects an act of violence or theft, it will automatically notify the police or security company. Step 4: The storage unit stores any questionable behavior detected by the real-time monitoring unit. For example, if the storage unit detects frequent movement to the same location or an unnatural delivery of a package, it stores the video. Step 5: The search unit searches for the relevant video when the user provides an image. For example, if the user provides an image such as "a person wearing a red hat" or "an event at a specific time and place," the AI ​​will use that information to search for the relevant video and quickly display it.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 monitoring unit that monitors the surveillance camera video acquired by the surveillance camera video acquisition unit in real time; a reporting unit that immediately reports suspicious behavior or crime detected by the real-time monitoring unit; a storage unit for storing the gray activities detected by the real-time monitoring unit; A system characterized by comprising a search unit that searches for relevant images by a user conveying an image.

2. The real-time monitoring unit In addition to analyzing surveillance camera footage, data from surrounding sensors is also integrated to more accurately detect suspicious behavior.

2. The system of claim 1.

3. The storage unit When the aforementioned shady behavior is detected, the video before and after the behavior is automatically saved, making it easier to understand the context of the behavior.

2. The system of claim 1.

4. The search unit When the user searches for a video, natural language processing technology is used to generate a more detailed search query, enabling a more accurate video search.

2. The system of claim 1.

5. The real-time monitoring unit The system estimates emotions from the facial expressions and movements of people in the video, and reports to the reporting department if abnormal stress or tension is detected.

2. The system of claim 1.

Images