System

Abstract

An object of a system according to an embodiment is to quickly identify a place where a suspect or a wanted criminal is hiding or a place where the suspect or the wanted criminal frequently stops by.SOLUTION: A system includes a face recognition unit, a collation unit, an identification unit, and a network construction unit. The face recognition unit memorizes a face of a suspect or a wanted criminal. The collation unit collates the face memorized by the face recognition unit with the security camera image. The specification unit specifies a hiding place or a place to be frequently dropped in based on the information obtained by the collation unit. The network construction unit constructs a security camera network by utilizing a network.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 made it difficult to identify the hiding places and frequent stops of suspects and wanted criminals, resulting in the time it takes to solve cases.

[0005] The system according to the embodiment aims to quickly identify hiding places and frequented places of suspects and wanted criminals. [Means for solving the problem]

[0006] The system according to the embodiment includes a face recognition unit, a matching unit, an identification unit, and a network construction unit. The face recognition unit memorizes the faces of suspects and wanted criminals. The matching unit compares the faces memorized by the face recognition unit with security camera footage. The identification unit identifies hiding places and frequent locations based on the information obtained by the matching unit. The network construction unit utilizes the network to construct a security camera network. [Effects of the Invention]

[0007] The system according to the embodiment can quickly identify hiding places and frequented places of suspects and wanted criminals. [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 crime prevention system according to the embodiment of the present invention memorizes the faces of suspects and wanted criminals, compares them with images from security cameras installed in various locations, and identifies their hiding places and frequent stops. This makes the crime prevention system useful for the early detection and arrest of suspects and wanted criminals, the early detection of missing persons, and crime prevention.

[0029] A security system according to an embodiment includes a face recognition unit, a matching unit, an identification unit, and a network construction unit. The face recognition unit memorizes the faces of suspects and wanted criminals. For example, the generation AI memorizes the faces of suspects based on facial photographs of suspects provided by the police. The face recognition unit can also memorize the faces of missing persons. For example, the generation AI memorizes the faces of missing persons based on facial photographs of the missing persons. The matching unit compares the faces memorized by the face recognition unit with security camera footage. For example, it analyzes footage from security cameras installed throughout the city in real time to determine whether the faces match those of the suspects. The matching unit can also compare the faces of the missing person with security camera footage. For example, it determines whether the faces match those of the missing person. The identification unit identifies hiding places and frequent stops based on information obtained by the matching unit. For example, it analyzes footage from multiple security cameras to identify the suspect's travel routes. The identification unit can also identify the missing person's travel routes. For example, it analyzes the missing person's travel routes. The network construction unit utilizes the company's own network to construct a security camera network. For example, security cameras could be installed every 0.5 to 1 km nationwide to provide constant video recording. The network construction unit could also develop services that provide various types of video information. For example, this could be useful for background checks, protection of elderly wanderers, and crime prevention, such as nuisance and reckless driving, and snatch theft. As a result, the security system according to the embodiment can be useful for the early detection and arrest of suspects and wanted criminals, the early detection of missing persons, and crime prevention. For example, identifying the suspect's route of movement allows the police to respond quickly. Furthermore, utilizing a security camera network can improve the safety of local areas.

[0030] The face recognition unit can include past behavioral patterns and characteristic movements in the facial recognition data of a suspect. For example, the face recognition unit adds past behavioral patterns to the facial recognition data of a suspect. For example, behavioral history in specific locations and behavioral patterns during specific time periods are registered in a database and combined with facial recognition. The face recognition unit can also include characteristic movements. For example, a specific walking style or hand movements are registered in a database and combined with facial recognition. This allows for more accurate recognition.

[0031] The face recognition unit can add images taken under different lighting conditions and angles to the facial recognition data of a suspect. For example, the face recognition unit adds facial images of a suspect taken under different lighting conditions to the database. For example, facial images taken in various environments, such as daytime and nighttime, indoors and outdoors, are collected to improve recognition accuracy. The face recognition unit can also add images taken from different angles. For example, facial images taken from various angles, such as from the front, side, and diagonal, are collected to improve recognition accuracy. This improves the versatility of recognition.

[0032] The facial recognition unit can combine the facial recognition data of a suspect with other biometric authentication data to perform multi-factor authentication. For example, the facial recognition unit can combine fingerprint data with the facial recognition data of a suspect. For example, facial recognition and fingerprint authentication can be performed simultaneously to improve recognition accuracy. The facial recognition unit can also combine voiceprint data. For example, facial recognition and voiceprint authentication can be performed simultaneously to improve recognition accuracy. The facial recognition unit can also combine iris data. For example, facial recognition and iris authentication can be performed simultaneously to improve recognition accuracy. This improves recognition accuracy.

[0033] The facial recognition unit can apply the facial recognition data of a suspect to recognition on different devices. For example, the facial recognition unit applies the facial recognition data of a suspect to recognition on a smartphone. For example, a police officer uses a smartphone to recognize the suspect's face in real time. The facial recognition unit can also be applied to recognition on a drone. For example, a drone can be used to recognize the suspect's face from the air. The facial recognition unit can also be applied to recognition on a surveillance camera. For example, a surveillance camera can be used to recognize the suspect's face in real time. This improves the versatility of recognition.

[0034] The matching unit can combine the analysis of security camera footage with audio data to confirm a match between the voice and face of a suspect. The matching unit, for example, combines the analysis of security camera footage with audio data. For example, it simultaneously analyzes video and audio to confirm a match between the voice and face of a suspect. The matching unit can also confirm a match between the voice and face of a suspect based on audio data. For example, it can use voice recognition technology to confirm a match between the voice and face of a suspect. The matching unit can also build a system that confirms a match between the voice and face of a suspect based on audio data. For example, it can use voice recognition technology to confirm a match between the voice and face of a suspect. This confirms a match between the voice and face of a suspect.

[0035] The matching unit can improve recognition accuracy by taking surrounding environmental information into account when analyzing security camera footage. The matching unit, for example, combines surrounding environmental information when analyzing security camera footage. For example, it takes weather and time of day into account to improve recognition accuracy. The matching unit can also improve recognition accuracy based on environmental information. For example, it improves recognition accuracy based on weather and time of day. The matching unit can also build a system that improves recognition accuracy based on environmental information. For example, it improves recognition accuracy based on weather and time of day. This improves recognition accuracy.

[0036] The matching unit can integrate the analysis of security camera footage with other video sources. For example, the matching unit integrates the analysis of security camera footage with other video sources. For example, it analyzes footage from a dashcam or smartphone camera to help identify a suspect. The matching unit can also analyze security camera footage based on other video sources. For example, it analyzes security camera footage based on footage from a dashcam or smartphone camera. The matching unit can also build a system that analyzes security camera footage based on other video sources. For example, it analyzes security camera footage based on footage from a dashcam or smartphone camera. This improves the accuracy of the analysis.

[0037] The identification unit can perform more accurate predictions by combining past crime data and geographic information to identify hiding places. The identification unit, for example, combines past crime data to identify hiding places. For example, it analyzes past crime locations and suspect behavior patterns to predict hiding places. The identification unit can also combine geographic information. For example, it predicts hiding places based on GPS data and map information. The identification unit can also build a system that predicts hiding places based on past crime data and geographic information. For example, it analyzes past crime locations and suspect behavior patterns to predict hiding places. This allows for more accurate predictions.

[0038] The identification unit can analyze the social media activity and communication history of a suspect to identify a hiding place and understand the behavioral pattern. The identification unit, for example, analyzes the social media activity of a suspect to understand the behavioral pattern. For example, it identifies places that the suspect frequently visits based on the content of posts and location information. The identification unit can also analyze communication history. For example, it identifies the behavioral pattern of a suspect based on call history and message history. The identification unit can also build a system that understands the behavioral pattern based on social media activity and communication history. For example, it identifies places that the suspect frequently visits based on the content of posts and location information. In this way, it understands the behavioral pattern.

[0039] The identification unit can link the identification of hiding places with other criminal investigation tools. For example, the identification unit can link the identification of hiding places with GPS tracking. For example, it can track the suspect's movement route in real time and identify the hiding place. The identification unit can also link with drone surveillance. For example, it can use a drone to identify the suspect's hiding place from the air. The identification unit can also build a system that links with other criminal investigation tools. For example, it can identify hiding places based on GPS tracking and drone surveillance. In this way, the identification of hiding places can be linked with other criminal investigation tools.

[0040] The Identification Department can share information about identified hiding places with investigative agencies in different regions or countries, and can be applied to international criminal investigations. The Identification Department, for example, shares information about identified hiding places with investigative agencies in different regions or countries. For example, in an international criminal investigation, data is shared with investigative agencies in multiple countries. The Identification Department can also share information with investigative agencies in different regions or countries. For example, in an international criminal investigation, data is shared with investigative agencies in multiple countries. The Identification Department can also build a system for sharing information with investigative agencies in different regions or countries. For example, in an international criminal investigation, data is shared with investigative agencies in multiple countries. This can be applied to international criminal investigations.

[0041] The face recognition unit can include past behavioral patterns and characteristic movements in the facial recognition data of a missing person. For example, the face recognition unit adds past behavioral patterns to the facial recognition data of a missing person. For example, behavioral history in specific locations and behavioral patterns during specific time periods can be registered in a database and combined with facial recognition. The face recognition unit can also include characteristic movements. For example, a specific way of walking or hand movements can be registered in a database and combined with facial recognition. This enables more accurate searches.

[0042] The face recognition unit can add images taken under different lighting conditions and angles to the facial recognition data of a missing person. For example, the face recognition unit adds facial images of a missing person taken under different lighting conditions to the database. For example, facial images taken under various environments, such as daytime and nighttime, indoors and outdoors, are collected to improve recognition accuracy. The face recognition unit can also add images taken from different angles. For example, facial images taken from various angles, such as from the front, side, and diagonal, are collected to improve recognition accuracy. This improves the versatility of recognition.

[0043] The face recognition unit can combine the face recognition data of the missing person with other biometric authentication data to perform multi-factor authentication. For example, the face recognition unit can combine the face recognition data of the missing person with fingerprint data. For example, face recognition and fingerprint authentication can be performed simultaneously to improve recognition accuracy. The face recognition unit can also combine voiceprint data. For example, face recognition and voiceprint authentication can be performed simultaneously to improve recognition accuracy. The face recognition unit can also combine iris data. For example, face recognition and iris authentication can be performed simultaneously to improve recognition accuracy. This improves recognition accuracy.

[0044] The face recognition unit can apply the facial recognition data of a missing person to recognition on different devices. For example, the face recognition unit applies the facial recognition data of a missing person to recognition on a smartphone. For example, a police officer uses a smartphone to recognize the face of a missing person in real time. The face recognition unit can also be applied to recognition on a drone. For example, a drone can be used to recognize the face of a missing person from the air. The face recognition unit can also be applied to recognition on a surveillance camera. For example, a surveillance camera can be used to recognize the face of a missing person in real time. This improves the versatility of recognition.

[0045] The network construction unit can use highly energy-efficient solar panels to construct the security camera network, thereby achieving sustainable operation. The network construction unit, for example, uses highly energy-efficient solar panels to construct the security camera network. For example, a solar panel can be installed on each camera, and power can be supplied using solar energy. The network construction unit can also utilize renewable energy to achieve sustainable operation. For example, power can be supplied by combining wind power and hydroelectric power. The network construction unit can also construct a system to achieve sustainable operation. For example, highly energy-efficient solar panels can be used to utilize renewable energy. This achieves sustainable operation.

[0046] The network construction unit can introduce wireless communication technology into the construction of a security camera network, thereby improving the flexibility of installation locations. The network construction unit, for example, introduces wireless communication technology into the construction of a security camera network. For example, wireless communication technology such as Wi-Fi or LoRa is used to connect cameras to a central monitoring system. The network construction unit can also utilize wireless communication technology to improve the flexibility of installation locations. For example, even if there are restrictions on installation locations, wireless communication technology can be used to flexibly accommodate them. The network construction unit can also build a system that uses wireless communication technology to improve the flexibility of installation locations. For example, wireless communication technology such as Wi-Fi or LoRa is used to connect cameras to a central monitoring system. This improves the flexibility of installation locations.

[0047] The network construction unit can integrate the security camera network with other security systems. The network construction unit, for example, integrates the security camera network with other security systems. For example, it can work in conjunction with an intrusion detection system or a fire alarm to realize comprehensive security measures. The network construction unit can also integrate with other security systems. For example, it can work in conjunction with an intrusion detection system or a fire alarm to realize comprehensive security measures. The network construction unit can also build a system that integrates with other security systems. For example, it can work in conjunction with an intrusion detection system or a fire alarm to realize comprehensive security measures. This allows integration with other security systems.

[0048] The network construction unit can link the security camera network with security camera networks in different regions or countries, thereby enabling application to international security measures. The network construction unit, for example, links the security camera network with security camera networks in different regions or countries. For example, in international security measures, security camera footage from multiple countries is analyzed. The network construction unit can also link with security camera networks in different regions or countries. For example, in international security measures, security camera footage from multiple countries is analyzed. The network construction unit can also build a system that links security camera networks in different regions or countries. For example, in international security measures, security camera footage from multiple countries is analyzed. This allows application to international security measures.

[0049] The network construction unit can introduce data anonymization technology into the video information provision service to protect user privacy. The network construction unit introduces data anonymization technology into the video information provision service, for example. For example, it uses technology that automatically blurs the faces of people and vehicle license plates in the video. The network construction unit can also utilize data anonymization technology to protect user privacy. For example, it uses data masking or pseudo-anonymization to protect user privacy. The network construction unit can also build a system that protects user privacy using data anonymization technology. For example, it uses technology that automatically blurs the faces of people and vehicle license plates in the video. This protects user privacy.

[0050] The network construction unit can add a customization function to the video information provision service according to the user's needs, thereby improving convenience. The network construction unit, for example, adds a customization function to the video information provision service. For example, it provides a function to preferentially display video from an area or time period in which the user is interested. The network construction unit can also add a customization function according to the user's needs. For example, it can change the user interface or add functions to improve convenience. The network construction unit can also build a system that improves convenience using the customization function. For example, it can provide a function to preferentially display video from an area or time period in which the user is interested. This improves convenience.

[0051] The network construction unit can integrate the video information provision service with other digital services. The network construction unit, for example, integrates the video information provision service with other digital services. For example, it works in conjunction with a smart home system to strengthen security within the home. The network construction unit can also integrate with other digital services. For example, it works in conjunction with a healthcare system to support health management. The network construction unit can also build a system that integrates with other digital services. For example, it works in conjunction with a smart home system and a healthcare system to provide a comprehensive service. This allows integration with other digital services.

[0052] The network construction unit can provide a video information service to users in different regions and countries, thereby expanding into a global market. The network construction unit, for example, provides a video information service to users in different regions and countries. For example, a multilingual interface can be introduced to expand into a global market. The network construction unit can also provide services tailored to the needs of each region in order to expand into a global market. For example, a service can be provided that meets the crime trends and crime prevention needs of each region. The network construction unit can also build a system for expanding into a global market. For example, a multilingual interface can be introduced to provide services tailored to the needs of each region. This allows for expansion into a global market.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The security system can further include a voice recognition unit. The voice recognition unit can memorize the voice of a suspect, for example. For example, the generation AI memorizes the voice of a suspect based on voice data of the suspect provided by the police. The voice recognition unit can also memorize the voice of a missing person. For example, the generation AI memorizes the voice based on the voice data of the missing person. This allows the voice recognition unit to compare security camera footage with the voice data and help identify suspects and missing persons. For example, it can analyze the footage and audio from security cameras installed around town in real time and determine whether the voice matches that of a suspect. The voice recognition unit can also compare the voice of a missing person with security camera footage. For example, it determines whether the voice matches that of a missing person. This enables more accurate recognition using voice data.

[0055] The crime prevention system may further include a behavior prediction unit. The behavior prediction unit may predict the next behavior of a suspect based on, for example, the past behavior patterns of the suspect. For example, it may analyze the behavior history in a specific location or the behavior pattern during a specific time period, and predict the location the suspect is likely to visit next. The behavior prediction unit may also predict the next behavior of a missing person based on the behavior pattern of the missing person. For example, it may analyze the locations the missing person has visited in the past or the behavior pattern during a specific time period, and predict the location the missing person is likely to visit next. This may be useful for the early detection of suspects and missing persons.

[0056] The security system can further include an abnormal behavior detection unit. The abnormal behavior detection unit can, for example, analyze the movements of people in security camera footage and detect abnormal behavior. For example, it can detect a sudden run or unnatural movement in a specific area and issue an alarm. The abnormal behavior detection unit can also be used to search for missing people. For example, if a missing person exhibits abnormal behavior in a specific area, a search can be carried out based on that information. This allows for early detection of abnormal behavior and a prompt response.

[0057] The security system may further include a biometric authentication unit. The biometric authentication unit may perform authentication based on, for example, the fingerprint or iris data of a suspect. For example, by combining face recognition and fingerprint authentication, recognition accuracy may be improved. The biometric authentication unit may also perform authentication based on the fingerprint or iris data of a missing person. For example, by combining face recognition and iris authentication, recognition accuracy may be improved. This allows for multi-factor authentication that combines multiple biometric authentication data, improving recognition accuracy.

[0058] The security system can further include a drone monitoring unit. The drone monitoring unit can, for example, use drones to monitor a wide area. For example, it can monitor a suspect's hiding place from the air and analyze the video in real time. The drone monitoring unit can also be used to search for missing persons. For example, if a missing person is moving over a wide area, a drone can be used to efficiently search for them. This enables wide-area monitoring and search, which can be useful for early detection.

[0059] The security system can further include an environmental information analysis unit. The environmental information analysis unit can, for example, combine the analysis of security camera footage with information about the surrounding environment. For example, it can take into account the weather, time of day, and lighting conditions to improve recognition accuracy. The environmental information analysis unit can also be applied to searches for missing persons. For example, if a missing person is moving under specific environmental conditions, a search can be carried out based on that information. This enables more accurate recognition and searches by utilizing environmental information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The facial recognition unit memorizes the faces of suspects and wanted criminals. For example, the generation AI memorizes the faces of suspects based on photos provided by the police. It can also memorize the faces of missing persons. Step 2: The matching unit matches the face memorized by the face recognition unit with security camera footage. For example, it can analyze security camera footage installed around town in real time and determine whether it matches the face of a suspect. It can also match the face of a missing person with security camera footage. Step 3: The identification unit identifies hiding places and frequent stops based on the information obtained by the matching unit. For example, it can analyze footage from multiple security cameras to identify the suspect's movement route. It can also identify the movement route of a missing person. Step 4: The Network Construction Department uses their company's network to build a security camera network. For example, security cameras could be installed every 0.5 to 1 km across the country, providing constant video recording. They could also develop a service that provides various types of video information.

[0062] (Example 2) The crime prevention system according to the embodiment of the present invention memorizes the faces of suspects and wanted criminals, compares them with images from security cameras installed in various locations, and identifies their hiding places and frequent stops. This makes the crime prevention system useful for the early detection and arrest of suspects and wanted criminals, the early detection of missing persons, and crime prevention.

[0063] A security system according to an embodiment includes a face recognition unit, a matching unit, an identification unit, and a network construction unit. The face recognition unit memorizes the faces of suspects and wanted criminals. For example, the generation AI memorizes the faces of suspects based on facial photographs of suspects provided by the police. The face recognition unit can also memorize the faces of missing persons. For example, the generation AI memorizes the faces of missing persons based on facial photographs of the missing persons. The matching unit compares the faces memorized by the face recognition unit with security camera footage. For example, it analyzes footage from security cameras installed throughout the city in real time to determine whether the faces match those of the suspects. The matching unit can also compare the faces of the missing person with security camera footage. For example, it determines whether the faces match those of the missing person. The identification unit identifies hiding places and frequent stops based on information obtained by the matching unit. For example, it analyzes footage from multiple security cameras to identify the suspect's travel routes. The identification unit can also identify the missing person's travel routes. For example, it analyzes the missing person's travel routes. The network construction unit utilizes the company's own network to construct a security camera network. For example, security cameras could be installed every 0.5 to 1 km nationwide to provide constant video recording. The network construction unit could also develop services that provide various types of video information. For example, this could be useful for background checks, protection of elderly wanderers, and crime prevention, such as nuisance and reckless driving, and snatch theft. As a result, the security system according to the embodiment can be useful for the early detection and arrest of suspects and wanted criminals, the early detection of missing persons, and crime prevention. For example, identifying the suspect's route of movement allows the police to respond quickly. Furthermore, utilizing a security camera network can improve the safety of local areas.

[0064] The face recognition unit can include past behavioral patterns and characteristic movements in the facial recognition data of a suspect. For example, the face recognition unit adds past behavioral patterns to the facial recognition data of a suspect. For example, behavioral history in specific locations and behavioral patterns during specific time periods are registered in a database and combined with facial recognition. The face recognition unit can also include characteristic movements. For example, a specific walking style or hand movements are registered in a database and combined with facial recognition. This allows for more accurate recognition.

[0065] The face recognition unit can add images taken under different lighting conditions and angles to the facial recognition data of a suspect. For example, the face recognition unit adds facial images of a suspect taken under different lighting conditions to the database. For example, facial images taken in various environments, such as daytime and nighttime, indoors and outdoors, are collected to improve recognition accuracy. The face recognition unit can also add images taken from different angles. For example, facial images taken from various angles, such as from the front, side, and diagonal, are collected to improve recognition accuracy. This improves the versatility of recognition.

[0066] The face recognition unit can use an emotion estimation function to estimate emotions from a photograph of a suspect's face and prioritize recognition of suspects in a specific emotional state. The face recognition unit adds, for example, a function to estimate emotions from a photograph of a suspect's face. For example, emotions such as anger or sadness can be analyzed, and suspects in a specific emotional state can be prioritized for recognition. The face recognition unit can also use the emotion estimation function to prioritize recognition of suspects in a specific emotional state. For example, priorities can be set based on emotion scores, and suspects in a specific emotional state can be prioritized for recognition. This allows suspects in a specific emotional state to be prioritized for recognition.

[0067] The facial recognition unit can combine the facial recognition data of a suspect with other biometric authentication data to perform multi-factor authentication. For example, the facial recognition unit can combine fingerprint data with the facial recognition data of a suspect. For example, facial recognition and fingerprint authentication can be performed simultaneously to improve recognition accuracy. The facial recognition unit can also combine voiceprint data. For example, facial recognition and voiceprint authentication can be performed simultaneously to improve recognition accuracy. The facial recognition unit can also combine iris data. For example, facial recognition and iris authentication can be performed simultaneously to improve recognition accuracy. This improves recognition accuracy.

[0068] The facial recognition unit can apply the facial recognition data of a suspect to recognition on different devices. For example, the facial recognition unit applies the facial recognition data of a suspect to recognition on a smartphone. For example, a police officer uses a smartphone to recognize the suspect's face in real time. The facial recognition unit can also be applied to recognition on a drone. For example, a drone can be used to recognize the suspect's face from the air. The facial recognition unit can also be applied to recognition on a surveillance camera. For example, a surveillance camera can be used to recognize the suspect's face in real time. This improves the versatility of recognition.

[0069] The face recognition unit can use the emotion estimation function to detect people in a specific emotional state in real time based on the facial recognition data of a suspect. The face recognition unit, for example, uses the emotion estimation function to build a system that detects people in a specific emotional state in real time from the facial recognition data of a suspect. For example, people who show expressions of anger or impatience are preferentially detected. The face recognition unit can also use the emotion estimation function to detect people in a specific emotional state in real time. For example, a priority is set based on the emotion score, and people in a specific emotional state are preferentially detected. In this way, people in a specific emotional state are detected in real time.

[0070] The matching unit can combine the analysis of security camera footage with audio data to confirm a match between the voice and face of a suspect. The matching unit, for example, combines the analysis of security camera footage with audio data. For example, it simultaneously analyzes video and audio to confirm a match between the voice and face of a suspect. The matching unit can also confirm a match between the voice and face of a suspect based on audio data. For example, it can use voice recognition technology to confirm a match between the voice and face of a suspect. The matching unit can also build a system that confirms a match between the voice and face of a suspect based on audio data. For example, it can use voice recognition technology to confirm a match between the voice and face of a suspect. This confirms a match between the voice and face of a suspect.

[0071] The matching unit can improve recognition accuracy by taking surrounding environmental information into account when analyzing security camera footage. The matching unit, for example, combines surrounding environmental information when analyzing security camera footage. For example, it takes weather and time of day into account to improve recognition accuracy. The matching unit can also improve recognition accuracy based on environmental information. For example, it improves recognition accuracy based on weather and time of day. The matching unit can also build a system that improves recognition accuracy based on environmental information. For example, it improves recognition accuracy based on weather and time of day. This improves recognition accuracy.

[0072] The matching unit can use the emotion estimation function to analyze the emotional state of a person in security camera footage and detect abnormal behavior. The matching unit, for example, uses the emotion estimation function to analyze the emotional state of a person in security camera footage. For example, it identifies a person showing expressions of anger or fear and detects abnormal behavior. The matching unit can also use the emotion estimation function to detect abnormal behavior. For example, it detects abnormal behavior based on an emotion score. The matching unit can also use the emotion estimation function to build a system for detecting abnormal behavior. For example, it detects abnormal behavior based on an emotion score. This allows abnormal behavior to be detected.

[0073] The matching unit can integrate the analysis of security camera footage with other video sources. For example, the matching unit integrates the analysis of security camera footage with other video sources. For example, it analyzes footage from a dashcam or smartphone camera to help identify a suspect. The matching unit can also analyze security camera footage based on other video sources. For example, it analyzes security camera footage based on footage from a dashcam or smartphone camera. The matching unit can also build a system that analyzes security camera footage based on other video sources. For example, it analyzes security camera footage based on footage from a dashcam or smartphone camera. This improves the accuracy of the analysis.

[0074] The matching unit can use the emotion estimation function to monitor the emotional state of people in security camera footage in real time and predict abnormal behavior. The matching unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of people in security camera footage in real time. For example, it identifies people who show expressions of anger or fear and predicts abnormal behavior. The matching unit can also use the emotion estimation function to predict abnormal behavior. For example, it predicts abnormal behavior based on an emotion score. The matching unit can also use the emotion estimation function to build a system that predicts abnormal behavior. For example, it predicts abnormal behavior based on an emotion score. This allows abnormal behavior to be predicted.

[0075] The identification unit can perform more accurate predictions by combining past crime data and geographic information to identify hiding places. The identification unit, for example, combines past crime data to identify hiding places. For example, it analyzes past crime locations and suspect behavior patterns to predict hiding places. The identification unit can also combine geographic information. For example, it predicts hiding places based on GPS data and map information. The identification unit can also build a system that predicts hiding places based on past crime data and geographic information. For example, it analyzes past crime locations and suspect behavior patterns to predict hiding places. This allows for more accurate predictions.

[0076] The identification unit can analyze the social media activity and communication history of a suspect to identify a hiding place and understand the behavioral pattern. The identification unit, for example, analyzes the social media activity of a suspect to understand the behavioral pattern. For example, it identifies places that the suspect frequently visits based on the content of posts and location information. The identification unit can also analyze communication history. For example, it identifies the behavioral pattern of a suspect based on call history and message history. The identification unit can also build a system that understands the behavioral pattern based on social media activity and communication history. For example, it identifies places that the suspect frequently visits based on the content of posts and location information. In this way, it understands the behavioral pattern.

[0077] The identification unit can use the emotion estimation function to preferentially identify locations in a specific emotional state based on the emotional state of the suspect. The identification unit, for example, uses the emotion estimation function to identify a hiding location based on the emotional state of the suspect. For example, it preferentially identifies locations where emotions of tension or anxiety are strong. The identification unit can also use the emotion estimation function to preferentially identify locations where emotions are strong. For example, it sets priorities based on emotion scores and preferentially identifies locations where emotions are strong. The identification unit can also use the emotion estimation function to build a system that preferentially identifies locations where emotions are strong. For example, it preferentially identifies locations where emotions are strong. In this way, it preferentially identifies locations where emotions are strong.

[0078] The identification unit can link the identification of hiding places with other criminal investigation tools. For example, the identification unit can link the identification of hiding places with GPS tracking. For example, it can track the suspect's movement route in real time and identify the hiding place. The identification unit can also link with drone surveillance. For example, it can use a drone to identify the suspect's hiding place from the air. The identification unit can also build a system that links with other criminal investigation tools. For example, it can identify hiding places based on GPS tracking and drone surveillance. In this way, the identification of hiding places can be linked with other criminal investigation tools.

[0079] The Identification Department can share information about identified hiding places with investigative agencies in different regions or countries, and can be applied to international criminal investigations. The Identification Department, for example, shares information about identified hiding places with investigative agencies in different regions or countries. For example, in an international criminal investigation, data is shared with investigative agencies in multiple countries. The Identification Department can also share information with investigative agencies in different regions or countries. For example, in an international criminal investigation, data is shared with investigative agencies in multiple countries. The Identification Department can also build a system for sharing information with investigative agencies in different regions or countries. For example, in an international criminal investigation, data is shared with investigative agencies in multiple countries. This can be applied to international criminal investigations.

[0080] The identification unit can use the emotion estimation function to monitor locations where the suspect is in a specific emotional state in real time based on the suspect's emotional state. The identification unit, for example, uses the emotion estimation function to build a system that monitors hiding places in real time based on the suspect's emotional state. For example, it can focus on monitoring locations where emotions of tension or anxiety are strong. The identification unit can also use the emotion estimation function to monitor locations where the suspect is in a specific emotional state in real time. For example, it can set priorities based on emotion scores and monitor locations where the suspect is in a specific emotional state in real time. The identification unit can also use the emotion estimation function to build a system that monitors locations where the suspect is in a specific emotional state in real time. For example, it can focus on monitoring locations where emotions of tension or anxiety are strong. In this way, it can monitor locations where the suspect is in a specific emotional state in real time.

[0081] The face recognition unit can include past behavioral patterns and characteristic movements in the facial recognition data of a missing person. For example, the face recognition unit adds past behavioral patterns to the facial recognition data of a missing person. For example, behavioral history in specific locations and behavioral patterns during specific time periods can be registered in a database and combined with facial recognition. The face recognition unit can also include characteristic movements. For example, a specific way of walking or hand movements can be registered in a database and combined with facial recognition. This enables more accurate searches.

[0082] The face recognition unit can add images taken under different lighting conditions and angles to the facial recognition data of a missing person. For example, the face recognition unit adds facial images of a missing person taken under different lighting conditions to the database. For example, facial images taken under various environments, such as daytime and nighttime, indoors and outdoors, are collected to improve recognition accuracy. The face recognition unit can also add images taken from different angles. For example, facial images taken from various angles, such as from the front, side, and diagonal, are collected to improve recognition accuracy. This improves the versatility of recognition.

[0083] The face recognition unit can use an emotion estimation function to estimate emotions from facial photographs of missing persons, and prioritize searches for missing persons who are in a specific emotional state. The face recognition unit adds, for example, a function to estimate emotions from facial photographs of missing persons. For example, emotions such as sadness or anxiety are analyzed, and a search for missing persons who are in a specific emotional state is prioritized. The face recognition unit can also use the emotion estimation function to prioritize searches for missing persons who are in a specific emotional state. For example, priorities are set based on emotion scores, and a search for missing persons who are in a specific emotional state is prioritized. This allows a search for missing persons who are in a specific emotional state to be prioritized.

[0084] The face recognition unit can combine the face recognition data of the missing person with other biometric authentication data to perform multi-factor authentication. For example, the face recognition unit can combine the face recognition data of the missing person with fingerprint data. For example, face recognition and fingerprint authentication can be performed simultaneously to improve recognition accuracy. The face recognition unit can also combine voiceprint data. For example, face recognition and voiceprint authentication can be performed simultaneously to improve recognition accuracy. The face recognition unit can also combine iris data. For example, face recognition and iris authentication can be performed simultaneously to improve recognition accuracy. This improves recognition accuracy.

[0085] The face recognition unit can apply the facial recognition data of a missing person to recognition on different devices. For example, the face recognition unit applies the facial recognition data of a missing person to recognition on a smartphone. For example, a police officer uses a smartphone to recognize the face of a missing person in real time. The face recognition unit can also be applied to recognition on a drone. For example, a drone can be used to recognize the face of a missing person from the air. The face recognition unit can also be applied to recognition on a surveillance camera. For example, a surveillance camera can be used to recognize the face of a missing person in real time. This improves the versatility of recognition.

[0086] The network construction unit can use highly energy-efficient solar panels to construct the security camera network, thereby achieving sustainable operation. The network construction unit, for example, uses highly energy-efficient solar panels to construct the security camera network. For example, a solar panel can be installed on each camera, and power can be supplied using solar energy. The network construction unit can also utilize renewable energy to achieve sustainable operation. For example, power can be supplied by combining wind power and hydroelectric power. The network construction unit can also construct a system to achieve sustainable operation. For example, highly energy-efficient solar panels can be used to utilize renewable energy. This achieves sustainable operation.

[0087] The network construction unit can introduce wireless communication technology into the construction of a security camera network, thereby improving the flexibility of installation locations. The network construction unit, for example, introduces wireless communication technology into the construction of a security camera network. For example, wireless communication technology such as Wi-Fi or LoRa is used to connect cameras to a central monitoring system. The network construction unit can also utilize wireless communication technology to improve the flexibility of installation locations. For example, even if there are restrictions on installation locations, wireless communication technology can be used to flexibly accommodate them. The network construction unit can also build a system that uses wireless communication technology to improve the flexibility of installation locations. For example, wireless communication technology such as Wi-Fi or LoRa is used to connect cameras to a central monitoring system. This improves the flexibility of installation locations.

[0088] The network construction unit can use the emotion estimation function to analyze video data from a security camera network and detect abnormal behavior in real time. The network construction unit, for example, uses the emotion estimation function to analyze video data from a security camera network. For example, it analyzes facial expressions of people in the video and detects abnormal behavior in real time. The network construction unit can also use the emotion estimation function to detect abnormal behavior in real time. For example, it detects abnormal behavior based on an emotion score. The network construction unit can also use the emotion estimation function to build a system that detects abnormal behavior in real time. For example, it detects abnormal behavior based on an emotion score. In this way, abnormal behavior is detected in real time.

[0089] The network construction unit can integrate the security camera network with other security systems. The network construction unit, for example, integrates the security camera network with other security systems. For example, it can work in conjunction with an intrusion detection system or a fire alarm to realize comprehensive security measures. The network construction unit can also integrate with other security systems. For example, it can work in conjunction with an intrusion detection system or a fire alarm to realize comprehensive security measures. The network construction unit can also build a system that integrates with other security systems. For example, it can work in conjunction with an intrusion detection system or a fire alarm to realize comprehensive security measures. This allows integration with other security systems.

[0090] The network construction unit can link the security camera network with security camera networks in different regions or countries, thereby enabling application to international security measures. The network construction unit, for example, links the security camera network with security camera networks in different regions or countries. For example, in international security measures, security camera footage from multiple countries is analyzed. The network construction unit can also link with security camera networks in different regions or countries. For example, in international security measures, security camera footage from multiple countries is analyzed. The network construction unit can also build a system that links security camera networks in different regions or countries. For example, in international security measures, security camera footage from multiple countries is analyzed. This allows application to international security measures.

[0091] The network construction unit can use the emotion estimation function to monitor video data from a security camera network in real time and predict abnormal behavior. The network construction unit, for example, uses the emotion estimation function to construct a system that monitors video data from a security camera network in real time. For example, a person showing expressions of anger or fear is identified and abnormal behavior is predicted. The network construction unit can also use the emotion estimation function to predict abnormal behavior. For example, abnormal behavior is predicted based on an emotion score. The network construction unit can also use the emotion estimation function to construct a system that predicts abnormal behavior. For example, abnormal behavior is predicted based on an emotion score. This allows abnormal behavior to be predicted.

[0092] The network construction unit can introduce data anonymization technology into the video information provision service to protect user privacy. The network construction unit introduces data anonymization technology into the video information provision service, for example. For example, it uses technology that automatically blurs the faces of people and vehicle license plates in the video. The network construction unit can also utilize data anonymization technology to protect user privacy. For example, it uses data masking or pseudo-anonymization to protect user privacy. The network construction unit can also build a system that protects user privacy using data anonymization technology. For example, it uses technology that automatically blurs the faces of people and vehicle license plates in the video. This protects user privacy.

[0093] The network construction unit can add a customization function to the video information provision service according to the user's needs, thereby improving convenience. The network construction unit, for example, adds a customization function to the video information provision service. For example, it provides a function to preferentially display video from an area or time period in which the user is interested. The network construction unit can also add a customization function according to the user's needs. For example, it can change the user interface or add functions to improve convenience. The network construction unit can also build a system that improves convenience using the customization function. For example, it can provide a function to preferentially display video from an area or time period in which the user is interested. This improves convenience.

[0094] The network construction unit can use the emotion estimation function to provide content based on the emotional state of a user in a video information provision service. The network construction unit, for example, uses the emotion estimation function to construct a system that provides content based on the emotional state of a user. For example, the network construction unit recommends optimal videos based on the user's emotion score. The network construction unit can also use the emotion estimation function to provide content based on the emotional state of a user. For example, the network construction unit recommends optimal videos based on the emotion score. The network construction unit can also use the emotion estimation function to construct a system that provides content based on the emotional state of a user. For example, the network construction unit recommends optimal videos based on the user's emotion score. In this way, content based on the user's emotional state is provided.

[0095] The network construction unit can integrate the video information provision service with other digital services. The network construction unit, for example, integrates the video information provision service with other digital services. For example, it works in conjunction with a smart home system to strengthen security within the home. The network construction unit can also integrate with other digital services. For example, it works in conjunction with a healthcare system to support health management. The network construction unit can also build a system that integrates with other digital services. For example, it works in conjunction with a smart home system and a healthcare system to provide a comprehensive service. This allows integration with other digital services.

[0096] The network construction unit can provide a video information service to users in different regions and countries, thereby expanding into a global market. The network construction unit, for example, provides a video information service to users in different regions and countries. For example, a multilingual interface can be introduced to expand into a global market. The network construction unit can also provide services tailored to the needs of each region in order to expand into a global market. For example, a service can be provided that meets the crime trends and crime prevention needs of each region. The network construction unit can also build a system for expanding into a global market. For example, a multilingual interface can be introduced to provide services tailored to the needs of each region. This allows for expansion into a global market.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The security system can further include a voice recognition unit. The voice recognition unit can memorize the voice of a suspect, for example. For example, the generation AI memorizes the voice of a suspect based on voice data of the suspect provided by the police. The voice recognition unit can also memorize the voice of a missing person. For example, the generation AI memorizes the voice based on the voice data of the missing person. This allows the voice recognition unit to compare security camera footage with the voice data and help identify suspects and missing persons. For example, it can analyze the footage and audio from security cameras installed around town in real time and determine whether the voice matches that of a suspect. The voice recognition unit can also compare the voice of a missing person with security camera footage. For example, it determines whether the voice matches that of a missing person. This enables more accurate recognition using voice data.

[0099] The crime prevention system may further include a behavior prediction unit. The behavior prediction unit may predict the next behavior of a suspect based on, for example, the past behavior patterns of the suspect. For example, it may analyze the behavior history in a specific location or the behavior pattern during a specific time period, and predict the location the suspect is likely to visit next. The behavior prediction unit may also predict the next behavior of a missing person based on the behavior pattern of the missing person. For example, it may analyze the locations the missing person has visited in the past or the behavior pattern during a specific time period, and predict the location the missing person is likely to visit next. This may be useful for the early detection of suspects and missing persons.

[0100] The security system can further include an abnormal behavior detection unit. The abnormal behavior detection unit can, for example, analyze the movements of people in security camera footage and detect abnormal behavior. For example, it can detect a sudden run or unnatural movement in a specific area and issue an alarm. The abnormal behavior detection unit can also be used to search for missing people. For example, if a missing person exhibits abnormal behavior in a specific area, a search can be carried out based on that information. This allows for early detection of abnormal behavior and a prompt response.

[0101] The crime prevention system can also use an emotion estimation function to predict the behavior of a suspect based on the emotional state of the suspect. For example, the emotion of the suspect can be estimated from a photograph of the suspect's face, and if the suspect shows strong feelings of anger or impatience, the system can predict the suspect's next behavior based on that emotional state. The emotion estimation function can also be used to predict the behavior of a missing person based on the emotional state of the suspect. For example, if the emotion of the missing person is estimated from a photograph of the suspect's face, and if the suspect shows strong feelings of anxiety or fear, the system can predict the suspect's next behavior based on that emotional state. This makes it possible to predict behavior based on the suspect's emotional state, which can be useful for early detection.

[0102] The security system may further include a biometric authentication unit. The biometric authentication unit may perform authentication based on, for example, the fingerprint or iris data of a suspect. For example, by combining face recognition and fingerprint authentication, recognition accuracy may be improved. The biometric authentication unit may also perform authentication based on the fingerprint or iris data of a missing person. For example, by combining face recognition and iris authentication, recognition accuracy may be improved. This allows for multi-factor authentication that combines multiple biometric authentication data, improving recognition accuracy.

[0103] The crime prevention system can further use an emotion estimation function to set priorities based on the emotional state of a suspect. For example, if the suspect's emotion is estimated from a facial photograph and strong feelings of anger or impatience are detected, the priority is set based on that emotional state. The emotion estimation function can also be used to set priorities based on the emotional state of a missing person. For example, if the emotion is estimated from a facial photograph and strong feelings of anxiety or fear are detected, the priority is set based on that emotional state. This allows for priority setting based on emotional state, enabling a rapid response.

[0104] The security system can further include a drone monitoring unit. The drone monitoring unit can, for example, use drones to monitor a wide area. For example, it can monitor a suspect's hiding place from the air and analyze the video in real time. The drone monitoring unit can also be used to search for missing persons. For example, if a missing person is moving over a wide area, a drone can be used to efficiently search for them. This enables wide-area monitoring and search, which can be useful for early detection.

[0105] The security system can further use the emotion estimation function to predict abnormal behavior. For example, by analyzing the emotional state of a person in security camera footage, if the person shows strong feelings of anger or fear, abnormal behavior can be predicted based on that emotional state. The emotion estimation function can also be used to predict abnormal behavior of a missing person. For example, by analyzing the emotional state of a missing person, if the person shows strong feelings of anxiety or fear, abnormal behavior can be predicted based on that emotional state. This makes it possible to predict abnormal behavior based on emotional state and respond quickly.

[0106] The security system can further include an environmental information analysis unit. The environmental information analysis unit can, for example, combine the analysis of security camera footage with information about the surrounding environment. For example, it can take into account the weather, time of day, and lighting conditions to improve recognition accuracy. The environmental information analysis unit can also be applied to searches for missing persons. For example, if a missing person is moving under specific environmental conditions, a search can be carried out based on that information. This enables more accurate recognition and searches by utilizing environmental information.

[0107] The crime prevention system can further use the emotion estimation function to identify hiding places based on the emotional state of a suspect. For example, emotions can be estimated from a photograph of the suspect's face, and locations where emotions of tension or anxiety are strong can be identified with priority. The emotion estimation function can also be used to identify hiding places based on the emotional state of a missing person. For example, emotions can be estimated from a photograph of the missing person's face, and locations where emotions of anxiety or fear are strong can be identified with priority. This allows for identification of hiding places based on emotional states, which can be useful for early detection.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The facial recognition unit memorizes the faces of suspects and wanted criminals. For example, the generation AI memorizes the faces of suspects based on photos provided by the police. It can also memorize the faces of missing persons. Step 2: The matching unit matches the face memorized by the face recognition unit with security camera footage. For example, it can analyze security camera footage installed around town in real time and determine whether it matches the face of a suspect. It can also match the face of a missing person with security camera footage. Step 3: The identification unit identifies hiding places and frequent stops based on the information obtained by the matching unit. For example, it can analyze footage from multiple security cameras to identify the suspect's movement route. It can also identify the movement route of a missing person. Step 4: The Network Construction Department uses their company's network to build a security camera network. For example, security cameras could be installed every 0.5 to 1 km across the country, providing constant video recording. They could also develop a service that provides various types of video information.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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]

[0177] 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 facial recognition unit that memorizes the faces of suspects and wanted criminals, a matching unit that matches the face memorized by the face recognition unit with a security camera image; an identification unit that identifies hiding places and frequent stopping places based on the information obtained by the matching unit; A network construction unit that utilizes a network to construct a security camera network. A system characterized by:

2. The face recognition unit Estimating emotions from a facial photograph of the suspect and preferentially recognizing the suspect in a specific emotional state 2. The system of claim 1.

3. The collation unit Combining the analysis of the security camera footage with audio data to confirm a match between the suspect's voice and face 2. The system of claim 1.

4. The identification unit By combining past crime data and geographic information with the identification of hiding places, more accurate predictions can be made.

2. The system of claim 1.

5. The network construction unit The security camera network will be built using energy-efficient solar panels, ensuring sustainable operation.

2. The system of claim 1.

6. The collation unit Analyzing the emotional state of a person in the security camera video and detecting abnormal behavior 2. The system of claim 1.

7. The identification unit and preferentially identifying the locations in a particular emotional state based on the emotional state of the suspect.

2. The system of claim 1.

8. The network construction unit Analyzes video data from the security camera network and detects abnormal behavior in real time.

2. The system of claim 1.

Images