System

Abstract

An object of the system according to the embodiment is to analyze video data of a monitoring camera, detect an abnormality in real time, and issue an alarm.SOLUTION: A system according to an embodiment includes a face authentication unit, an intrusion detection unit, an abnormal behavior detection unit, and an alarm unit. The face authentication unit analyzes video data acquired from the monitoring camera and performs face authentication. The intrusion detection unit analyzes data from the monitoring camera or the sensor and detects an intrusion. The abnormal behavior detection unit analyzes video data acquired from a monitoring camera and detects an abnormal behavior. The alarm unit issues an alarm based on the abnormality detected by the abnormal behavior detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to effectively analyze surveillance camera video data, detect abnormalities in real time, and issue alarms.

[0005] The system according to the embodiment aims to analyze video data from a surveillance camera, detect abnormalities in real time, and issue an alarm. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, an intrusion detection unit, an abnormal behavior detection unit, and an alarm unit. The face authentication unit analyzes video data acquired from a surveillance camera and performs face authentication. The intrusion detection unit analyzes data from the surveillance camera or a sensor and detects intrusions. The abnormal behavior detection unit analyzes video data acquired from the surveillance camera and detects abnormal behavior. The alarm unit issues an alarm based on the abnormality detected by the abnormal behavior detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze video data from a surveillance camera, detect abnormalities in real time, and issue an alarm. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The surveillance system according to the embodiment of the present invention provides functions such as face recognition, intrusion detection, and abnormal behavior detection, enabling real-time monitoring and immediate alerts, thereby enhancing the safety of buildings and public places.

[0029] A surveillance system according to an embodiment includes a face authentication unit, an intrusion detection unit, an abnormal behavior detection unit, and an alarm unit. The face authentication unit analyzes video data acquired from a surveillance camera and performs face authentication. For example, a camera installed at the entrance of a building captures a visitor's face and inputs the video data into a generation AI. The generation AI compares the video data with a pre-registered face database to determine whether the visitor is an authorized individual. The intrusion detection unit analyzes data from the surveillance camera and sensors to detect intrusions. For example, if there is suspicious activity inside a building at night, the generation AI analyzes the activity and determines that there is a possibility of intrusion. The abnormal behavior detection unit analyzes video data acquired from the surveillance camera and detects abnormal behavior. For example, it detects violent acts or suspicious behavior in public places. The alarm unit issues an alarm based on the abnormality detected by the abnormal behavior detection unit. For example, if an intrusion or abnormal behavior is detected, the generation AI issues an alarm based on the information and notifies a security guard or administrator. As a result, the monitoring system according to the embodiment performs face authentication, intrusion detection, and abnormal behavior detection in real time, and issues an alarm immediately, enabling a rapid response.

[0030] The facial recognition unit can improve authentication accuracy by combining facial recognition data with voice recognition and analyzing the degree of match between voice and face. For example, the facial recognition unit uses generative AI to perform voice recognition simultaneously with facial recognition and analyze the degree of match between voice and face. For example, it analyzes the voice data when a user speaks their name and compares it with face data. This improves authentication accuracy by analyzing the degree of match between voice and face.

[0031] The facial recognition unit can refer to the user's past behavioral history and check for any abnormal behavioral patterns. For example, using a generation AI, the facial recognition unit refers to the user's past behavioral history during facial recognition and checks for any abnormal behavioral patterns. For example, if there is a history of unauthorized access in the past, a warning is issued. This makes it possible to check for any abnormal behavioral patterns by referring to the user's past behavioral history.

[0032] The facial recognition unit can integrate the facial recognition function into a smart home system to enhance security within the home. For example, by using generative AI, the facial recognition unit can integrate the facial recognition function into a smart home system to enhance security within the home. For example, a front door camera can authenticate the face of a visitor, allowing only authorized people to open the door. In this way, integrating the unit into a smart home system enhances security within the home.

[0033] The facial recognition unit can improve passenger safety by introducing a facial recognition function into a public transportation ticket gate system. The facial recognition unit can improve passenger safety by introducing a facial recognition function into a public transportation ticket gate system, for example, using generative AI. For example, facial recognition can be performed at a station ticket gate to allow only authorized passengers to pass through. This improves passenger safety by introducing it into a public transportation ticket gate system.

[0034] The intrusion detection unit can analyze the intruder's movement patterns and infer the intruder's intention to intrude. The intrusion detection unit can use, for example, generative AI to analyze the intruder's movement patterns and infer the intruder's intention to intrude. For example, it can analyze the movement captured by a surveillance camera and infer the intruder's purpose in their actions. This allows the intruder's movement patterns to be analyzed and the intruder's intention to intrude to be inferred, thereby improving the accuracy of the alarm.

[0035] The intrusion detection unit can analyze the surrounding environmental sounds when an intrusion is detected, and intensify the alarm if an abnormal sound is detected. The intrusion detection unit can, for example, use a generation AI to analyze the surrounding environmental sounds when an intrusion is detected, and intensify the alarm if an abnormal sound is detected. For example, if the sound of breaking glass or a metallic sound is detected, a high-priority alarm is issued. This improves the accuracy of intrusion detection by analyzing the surrounding environmental sounds and intensifying the alarm if an abnormal sound is detected.

[0036] The intrusion detection unit can refer to past intrusion data when detecting an intrusion and check for similar patterns. For example, using a generation AI, the intrusion detection unit can refer to past intrusion data when detecting an intrusion and check for similar patterns. For example, if an intrusion has been made using the same method in the past, the alarm will be strengthened. In this way, by referring to past intrusion data and checking for similar patterns, the accuracy of intrusion detection can be improved.

[0037] The intrusion detection unit can be equipped with an intrusion detection function in agricultural drones to monitor farmland and detect intruders. For example, the intrusion detection unit can use generative AI to equip agricultural drones with an intrusion detection function to monitor farmland and detect intruders. For example, the drone can fly over farmland and issue an alarm if it detects an intruder. As a result, by equipping agricultural drones with an intrusion detection function, it becomes possible to monitor farmland and detect intruders.

[0038] The intrusion detection unit can introduce an intrusion detection function into a maritime surveillance system and detect the intrusion of a suspicious ship. The intrusion detection unit can introduce an intrusion detection function into a maritime surveillance system using, for example, a generative AI, and detect the intrusion of a suspicious ship. For example, a surveillance camera monitors the sea and issues an alarm if it detects a suspicious ship. In this way, by introducing the intrusion detection function into a maritime surveillance system, the intrusion of a suspicious ship can be detected.

[0039] The abnormal behavior detection unit can analyze abnormal behavior patterns and infer the intention behind the behavior. The abnormal behavior detection unit, for example, uses generative AI to analyze abnormal behavior patterns and infer the intention behind the behavior. For example, it analyzes movements captured by a surveillance camera and infers the purpose of the abnormal behavior. In this way, by analyzing abnormal behavior patterns and inferring the intention behind the behavior, the accuracy of warnings is improved.

[0040] The abnormal behavior detection unit can analyze surrounding environmental data when abnormal behavior is detected and identify the cause of the abnormal behavior. The abnormal behavior detection unit can, for example, use generative AI to analyze surrounding environmental data when abnormal behavior is detected and identify the cause of the abnormal behavior. For example, it can analyze whether a sudden change in temperature or humidity is the cause of the abnormal behavior. This allows the unit to analyze surrounding environmental data and identify the cause of the abnormal behavior, improving the accuracy of the alarm.

[0041] The abnormal behavior detection unit can refer to past abnormal behavior data when detecting abnormal behavior and check for similar patterns. For example, using a generation AI, the abnormal behavior detection unit can refer to past abnormal behavior data when detecting abnormal behavior and check for similar patterns. For example, if abnormal behavior using the same method has occurred in the past, the alarm can be strengthened. In this way, by referring to past abnormal behavior data and checking for similar patterns, the accuracy of abnormal behavior detection can be improved.

[0042] The abnormal behavior detection unit can ensure the safety of students by introducing an abnormal behavior detection function into a school's surveillance system. The abnormal behavior detection unit can ensure the safety of students by, for example, using generative AI. For example, if a camera in a classroom or hallway detects abnormal behavior, an alarm will be issued. In this way, introducing the abnormal behavior detection function into a school's surveillance system can ensure the safety of students.

[0043] The abnormal behavior detection unit introduces an abnormal behavior detection function into the hospital's monitoring system, enabling early detection of abnormal patient behavior. The abnormal behavior detection unit, for example, uses generative AI to introduce an abnormal behavior detection function into the hospital's monitoring system, enabling early detection of abnormal patient behavior. For example, if a camera in a hospital room detects abnormal behavior, an alarm is issued. In this way, introducing the abnormal behavior detection function into the hospital's monitoring system enables early detection of abnormal patient behavior.

[0044] The alarm unit can analyze the monitoring data in real time and detect signs of abnormalities. The alarm unit can, for example, use generative AI to analyze the monitoring data in real time and detect signs of abnormalities. For example, it can analyze movements captured by a monitoring camera and detect signs of abnormalities before they occur. This allows the unit to analyze the monitoring data in real time and detect signs of abnormalities, improving the accuracy of the alarm.

[0045] The alarm unit can automatically set the priority of an alarm when an immediate alert is issued and respond according to the level of importance. The alarm unit can automatically set the priority of an alarm when an immediate alert is issued and respond according to the level of importance, for example, by using a generation AI. For example, if an intruder is heading towards a critical area, a high-priority alarm is issued. This allows for a rapid response by automatically setting the priority of an alarm when an immediate alert is issued and responding according to the level of importance.

[0046] The alarm unit can compare real-time monitoring data with past data and analyze trends in abnormalities. The alarm unit, for example, uses a generation AI to compare real-time monitoring data with past data and analyze trends in abnormalities. For example, it compares past data with real-time data and analyzes the frequency and patterns of abnormalities. This improves the accuracy of alarms by comparing real-time monitoring data with past data and analyzing trends in abnormalities.

[0047] The alarm unit can introduce real-time monitoring and immediate warning functions into the smart city's monitoring system, improving the safety of the entire city. For example, the alarm unit can use generative AI to introduce real-time monitoring and immediate warning functions into the smart city's monitoring system, improving the safety of the entire city. For example, if a street camera detects an abnormality, it can issue an alarm. In this way, introducing real-time monitoring and immediate warning functions into the smart city's monitoring system improves the safety of the entire city.

[0048] The alarm unit can introduce real-time monitoring and immediate warning functions into a factory's monitoring system to ensure worker safety. The alarm unit can, for example, use generative AI to introduce real-time monitoring and immediate warning functions into a factory's monitoring system to ensure worker safety. For example, if a camera in a work area detects an abnormality, an alarm can be issued. This allows the introduction of real-time monitoring and immediate warning functions into a factory's monitoring system to ensure worker safety.

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

[0050] The surveillance system can further include a voice recognition unit. The voice recognition unit works in conjunction with the surveillance camera to acquire and analyze voice data. For example, by detecting shouts or abnormal sounds in public places and analyzing the voice data, it becomes possible to detect abnormal behavior early. In this way, analyzing the voice data makes it possible to detect abnormal behavior by utilizing not only visual information but also auditory information.

[0051] The monitoring system can further include a temperature sensor unit. The temperature sensor unit monitors the temperature in the monitored area in real time and detects abnormal temperature changes. For example, it can detect a temperature rise in the early stages of a fire and issue an alarm. This allows for quick response to fires and abnormal temperature changes by monitoring temperature changes.

[0052] The monitoring system can further include a vibration sensor unit. The vibration sensor unit monitors vibrations in buildings and facilities and detects abnormal vibrations. For example, it can detect earthquakes and structural problems in buildings early and issue an alarm. By monitoring vibrations, it is possible to respond quickly to earthquakes and structural abnormalities.

[0053] The surveillance system can further include a location information acquisition unit. The location information acquisition unit acquires location information of people and objects within the surveillance area in real time and detects abnormal behavior. For example, it can detect people staying in a specific area for a long time and issue an alarm. This makes it possible to use location information to detect abnormal behavior and respond quickly.

[0054] The surveillance system can also be equipped with a biometric authentication unit, which acquires biometric information such as fingerprints and irises and performs authentication. For example, fingerprint authentication can be performed at the entrance to a building to ensure that only authorized individuals can enter. This makes it possible to strengthen security by utilizing biometric information.

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

[0056] Step 1: The facial recognition unit analyzes video data acquired from a surveillance camera and performs facial recognition. For example, a camera installed at the entrance of a building captures a visitor's face and inputs the video data into the generation AI. The generation AI compares the image with a pre-registered face database to determine whether the visitor is an authorized person. Step 2: The intrusion detection unit analyzes data from surveillance cameras and sensors to detect intrusions. For example, if there is suspicious activity inside a building at night, the generated AI analyzes that activity and determines that it may be an intrusion. Step 3: The abnormal behavior detection unit analyzes the video data acquired from the surveillance cameras and detects abnormal behavior, such as violent acts or suspicious movements in public places. Step 4: The alarm unit issues an alert based on the abnormality detected by the abnormal behavior detection unit. For example, if an intrusion or abnormal behavior is detected, the generation AI issues an alert based on that information and notifies security guards or administrators.

[0057] (Example 2) The surveillance system according to the embodiment of the present invention provides functions such as face recognition, intrusion detection, and abnormal behavior detection, enabling real-time monitoring and immediate alerts, thereby enhancing the safety of buildings and public places.

[0058] A surveillance system according to an embodiment includes a face authentication unit, an intrusion detection unit, an abnormal behavior detection unit, and an alarm unit. The face authentication unit analyzes video data acquired from a surveillance camera and performs face authentication. For example, a camera installed at the entrance of a building captures a visitor's face and inputs the video data into a generation AI. The generation AI compares the video data with a pre-registered face database to determine whether the visitor is an authorized individual. The intrusion detection unit analyzes data from the surveillance camera and sensors to detect intrusions. For example, if there is suspicious activity inside a building at night, the generation AI analyzes the activity and determines that there is a possibility of intrusion. The abnormal behavior detection unit analyzes video data acquired from the surveillance camera and detects abnormal behavior. For example, it detects violent acts or suspicious behavior in public places. The alarm unit issues an alarm based on the abnormality detected by the abnormal behavior detection unit. For example, if an intrusion or abnormal behavior is detected, the generation AI issues an alarm based on the information and notifies a security guard or administrator. As a result, the monitoring system according to the embodiment performs face authentication, intrusion detection, and abnormal behavior detection in real time, and issues an alarm immediately, enabling a rapid response.

[0059] The facial recognition unit can analyze the user's facial expression and estimate their emotional state to improve the reliability of authentication. For example, the facial recognition unit uses a generative AI to analyze the user's facial expression in real time during facial recognition and estimate their emotional state. For example, a camera captures the user's face, and the generative AI estimates emotions such as joy or anger from the facial expression. This analysis of the user's emotional state improves the reliability of authentication.

[0060] The facial recognition unit can improve authentication accuracy by combining facial recognition data with voice recognition and analyzing the degree of match between voice and face. For example, the facial recognition unit uses generative AI to perform voice recognition simultaneously with facial recognition and analyze the degree of match between voice and face. For example, it analyzes the voice data when a user speaks their name and compares it with face data. This improves authentication accuracy by analyzing the degree of match between voice and face.

[0061] The facial recognition unit can refer to the user's past behavioral history and check for any abnormal behavioral patterns. For example, using a generation AI, the facial recognition unit refers to the user's past behavioral history during facial recognition and checks for any abnormal behavioral patterns. For example, if there is a history of unauthorized access in the past, a warning is issued. This makes it possible to check for any abnormal behavioral patterns by referring to the user's past behavioral history.

[0062] The facial recognition unit can integrate the facial recognition function into a smart home system to enhance security within the home. For example, by using generative AI, the facial recognition unit can integrate the facial recognition function into a smart home system to enhance security within the home. For example, a front door camera can authenticate the face of a visitor, allowing only authorized people to open the door. In this way, integrating the unit into a smart home system enhances security within the home.

[0063] The facial recognition unit can improve passenger safety by introducing a facial recognition function into a public transportation ticket gate system. The facial recognition unit can improve passenger safety by introducing a facial recognition function into a public transportation ticket gate system, for example, using generative AI. For example, facial recognition can be performed at a station ticket gate to allow only authorized passengers to pass through. This improves passenger safety by introducing it into a public transportation ticket gate system.

[0064] The facial recognition unit can use an emotion estimation function to analyze the user's emotional state during facial recognition and introduce an additional authentication step if the user is feeling stressed or anxious. The facial recognition unit can, for example, use generative AI to analyze the user's emotional state during facial recognition and introduce an additional authentication step if the user is feeling stressed or anxious. For example, an additional question can be asked to a user who is nervous. This improves the reliability of authentication by analyzing the user's emotional state and introducing an additional authentication step if the user is feeling stressed or anxious.

[0065] The intrusion detection unit can analyze the intruder's movement patterns and infer the intruder's intention to intrude. The intrusion detection unit can use, for example, generative AI to analyze the intruder's movement patterns and infer the intruder's intention to intrude. For example, it can analyze the movement captured by a surveillance camera and infer the intruder's purpose in their actions. This allows the intruder's movement patterns to be analyzed and the intruder's intention to intrude to be inferred, thereby improving the accuracy of the alarm.

[0066] The intrusion detection unit can analyze the surrounding environmental sounds when an intrusion is detected, and intensify the alarm if an abnormal sound is detected. The intrusion detection unit can, for example, use a generation AI to analyze the surrounding environmental sounds when an intrusion is detected, and intensify the alarm if an abnormal sound is detected. For example, if the sound of breaking glass or a metallic sound is detected, a high-priority alarm is issued. This improves the accuracy of intrusion detection by analyzing the surrounding environmental sounds and intensifying the alarm if an abnormal sound is detected.

[0067] The intrusion detection unit can refer to past intrusion data when detecting an intrusion and check for similar patterns. For example, using a generation AI, the intrusion detection unit can refer to past intrusion data when detecting an intrusion and check for similar patterns. For example, if an intrusion has been made using the same method in the past, the alarm will be strengthened. In this way, by referring to past intrusion data and checking for similar patterns, the accuracy of intrusion detection can be improved.

[0068] The intrusion detection unit can be equipped with an intrusion detection function in agricultural drones to monitor farmland and detect intruders. For example, the intrusion detection unit can use generative AI to equip agricultural drones with an intrusion detection function to monitor farmland and detect intruders. For example, the drone can fly over farmland and issue an alarm if it detects an intruder. As a result, by equipping agricultural drones with an intrusion detection function, it becomes possible to monitor farmland and detect intruders.

[0069] The intrusion detection unit can introduce an intrusion detection function into a maritime surveillance system and detect the intrusion of a suspicious ship. The intrusion detection unit can introduce an intrusion detection function into a maritime surveillance system using, for example, a generative AI, and detect the intrusion of a suspicious ship. For example, a surveillance camera monitors the sea and issues an alarm if it detects a suspicious ship. In this way, by introducing the intrusion detection function into a maritime surveillance system, the intrusion of a suspicious ship can be detected.

[0070] The intrusion detection unit can use an emotion estimation function to analyze the emotional state of an intruder and intensify the alarm if the intruder feels fear or tension. The intrusion detection unit can, for example, use generative AI to analyze the emotional state of an intruder and intensify the alarm if the intruder feels fear or tension. For example, it can analyze the intruder's facial expression and issue a high-priority alarm if the emotion score is high. This improves the accuracy of intrusion detection by analyzing the intruder's emotional state and intensifying the alarm if the intruder feels fear or tension.

[0071] The abnormal behavior detection unit can analyze abnormal behavior patterns and infer the intention behind the behavior. The abnormal behavior detection unit, for example, uses generative AI to analyze abnormal behavior patterns and infer the intention behind the behavior. For example, it analyzes movements captured by a surveillance camera and infers the purpose of the abnormal behavior. In this way, by analyzing abnormal behavior patterns and inferring the intention behind the behavior, the accuracy of warnings is improved.

[0072] The abnormal behavior detection unit can analyze surrounding environmental data when abnormal behavior is detected and identify the cause of the abnormal behavior. The abnormal behavior detection unit can, for example, use generative AI to analyze surrounding environmental data when abnormal behavior is detected and identify the cause of the abnormal behavior. For example, it can analyze whether a sudden change in temperature or humidity is the cause of the abnormal behavior. This allows the unit to analyze surrounding environmental data and identify the cause of the abnormal behavior, improving the accuracy of the alarm.

[0073] The abnormal behavior detection unit can refer to past abnormal behavior data when detecting abnormal behavior and check for similar patterns. For example, using a generation AI, the abnormal behavior detection unit can refer to past abnormal behavior data when detecting abnormal behavior and check for similar patterns. For example, if abnormal behavior using the same method has occurred in the past, the alarm can be strengthened. In this way, by referring to past abnormal behavior data and checking for similar patterns, the accuracy of abnormal behavior detection can be improved.

[0074] The abnormal behavior detection unit can ensure the safety of students by introducing an abnormal behavior detection function into a school's surveillance system. The abnormal behavior detection unit can ensure the safety of students by, for example, using generative AI. For example, if a camera in a classroom or hallway detects abnormal behavior, an alarm will be issued. In this way, introducing the abnormal behavior detection function into a school's surveillance system can ensure the safety of students.

[0075] The abnormal behavior detection unit introduces an abnormal behavior detection function into the hospital's monitoring system, enabling early detection of abnormal patient behavior. The abnormal behavior detection unit, for example, uses generative AI to introduce an abnormal behavior detection function into the hospital's monitoring system, enabling early detection of abnormal patient behavior. For example, if a camera in a hospital room detects abnormal behavior, an alarm is issued. In this way, introducing the abnormal behavior detection function into the hospital's monitoring system enables early detection of abnormal patient behavior.

[0076] The abnormal behavior detection unit uses an emotion estimation function to analyze the emotional state at the time of abnormal behavior and can intensify the alarm if the person is feeling anger or excitement. The abnormal behavior detection unit can, for example, use a generative AI to analyze the emotional state at the time of abnormal behavior and intensify the alarm if the person is feeling anger or excitement. For example, it can analyze the facial expression of a person engaging in abnormal behavior and issue a high-priority alarm if the emotion score is high. This improves the accuracy of abnormal behavior detection by analyzing the emotional state at the time of abnormal behavior and intensifying the alarm if the person is feeling anger or excitement.

[0077] The alarm unit can analyze the monitoring data in real time and detect signs of abnormalities. The alarm unit can, for example, use generative AI to analyze the monitoring data in real time and detect signs of abnormalities. For example, it can analyze movements captured by a monitoring camera and detect signs of abnormalities before they occur. This allows the unit to analyze the monitoring data in real time and detect signs of abnormalities, improving the accuracy of the alarm.

[0078] The alarm unit can automatically set the priority of an alarm when an immediate alert is issued and respond according to the level of importance. The alarm unit can automatically set the priority of an alarm when an immediate alert is issued and respond according to the level of importance, for example, by using a generation AI. For example, if an intruder is heading towards a critical area, a high-priority alarm is issued. This allows for a rapid response by automatically setting the priority of an alarm when an immediate alert is issued and responding according to the level of importance.

[0079] The alarm unit can compare real-time monitoring data with past data and analyze trends in abnormalities. The alarm unit, for example, uses a generation AI to compare real-time monitoring data with past data and analyze trends in abnormalities. For example, it compares past data with real-time data and analyzes the frequency and patterns of abnormalities. This improves the accuracy of alarms by comparing real-time monitoring data with past data and analyzing trends in abnormalities.

[0080] The alarm unit can introduce real-time monitoring and immediate warning functions into the smart city's monitoring system, improving the safety of the entire city. For example, the alarm unit can use generative AI to introduce real-time monitoring and immediate warning functions into the smart city's monitoring system, improving the safety of the entire city. For example, if a street camera detects an abnormality, it can issue an alarm. In this way, introducing real-time monitoring and immediate warning functions into the smart city's monitoring system improves the safety of the entire city.

[0081] The alarm unit can introduce real-time monitoring and immediate warning functions into a factory's monitoring system to ensure worker safety. The alarm unit can, for example, use generative AI to introduce real-time monitoring and immediate warning functions into a factory's monitoring system to ensure worker safety. For example, if a camera in a work area detects an abnormality, an alarm can be issued. This allows the introduction of real-time monitoring and immediate warning functions into a factory's monitoring system to ensure worker safety.

[0082] The alarm unit uses an emotion estimation function to analyze the emotional state during real-time monitoring and can adjust the intensity of the alarm when an abnormality is detected. The alarm unit can, for example, use a generative AI to analyze the emotional state during real-time monitoring and adjust the intensity of the alarm when an abnormality is detected. For example, it can analyze the facial expressions of people captured by a surveillance camera and issue a high-priority alarm if the emotion score is high. This improves the accuracy of alarms by analyzing the emotional state during real-time monitoring and adjusting the intensity of the alarm when an abnormality is detected.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The surveillance system can further include a voice recognition unit. The voice recognition unit works in conjunction with the surveillance camera to acquire and analyze voice data. For example, by detecting shouts or abnormal sounds in public places and analyzing the voice data, it becomes possible to detect abnormal behavior early. In this way, analyzing the voice data makes it possible to detect abnormal behavior by utilizing not only visual information but also auditory information.

[0085] The monitoring system can further include a temperature sensor unit. The temperature sensor unit monitors the temperature in the monitored area in real time and detects abnormal temperature changes. For example, it can detect a temperature rise in the early stages of a fire and issue an alarm. This allows for quick response to fires and abnormal temperature changes by monitoring temperature changes.

[0086] The monitoring system can further include a vibration sensor unit. The vibration sensor unit monitors vibrations in buildings and facilities and detects abnormal vibrations. For example, it can detect earthquakes and structural problems in buildings early and issue an alarm. By monitoring vibrations, it is possible to respond quickly to earthquakes and structural abnormalities.

[0087] The surveillance system can further include a location information acquisition unit. The location information acquisition unit acquires location information of people and objects within the surveillance area in real time and detects abnormal behavior. For example, it can detect people staying in a specific area for a long time and issue an alarm. This makes it possible to use location information to detect abnormal behavior and respond quickly.

[0088] The surveillance system can also be equipped with a biometric authentication unit, which acquires biometric information such as fingerprints and irises and performs authentication. For example, fingerprint authentication can be performed at the entrance to a building to ensure that only authorized individuals can enter. This makes it possible to strengthen security by utilizing biometric information.

[0089] The monitoring system can also analyze the user's emotional state and provide relaxing music or images if the user is feeling stressed or anxious. For example, a user who is feeling tense can be relieved by playing relaxing music. This allows the user's comfort to be improved by analyzing the user's emotional state and providing an appropriate relaxation effect.

[0090] The monitoring system can also analyze the user's emotional state and provide advertisements and information according to the user's emotions. For example, a positive advertisement can be displayed to a happy user, and an encouraging message can be displayed to a sad user. This allows the system to analyze the user's emotional state and provide appropriate information according to the user's emotions, thereby improving user satisfaction.

[0091] The monitoring system can also analyze the user's emotional state and adjust the color and brightness of the lighting according to the user's emotions. For example, it can provide warm lighting to a user who wants to relax, and white lighting to a user who wants to concentrate. This allows the system to analyze the user's emotional state and provide an appropriate lighting environment to improve the user's comfort.

[0092] The monitoring system can also analyze the user's emotional state and adjust the temperature and humidity accordingly. For example, it can provide an appropriate temperature and humidity for a user who wants to relax, and a comfortable environment for a user who wants to concentrate. This allows the system to analyze the user's emotional state and provide an appropriate environment to improve user comfort.

[0093] The monitoring system can also analyze the user's emotional state and select a notification method according to the emotion. For example, a gentle notification sound can be used for a user who is nervous, and a normal notification sound can be used for a user who is relaxed. This allows the system to analyze the user's emotional state and provide an appropriate notification method, thereby reducing the user's stress.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The facial recognition unit analyzes video data acquired from a surveillance camera and performs facial recognition. For example, a camera installed at the entrance of a building captures a visitor's face and inputs the video data into the generation AI. The generation AI compares the image with a pre-registered face database to determine whether the visitor is an authorized person. Step 2: The intrusion detection unit analyzes data from surveillance cameras and sensors to detect intrusions. For example, if there is suspicious activity inside a building at night, the generated AI analyzes that activity and determines that it may be an intrusion. Step 3: The abnormal behavior detection unit analyzes the video data acquired from the surveillance cameras and detects abnormal behavior, such as violent acts or suspicious movements in public places. Step 4: The alarm unit issues an alert based on the abnormality detected by the abnormal behavior detection unit. For example, if an intrusion or abnormal behavior is detected, the generation AI issues an alert based on that information and notifies security guards or administrators.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The 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.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 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.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the 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.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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, in order to avoid confusion and to 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.

[0162] 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]

[0163] 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 face recognition unit that analyzes video data acquired from a surveillance camera and performs face recognition; an intrusion detection unit that analyzes data from the surveillance camera or sensor and detects an intrusion; an abnormal behavior detection unit that analyzes video data acquired from the surveillance camera and detects abnormal behavior; an alarm unit that issues an alarm based on the abnormality detected by the abnormal behavior detection unit. A system characterized by:

2. The face authentication unit In addition to facial recognition data, voice recognition is combined and the accuracy of recognition is improved by analyzing the degree of match between the voice and the face.

2. The system of claim 1.

3. The intrusion detection unit Analyzing the movement patterns of the intruder and inferring the intention of said intrusion 2. The system of claim 1.

4. The abnormal behavior detection unit Analyzing the pattern of the abnormal behavior and inferring the intention of the behavior 2. The system of claim 1.

5. The alarm unit Analyze real-time monitoring data and detect signs of abnormalities.

2. The system of claim 1.

6. The face authentication unit Analyzing a user's facial expression and estimating their emotional state improves the reliability of authentication 2. The system of claim 1.

7. The intrusion detection unit Using emotion estimation, the system analyzes the intruder's emotional state and intensifies the alarm if the intruder feels fear or tension.

2. The system of claim 1.

8. The abnormal behavior detection unit Using emotion estimation function, the emotional state at the time of abnormal behavior is analyzed, and if the person is feeling angry or excited, the warning is strengthened.

2. The system of claim 1.

Images