System

Abstract

To provide a system for quickly and accurately detecting and coping with abnormality in a security area.SOLUTION: A security system includes a patrol unit, an analysis unit, a notification unit, and a tracking unit. The patrol unit patrols the security area. The analysis unit analyzes the data collected by the patrol unit. The notification unit issues an alarm and notifies a security guard when the analysis unit detects an abnormality. The tracking unit tracks the area where the abnormality notified by the notification unit has occurred.SELECTED DRAWING: None

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately detect and respond to abnormalities in security areas.

[0005] The system according to the embodiment aims to quickly and accurately detect and respond to abnormalities in a security area. [Means for solving the problem]

[0006] The system according to the embodiment includes a patrol unit, an analysis unit, a notification unit, and a tracking unit. The patrol unit patrols a security area. The analysis unit analyzes data collected by the patrol unit. The notification unit issues an alarm and notifies a security guard when an abnormality is detected by the analysis unit. The tracking unit tracks the area where the abnormality notified by the notification unit has occurred. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately detect and respond to abnormalities in a security area. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A security system according to an embodiment of the present invention uses drones to patrol a security area, analyzes collected data using AI to detect abnormalities, issues an alarm to notify security guards, and tracks the area where the abnormality occurred. In the security system, drones patrol the security area and collect data in real time using cameras and sensors. AI then analyzes the collected data and detects abnormalities. If an abnormality is detected, the AI ​​issues an alarm and notifies security guards. The drones also automatically track the area where the abnormality occurred and provide detailed video footage. For example, in a security system, a drone flies around a building, capturing video footage with a camera while collecting data such as temperature and sound with sensors. AI then analyzes the collected data to detect abnormalities such as the intrusion of a suspicious person or the outbreak of a fire. If an abnormality is detected, the AI ​​sounds an alarm and notifies security guards of the location. Furthermore, the drones automatically track the area where the abnormality occurred and provide detailed video footage. This allows the security system to cover a wide area, detect abnormalities early, and respond quickly. This enables efficient and rapid security. For example, it can efficiently guard large areas such as large factories and commercial facilities. In addition, drones can automatically patrol and detect abnormalities even at night or during times when manpower is scarce, improving the quality of security.

[0029] A security system according to an embodiment includes a patrol unit, an analysis unit, a notification unit, and a tracking unit. The patrol unit patrols a security area. For example, the patrol unit flies a drone over the security area and monitors the surrounding situation in real time using cameras and sensors. The patrol unit can also set the drone's flight route and patrol frequency. For example, the patrol unit optimizes the drone's patrol route to achieve efficient security. The analysis unit analyzes data collected by the patrol unit. For example, the analysis unit analyzes camera footage and sensor data to detect anomalies. The analysis unit can analyze data using AI to detect anomalies such as the intrusion of a suspicious person or the outbreak of a fire. If the analysis unit detects an anomaly, the notification unit issues an alarm and notifies a security guard. For example, if an anomaly is detected, the notification unit issues an alarm and notifies the security guard of the location. The notification unit can also select different notification methods depending on the type of anomaly. The tracking unit tracks the area where the anomaly notified by the notification unit occurred. The tracking unit may, for example, use a drone to automatically track an area where an abnormality has occurred and provide detailed video. The tracking unit may also automatically adjust the camera angle of the drone to provide optimal video. This allows the security system according to the embodiment to efficiently patrol the security area, detect abnormalities, and respond quickly.

[0030] The security system includes a battery management unit that is responsible for managing the drone's battery and charging method. The battery management unit is responsible for managing the drone's battery and charging method. For example, the battery management unit manages the charging cycle of the drone's battery and provides the optimal charging method to extend the battery's lifespan. The battery management unit can also monitor the drone's remaining battery level and charge it as needed. For example, the battery management unit can set the drone to automatically return to a charging station when its battery level is low. Furthermore, the battery management unit manages the timing of the drone's battery replacement, achieving efficient battery management. This allows for efficient battery management of the drone, enabling it to patrol for longer periods of time.

[0031] The security system includes a route setting unit that sets the drone's patrol route and patrol frequency. The route setting unit, for example, optimizes the drone's patrol route to achieve efficient security. The route setting unit can analyze past patrol data using AI to generate an optimal patrol route. The route setting unit can also adjust the patrol route taking into account environmental conditions (weather, time of day, etc.). For example, when it rains, the route setting unit sets routes that prioritize covered routes and underground passages. This allows for flexible setting of the drone's patrol route and frequency, enabling efficient security.

[0032] The patrol unit can monitor the surrounding conditions in real time using a camera or a sensor. For example, the patrol unit flies a drone over a security area and monitors the surrounding conditions in real time using a camera or a sensor. The patrol unit can capture video using the drone's camera and collect data such as temperature and sound using a sensor. For example, the patrol unit flies a drone around a building, capturing video with a camera while collecting data such as temperature and sound with a sensor. The patrol unit can also set the drone's flight route and patrol frequency. This allows for real-time monitoring of the surrounding conditions, enabling early detection of abnormalities.

[0033] The analysis unit can analyze camera footage or sensor data to detect abnormalities. For example, the analysis unit can analyze camera footage or sensor data to detect abnormalities. The analysis unit can analyze data using AI to detect abnormalities such as the intrusion of a suspicious person or the outbreak of a fire. For example, the analysis unit can analyze camera footage to detect the movement of a suspicious person. The analysis unit can also analyze sensor data to detect abnormal temperature increases or abnormal sounds. This allows for accurate detection of abnormalities by analyzing camera footage and sensor data.

[0034] The notification unit can issue an alarm and notify a security guard when an abnormality is detected. For example, the notification unit can sound an alarm when an abnormality is detected and notify the security guard of the location. The notification unit can select different notification means depending on the type of abnormality. For example, the notification unit can notify with an audio alarm when the urgency is high, and by push notification when the urgency is medium. The notification unit can also notify by email when the urgency is low. This allows security guards to be notified quickly when an abnormality is detected, enabling a quick response.

[0035] The tracking unit can automatically track an area where an abnormality has occurred and provide detailed video. For example, the tracking unit can use a drone to automatically track an area where an abnormality has occurred and provide detailed video. The tracking unit can automatically adjust the drone's camera angle to provide optimal video. For example, when the drone is tracking a suspicious person, the tracking unit can adjust the camera angle to capture the face. Also, when the drone is tracking a fire, the tracking unit can adjust the camera angle to capture the source of the fire. In this way, by automatically tracking an area where an abnormality has occurred and providing detailed video, security guards can grasp the situation at the scene in real time.

[0036] The patrol unit can automatically adjust the drone's altitude during patrols to ensure an appropriate surveillance viewpoint. For example, when the drone patrols around a building, the patrol unit adjusts its altitude to monitor the roof and windows. The patrol unit can also increase the altitude when the drone patrols a wide area to monitor a wide area at once. Furthermore, when the drone approaches a specific area, the patrol unit can lower its altitude to obtain more detailed footage. This allows the drone's altitude to be automatically adjusted to ensure an optimal surveillance viewpoint.

[0037] The patrol unit can dynamically change the drone's speed during patrol depending on the surrounding conditions. For example, when the drone patrols a crowded area, the patrol unit slows down the speed to monitor safely. The patrol unit can also increase the speed when the drone patrols a large area to monitor efficiently. Furthermore, if the drone detects an abnormality, the patrol unit can increase the speed to quickly reach the scene. This allows the drone's speed to be changed depending on the surrounding conditions, enabling safe and efficient patrols.

[0038] The patrol unit can switch the type of data collected by the drone during patrol depending on the environmental conditions. For example, the patrol unit uses an infrared camera to monitor at night. The patrol unit can also use a temperature sensor to detect abnormalities on rainy days. Furthermore, the patrol unit can also use an audio sensor to detect abnormalities on windy days. In this way, by switching the type of data collected depending on the environmental conditions, abnormalities can be detected more accurately.

[0039] When patrolling, the patrol unit allows drones to cooperate with other drones and share areas. For example, the patrol unit can divide up areas and efficiently monitor a wide area. Also, if a drone detects an abnormality, other drones can concentrate on monitoring that area. Furthermore, if a drone's battery runs out, other drones can cover that area. This allows multiple drones to patrol in cooperation, making it possible to efficiently monitor a wide area.

[0040] The patrol unit can be equipped with additional sensors that allow the drone to detect specific sounds or smells during patrols. For example, the patrol unit can be equipped with a microphone that allows the drone to detect suspicious sounds. The patrol unit can also be equipped with an odor sensor that allows the drone to detect gas leaks. Furthermore, the patrol unit can be equipped with a smoke sensor that allows the drone to detect the early stages of a fire. By adding sensors that detect specific sounds or smells, abnormalities can be detected from a more multifaceted perspective.

[0041] When patrolling, the patrol unit can set areas that the drone will focus on patrolling during specific times of the day. For example, the patrol unit can focus on patrolling specific areas at night to detect abnormalities early. The patrol unit can also focus on patrolling crowded areas during the day to ensure safety. Furthermore, the patrol unit can focus on patrolling around commercial facilities on weekends to prevent crime. By setting areas to focus on patrolling during specific times of the day, abnormalities can be detected early.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type of abnormality. For example, the analysis unit applies a face recognition algorithm to detect the intrusion of a suspicious person. The analysis unit can also apply a temperature analysis algorithm to detect a fire. Furthermore, the analysis unit can also apply an odor analysis algorithm to detect a gas leak. In this way, by applying an appropriate analysis algorithm depending on the type of abnormality, the accuracy of abnormality detection can be improved.

[0043] During analysis, the analysis unit can improve the accuracy of analysis by referring to past abnormality data. For example, the analysis unit can improve the accuracy of face recognition by referring to past suspicious person intrusion data. The analysis unit can also improve the accuracy of temperature analysis by referring to past fire data. Furthermore, the analysis unit can improve the accuracy of odor analysis by referring to past gas leak data. In this way, by referring to past abnormality data, the analysis accuracy is improved and abnormalities can be detected more accurately.

[0044] During analysis, the analysis unit can detect anomalies based on environmental conditions. For example, when it is raining, the analysis unit may detect anomalies by prioritizing data from a temperature sensor. The analysis unit may also detect anomalies by prioritizing data from an infrared camera at night. Furthermore, the analysis unit may detect anomalies by prioritizing data from an audio sensor on windy days. This improves the accuracy of anomaly detection by taking environmental conditions into consideration.

[0045] During analysis, the analysis unit can detect abnormalities by analyzing the audio data collected by the drone. For example, the analysis unit can detect suspicious sounds from the audio data collected by the drone. The analysis unit can also detect the sound of a gas leak from the audio data collected by the drone. Furthermore, the analysis unit can also detect the sound of a fire from the audio data collected by the drone. This allows for more multifaceted detection of abnormalities by analyzing the audio data.

[0046] During analysis, the analysis unit can analyze the temperature data collected by the drone to detect abnormalities. For example, the analysis unit can detect the occurrence of a fire from the temperature data collected by the drone. The analysis unit can also detect abnormal temperature increases from the temperature data collected by the drone. Furthermore, the analysis unit can detect cooling device malfunctions from the temperature data collected by the drone. This allows for more accurate detection of abnormalities by analyzing the temperature data.

[0047] During analysis, the analysis unit can analyze the vibration data collected by the drone to detect abnormalities. For example, the analysis unit can detect the occurrence of an earthquake from the vibration data collected by the drone. The analysis unit can also detect structural abnormalities in buildings from the vibration data collected by the drone. Furthermore, the analysis unit can also detect mechanical failures from the vibration data collected by the drone. This allows for more multifaceted detection of abnormalities by analyzing vibration data.

[0048] When notifying, the notification unit can select different notification means depending on the urgency of the abnormality. For example, if the urgency is high, the notification unit notifies along with an audio alarm. Furthermore, if the urgency is medium, the notification unit can also notify by push notification. Furthermore, if the urgency is low, the notification unit can also notify by email. This allows for a prompt response by selecting an appropriate notification means depending on the urgency of the abnormality.

[0049] The notification unit can improve the accuracy of notifications by referring to past notification history when making a notification. For example, the notification unit can refer to past notification history and quickly notify when a similar abnormality occurs. The notification unit can also analyze past notification history to optimize the timing of notifications. Furthermore, the notification unit can predict the user's reaction based on the past notification history and adjust the notification content. In this way, by referring to past notification history, the accuracy of notifications can be improved and a quick response can be made.

[0050] When making a notification, the notification unit can generate different notification contents depending on the type of abnormality. For example, if the intrusion of a suspicious person is detected, the notification unit will notify detailed information about the intrusion. Furthermore, if a fire is detected, the notification unit can also notify the location and scale of the fire. Furthermore, if a gas leak is detected, the notification unit can also notify the type of gas and the location of the leak. This allows for a quick response by generating appropriate notification contents depending on the type of abnormality.

[0051] When making a notification, the notification unit can select the optimal notification means by taking into account the security guard's location information. For example, if the security guard is nearby, the notification unit can issue a notification by audio alarm. If the security guard is far away, the notification unit can also issue a notification by push notification. Furthermore, if the security guard is inside the building, the notification unit can also issue a notification by email. This allows the optimal notification means to be selected by taking into account the security guard's location information, enabling a quick response.

[0052] The notification unit can determine the priority of notifications by taking into account the schedule of the security guard. For example, when a security guard is on break, the notification unit prioritizes only notifications with a high level of urgency. The notification unit can also prioritize regular notifications when a security guard is on patrol. Furthermore, the notification unit can postpone notifications with a low level of urgency when a security guard is in a meeting. This allows notifications to be sent at the appropriate time by taking into account the schedule of the security guard, enabling a prompt response.

[0053] When notifying, the notification unit can generate different notification contents depending on the location of the abnormality. For example, if an abnormality occurs inside a building, the notification unit can notify the floor and room number. If an abnormality occurs outdoors, the notification unit can also notify the area and specific location. Furthermore, if an abnormality occurs in a parking lot, the notification unit can also notify the parking space number. This allows for a prompt response by generating appropriate notification contents depending on the location of the abnormality.

[0054] The tracking unit can automatically adjust the drone's camera angle during tracking to provide optimal footage. For example, when a drone tracks a suspicious person, the tracking unit adjusts the camera angle to capture the face. When a drone tracks a fire, the tracking unit can also adjust the camera angle to capture the source of the fire. Furthermore, when a drone tracks a gas leak, the tracking unit can adjust the camera angle to capture the leak location. This automatically adjusts the camera angle to provide optimal footage and enable more accurate detection of abnormalities.

[0055] The tracking unit can dynamically change the drone's speed during tracking depending on the type of anomaly. For example, when the drone is tracking a suspicious person, the tracking unit increases the speed to quickly track the suspicious person. The tracking unit can also adjust the speed to safely track a fire. Furthermore, when the drone is tracking a gas leak, the tracking unit can adjust the speed to collect detailed data. This allows the drone's speed to be changed depending on the type of anomaly, enabling fast and safe tracking.

[0056] The tracking unit allows a drone to coordinate with other drones to track anomalies. For example, multiple drones can work together to track a suspicious individual and block their escape route. The tracking unit can also coordinate with multiple drones to track the spread of a fire and minimize damage. Furthermore, the tracking unit can also coordinate with multiple drones to identify the extent of a gas leak and respond quickly. This allows multiple drones to coordinate to track anomalies, enabling efficient monitoring of a wide area.

[0057] The tracking unit can provide security guards with data collected by the drone during tracking in real time. For example, when a drone is tracking a suspicious person, the tracking unit can provide security guards with video footage in real time. The tracking unit can also provide security guards with temperature data in real time when a drone is tracking a fire. Furthermore, the tracking unit can provide security guards with odor data in real time when a drone is tracking a gas leak. In this way, security guards can respond quickly by providing collected data in real time.

[0058] The tracking unit can select different tracking methods depending on the location of the abnormality. For example, if an abnormality occurs inside a building, the tracking unit can have the drone provide detailed images at a low altitude. If an abnormality occurs outdoors, the tracking unit can also have the drone monitor a wide area at a high altitude. Furthermore, if an abnormality occurs in a parking lot, the tracking unit can have the drone track the vehicle's movement at a medium altitude. This allows for quick and accurate tracking by selecting the appropriate tracking method depending on the location of the abnormality.

[0059] The tracking unit can apply different tracking algorithms depending on the type of anomaly. For example, the tracking unit can apply a facial recognition algorithm to track a suspicious person. The tracking unit can also apply a temperature analysis algorithm to track a fire. Furthermore, the tracking unit can apply an odor analysis algorithm to track a gas leak. This allows the drone to accurately track anomalies by applying the appropriate tracking algorithm depending on the type of anomaly.

[0060] The battery management unit can optimize the charging method according to the drone's usage status when managing the battery. For example, if the drone is used frequently, the battery management unit can perform rapid charging. The battery management unit can also maintain the normal charging method if the drone is not used for a long period of time. Furthermore, if the drone is used during a specific time period, the battery management unit can also charge according to that time period. This allows for efficient battery management by optimizing the charging method according to the drone's usage status.

[0061] The battery management unit can improve charging efficiency by referring to past battery usage data when managing the battery. For example, the battery management unit can refer to past battery usage data to determine the optimal charging timing. The battery management unit can also analyze past battery usage data to optimize the charging method. Furthermore, the battery management unit can also suggest a charging method to extend the battery life based on the past battery usage data. In this way, by referring to past battery usage data, charging efficiency can be improved and the battery life can be extended.

[0062] When managing batteries, the battery management unit can select the optimal charging station by taking into account the drone's flight route. For example, if the drone is flying a long distance, the battery management unit selects a station where it can charge along the way. In addition, if the drone is patrolling a specific area, the battery management unit can also select a charging station within that area. Furthermore, if the drone is used frequently, the battery management unit can also select the nearest charging station. This enables efficient charging by taking into account the drone's flight route.

[0063] The battery management unit can provide an energy saving mode to extend the drone's flight time during battery management. For example, when the drone is flying for a long time, the battery management unit enables the energy saving mode to reduce battery consumption. In addition, when the drone is patrolling a specific area, the battery management unit can enable the energy saving mode to allow efficient patrol. Furthermore, when the drone is tracking an abnormality, the battery management unit can disable the energy saving mode to quickly track it. Thus, by providing the energy saving mode, the drone's flight time can be extended, enabling efficient security.

[0064] When setting a route, the route setting unit can refer to past patrol data to generate an optimal route. For example, the route setting unit references past patrol data to generate the most efficient patrol route. The route setting unit can also analyze past patrol data to generate a route that prioritizes patrol of areas where abnormalities frequently occur. Furthermore, the route setting unit can also generate a route that minimizes battery consumption based on past patrol data. In this way, by referring to past patrol data, an efficient patrol route can be generated and abnormalities can be detected early.

[0065] The route setting unit can optimize the route by taking environmental conditions into consideration when setting the route. For example, the route setting unit prioritizes covered routes and underground passages when it is raining. The route setting unit can also set a route to patrol at night using an infrared camera. Furthermore, the route setting unit can also set a route that is less affected by wind on a windy day. In this way, by taking environmental conditions into consideration, an efficient patrol route can be set and abnormalities can be detected early.

[0066] When setting a route, the route setting unit allows a drone to patrol an area in cooperation with other drones. For example, the route setting unit allows multiple drones to share an area and efficiently monitor a wide area. In addition, if a drone detects an abnormality, the route setting unit can also have other drones concentrate on monitoring that area. Furthermore, if a drone runs out of battery, the route setting unit can have other drones cover that area. This allows multiple drones to patrol in cooperation with each other, enabling efficient monitoring of a wide area.

[0067] When setting a route, the route setting unit can set areas to be patrolled intensively during specific time periods. For example, the route setting unit can patrol specific areas intensively at night to detect abnormalities early. The route setting unit can also patrol areas with many people intensively during the day to ensure safety. Furthermore, the route setting unit can patrol areas around commercial facilities intensively on weekends to prevent crime. In this way, by setting areas to be patrolled intensively during specific time periods, abnormalities can be detected early.

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

[0069] The security system can further include a voice recognition unit. The voice recognition unit can analyze the voice data collected by the drone and detect abnormalities. For example, the voice recognition unit can detect suspicious voices or screams and issue an alarm. The voice recognition unit can also detect the sound of breaking glass or metal crashing. Furthermore, the voice recognition unit can detect the sound of a fire alarm and respond quickly. This allows for more comprehensive detection of abnormalities by analyzing voice data.

[0070] The security system can further include an environmental monitoring unit. The environmental monitoring unit can analyze the environmental data collected by the drone and detect abnormalities. For example, the environmental monitoring unit can detect harmful substances in the air and issue an alarm. The environmental monitoring unit can also detect abnormalities in water quality. Furthermore, the environmental monitoring unit can detect abnormalities in radiation levels and respond quickly. This allows for more comprehensive detection of abnormalities by analyzing environmental data.

[0071] The security system can further include a facial recognition unit. The facial recognition unit can analyze video data collected by the drone and identify suspicious individuals. For example, the facial recognition unit can detect the faces of suspicious individuals who have been registered in the past and issue an alarm. The facial recognition unit can also monitor people entering and exiting a specific area and detect abnormalities. Furthermore, the facial recognition unit can issue an alarm if a specific person enters or leaves the area during a specific time period. This makes it possible to quickly identify and respond to suspicious individuals using facial recognition technology.

[0072] The security system can further include a motion analysis unit. The motion analysis unit can analyze video data collected by the drone and detect abnormal behavior. For example, the motion analysis unit can detect a person behaving suspiciously and issue an alarm. The motion analysis unit can also detect abnormal behavior in a specific area. Furthermore, the motion analysis unit can monitor people behaving abnormally during specific times of the day and respond quickly. Thus, by using motion analysis technology, abnormal behavior can be detected and responded to quickly.

[0073] The security system can further include an object recognition unit. The object recognition unit can analyze the video data collected by the drone and detect abnormal objects. For example, the object recognition unit can detect suspicious luggage or abandoned objects and issue an alarm. The object recognition unit can also detect abnormal objects placed in a specific area. Furthermore, the object recognition unit can issue an alarm if an abnormal object is placed during a specific time period. Thus, by using object recognition technology, abnormal objects can be quickly detected and responded to.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The patrol unit patrols the security area. For example, the patrol unit flies a drone over the security area and monitors the surrounding situation in real time using cameras and sensors. The patrol unit can also set the drone's flight route and patrol frequency. For example, the patrol unit can optimize the drone's patrol route to achieve efficient security. Step 2: The analysis unit analyzes the data collected by the patrol unit. For example, the analysis unit analyzes camera footage and sensor data to detect abnormalities. The analysis unit uses AI to analyze the data and can detect abnormalities such as the intrusion of a suspicious person or the outbreak of a fire. Step 3: The notification unit issues an alarm and notifies a security guard if an abnormality is detected by the analysis unit. For example, the notification unit sounds an alarm when an abnormality is detected and notifies the security guard of the location. The notification unit can also select different notification means depending on the type of abnormality. Step 4: The tracking unit tracks the area where the abnormality notified by the notification unit has occurred. The tracking unit automatically tracks the area where the abnormality has occurred using, for example, a drone and provides detailed video. The tracking unit can also automatically adjust the camera angle of the drone to provide optimal video.

[0076] (Example 2) A security system according to an embodiment of the present invention uses drones to patrol a security area, analyzes collected data using AI to detect abnormalities, issues an alarm to notify security guards, and tracks the area where the abnormality occurred. In the security system, drones patrol the security area and collect data in real time using cameras and sensors. AI then analyzes the collected data and detects abnormalities. If an abnormality is detected, the AI ​​issues an alarm and notifies security guards. The drones also automatically track the area where the abnormality occurred and provide detailed video footage. For example, in a security system, a drone flies around a building, capturing video footage with a camera while collecting data such as temperature and sound with sensors. AI then analyzes the collected data to detect abnormalities such as the intrusion of a suspicious person or the outbreak of a fire. If an abnormality is detected, the AI ​​sounds an alarm and notifies security guards of the location. Furthermore, the drones automatically track the area where the abnormality occurred and provide detailed video footage. This allows the security system to cover a wide area, detect abnormalities early, and respond quickly. This enables efficient and rapid security. For example, it can efficiently guard large areas such as large factories and commercial facilities. In addition, drones can automatically patrol and detect abnormalities even at night or during times when manpower is scarce, improving the quality of security.

[0077] A security system according to an embodiment includes a patrol unit, an analysis unit, a notification unit, and a tracking unit. The patrol unit patrols a security area. For example, the patrol unit flies a drone over the security area and monitors the surrounding situation in real time using cameras and sensors. The patrol unit can also set the drone's flight route and patrol frequency. For example, the patrol unit optimizes the drone's patrol route to achieve efficient security. The analysis unit analyzes data collected by the patrol unit. For example, the analysis unit analyzes camera footage and sensor data to detect anomalies. The analysis unit can analyze data using AI to detect anomalies such as the intrusion of a suspicious person or the outbreak of a fire. If the analysis unit detects an anomaly, the notification unit issues an alarm and notifies a security guard. For example, if an anomaly is detected, the notification unit issues an alarm and notifies the security guard of the location. The notification unit can also select different notification methods depending on the type of anomaly. The tracking unit tracks the area where the anomaly notified by the notification unit occurred. The tracking unit may, for example, use a drone to automatically track an area where an abnormality has occurred and provide detailed video. The tracking unit may also automatically adjust the camera angle of the drone to provide optimal video. This allows the security system according to the embodiment to efficiently patrol the security area, detect abnormalities, and respond quickly.

[0078] The security system includes a battery management unit that is responsible for managing the drone's battery and charging method. The battery management unit is responsible for managing the drone's battery and charging method. For example, the battery management unit manages the charging cycle of the drone's battery and provides the optimal charging method to extend the battery's lifespan. The battery management unit can also monitor the drone's remaining battery level and charge it as needed. For example, the battery management unit can set the drone to automatically return to a charging station when its battery level is low. Furthermore, the battery management unit manages the timing of the drone's battery replacement, achieving efficient battery management. This allows for efficient battery management of the drone, enabling it to patrol for longer periods of time.

[0079] The security system includes a route setting unit that sets the drone's patrol route and patrol frequency. The route setting unit, for example, optimizes the drone's patrol route to achieve efficient security. The route setting unit can analyze past patrol data using AI to generate an optimal patrol route. The route setting unit can also adjust the patrol route taking into account environmental conditions (weather, time of day, etc.). For example, when it rains, the route setting unit sets routes that prioritize covered routes and underground passages. This allows for flexible setting of the drone's patrol route and frequency, enabling efficient security.

[0080] The patrol unit can monitor the surrounding conditions in real time using a camera or a sensor. For example, the patrol unit flies a drone over a security area and monitors the surrounding conditions in real time using a camera or a sensor. The patrol unit can capture video using the drone's camera and collect data such as temperature and sound using a sensor. For example, the patrol unit flies a drone around a building, capturing video with a camera while collecting data such as temperature and sound with a sensor. The patrol unit can also set the drone's flight route and patrol frequency. This allows for real-time monitoring of the surrounding conditions, enabling early detection of abnormalities.

[0081] The analysis unit can analyze camera footage or sensor data to detect abnormalities. For example, the analysis unit can analyze camera footage or sensor data to detect abnormalities. The analysis unit can analyze data using AI to detect abnormalities such as the intrusion of a suspicious person or the outbreak of a fire. For example, the analysis unit can analyze camera footage to detect the movement of a suspicious person. The analysis unit can also analyze sensor data to detect abnormal temperature increases or abnormal sounds. This allows for accurate detection of abnormalities by analyzing camera footage and sensor data.

[0082] The notification unit can issue an alarm and notify a security guard when an abnormality is detected. For example, the notification unit can sound an alarm when an abnormality is detected and notify the security guard of the location. The notification unit can select different notification means depending on the type of abnormality. For example, the notification unit can notify with an audio alarm when the urgency is high, and by push notification when the urgency is medium. The notification unit can also notify by email when the urgency is low. This allows security guards to be notified quickly when an abnormality is detected, enabling a quick response.

[0083] The tracking unit can automatically track an area where an abnormality has occurred and provide detailed video. For example, the tracking unit can use a drone to automatically track an area where an abnormality has occurred and provide detailed video. The tracking unit can automatically adjust the drone's camera angle to provide optimal video. For example, when the drone is tracking a suspicious person, the tracking unit can adjust the camera angle to capture the face. Also, when the drone is tracking a fire, the tracking unit can adjust the camera angle to capture the source of the fire. In this way, by automatically tracking an area where an abnormality has occurred and providing detailed video, security guards can grasp the situation at the scene in real time.

[0084] The patrol unit can estimate the user's emotions and adjust the patrol route based on the estimated user emotions. For example, if the user feels anxious, the patrol unit can have the drone patrol more frequently and focus on monitoring a specific area. Furthermore, if the user feels relaxed, the patrol unit can have the drone maintain its normal patrol route, emphasizing energy efficiency. Furthermore, if the user feels an emergency, the patrol unit can have the drone patrol important areas via the shortest route and quickly detect abnormalities. This allows for more appropriate security by adjusting the patrol route according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The patrol unit can automatically adjust the drone's altitude during patrols to ensure an appropriate surveillance viewpoint. For example, when the drone patrols around a building, the patrol unit adjusts its altitude to monitor the roof and windows. The patrol unit can also increase the altitude when the drone patrols a wide area to monitor a wide area at once. Furthermore, when the drone approaches a specific area, the patrol unit can lower its altitude to obtain more detailed footage. This allows the drone's altitude to be automatically adjusted to ensure an optimal surveillance viewpoint.

[0086] The patrol unit can dynamically change the drone's speed during patrol depending on the surrounding conditions. For example, when the drone patrols a crowded area, the patrol unit slows down the speed to monitor safely. The patrol unit can also increase the speed when the drone patrols a large area to monitor efficiently. Furthermore, if the drone detects an abnormality, the patrol unit can increase the speed to quickly reach the scene. This allows the drone's speed to be changed depending on the surrounding conditions, enabling safe and efficient patrols.

[0087] The patrol unit can switch the type of data collected by the drone during patrol depending on the environmental conditions. For example, the patrol unit uses an infrared camera to monitor at night. The patrol unit can also use a temperature sensor to detect abnormalities on rainy days. Furthermore, the patrol unit can also use an audio sensor to detect abnormalities on windy days. In this way, by switching the type of data collected depending on the environmental conditions, abnormalities can be detected more accurately.

[0088] The patrol unit can estimate the user's emotions and adjust the patrol frequency based on the estimated user emotions. For example, if the user feels anxious, the patrol unit increases the patrol frequency to provide a sense of security. The patrol unit can also maintain the normal patrol frequency if the user feels relaxed. Furthermore, the patrol unit can maximize the patrol frequency if the user feels an emergency. This allows for more appropriate security by adjusting the patrol frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] When patrolling, the patrol unit allows drones to cooperate with other drones and share areas. For example, the patrol unit can divide up areas and efficiently monitor a wide area. Also, if a drone detects an abnormality, other drones can concentrate on monitoring that area. Furthermore, if a drone's battery runs out, other drones can cover that area. This allows multiple drones to patrol in cooperation, making it possible to efficiently monitor a wide area.

[0090] The patrol unit can be equipped with additional sensors that allow the drone to detect specific sounds or smells during patrols. For example, the patrol unit can be equipped with a microphone that allows the drone to detect suspicious sounds. The patrol unit can also be equipped with an odor sensor that allows the drone to detect gas leaks. Furthermore, the patrol unit can be equipped with a smoke sensor that allows the drone to detect the early stages of a fire. By adding sensors that detect specific sounds or smells, abnormalities can be detected from a more multifaceted perspective.

[0091] When patrolling, the patrol unit can set areas that the drone will focus on patrolling during specific times of the day. For example, the patrol unit can focus on patrolling specific areas at night to detect abnormalities early. The patrol unit can also focus on patrolling crowded areas during the day to ensure safety. Furthermore, the patrol unit can focus on patrolling around commercial facilities on weekends to prevent crime. By setting areas to focus on patrolling during specific times of the day, abnormalities can be detected early.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] During analysis, the analysis unit can apply different analysis algorithms depending on the type of abnormality. For example, the analysis unit applies a face recognition algorithm to detect the intrusion of a suspicious person. The analysis unit can also apply a temperature analysis algorithm to detect a fire. Furthermore, the analysis unit can also apply an odor analysis algorithm to detect a gas leak. In this way, by applying an appropriate analysis algorithm depending on the type of abnormality, the accuracy of abnormality detection can be improved.

[0094] During analysis, the analysis unit can improve the accuracy of analysis by referring to past abnormality data. For example, the analysis unit can improve the accuracy of face recognition by referring to past suspicious person intrusion data. The analysis unit can also improve the accuracy of temperature analysis by referring to past fire data. Furthermore, the analysis unit can improve the accuracy of odor analysis by referring to past gas leak data. In this way, by referring to past abnormality data, the analysis accuracy is improved and abnormalities can be detected more accurately.

[0095] During analysis, the analysis unit can detect anomalies based on environmental conditions. For example, when it is raining, the analysis unit may detect anomalies by prioritizing data from a temperature sensor. The analysis unit may also detect anomalies by prioritizing data from an infrared camera at night. Furthermore, the analysis unit may detect anomalies by prioritizing data from an audio sensor on windy days. This improves the accuracy of anomaly detection by taking environmental conditions into consideration.

[0096] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize displaying important abnormalities. Furthermore, if the user is relaxed, the analysis unit can also display the analysis results with normal priority. Furthermore, if the user is feeling an emergency, the analysis unit can also display the most important abnormalities with top priority. This allows important information to be provided quickly by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] During analysis, the analysis unit can detect abnormalities by analyzing the audio data collected by the drone. For example, the analysis unit can detect suspicious sounds from the audio data collected by the drone. The analysis unit can also detect the sound of a gas leak from the audio data collected by the drone. Furthermore, the analysis unit can also detect the sound of a fire from the audio data collected by the drone. This allows for more multifaceted detection of abnormalities by analyzing the audio data.

[0098] During analysis, the analysis unit can analyze the temperature data collected by the drone to detect abnormalities. For example, the analysis unit can detect the occurrence of a fire from the temperature data collected by the drone. The analysis unit can also detect abnormal temperature increases from the temperature data collected by the drone. Furthermore, the analysis unit can detect cooling device malfunctions from the temperature data collected by the drone. This allows for more accurate detection of abnormalities by analyzing the temperature data.

[0099] During analysis, the analysis unit can analyze the vibration data collected by the drone to detect abnormalities. For example, the analysis unit can detect the occurrence of an earthquake from the vibration data collected by the drone. The analysis unit can also detect structural abnormalities in buildings from the vibration data collected by the drone. Furthermore, the analysis unit can also detect mechanical failures from the vibration data collected by the drone. This allows for more multifaceted detection of abnormalities by analyzing vibration data.

[0100] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user emotions. For example, if the user is nervous, the notification unit can provide a simple, highly visible notification method. If the user is relaxed, the notification unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can also provide a notification method that focuses on the main points. This allows for more appropriate information provision by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] When notifying, the notification unit can select different notification means depending on the urgency of the abnormality. For example, if the urgency is high, the notification unit notifies along with an audio alarm. Furthermore, if the urgency is medium, the notification unit can also notify by push notification. Furthermore, if the urgency is low, the notification unit can also notify by email. This allows for a prompt response by selecting an appropriate notification means depending on the urgency of the abnormality.

[0102] The notification unit can improve the accuracy of notifications by referring to past notification history when making a notification. For example, the notification unit can refer to past notification history and quickly notify when a similar abnormality occurs. The notification unit can also analyze past notification history to optimize the timing of notifications. Furthermore, the notification unit can predict the user's reaction based on the past notification history and adjust the notification content. In this way, by referring to past notification history, the accuracy of notifications can be improved and a quick response can be made.

[0103] When making a notification, the notification unit can generate different notification contents depending on the type of abnormality. For example, if the intrusion of a suspicious person is detected, the notification unit will notify detailed information about the intrusion. Furthermore, if a fire is detected, the notification unit can also notify the location and scale of the fire. Furthermore, if a gas leak is detected, the notification unit can also notify the type of gas and the location of the leak. This allows for a quick response by generating appropriate notification contents depending on the type of abnormality.

[0104] The notification unit can estimate the user's emotions and adjust the timing of the notification based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can quickly notify the user. Furthermore, if the user is relaxed, the notification unit can also notify the user at a normal timing. Furthermore, if the user feels an emergency, the notification unit can also notify the user as quickly as possible. This makes it possible to provide more appropriate information by adjusting the timing of the notification according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] When making a notification, the notification unit can select the optimal notification means by taking into account the security guard's location information. For example, if the security guard is nearby, the notification unit can issue a notification by audio alarm. If the security guard is far away, the notification unit can also issue a notification by push notification. Furthermore, if the security guard is inside the building, the notification unit can also issue a notification by email. This allows the optimal notification means to be selected by taking into account the security guard's location information, enabling a quick response.

[0106] The notification unit can determine the priority of notifications by taking into account the schedule of the security guard. For example, when a security guard is on break, the notification unit prioritizes only notifications with a high level of urgency. The notification unit can also prioritize regular notifications when a security guard is on patrol. Furthermore, the notification unit can postpone notifications with a low level of urgency when a security guard is in a meeting. This allows notifications to be sent at the appropriate time by taking into account the schedule of the security guard, enabling a prompt response.

[0107] When notifying, the notification unit can generate different notification contents depending on the location of the abnormality. For example, if an abnormality occurs inside a building, the notification unit can notify the floor and room number. If an abnormality occurs outdoors, the notification unit can also notify the area and specific location. Furthermore, if an abnormality occurs in a parking lot, the notification unit can also notify the parking space number. This allows for a prompt response by generating appropriate notification contents depending on the location of the abnormality.

[0108] The tracking unit can estimate the user's emotions and adjust the tracking method based on the estimated user's emotions. For example, if the user feels anxious, the tracking unit can cause the drone to provide more detailed images. The tracking unit can also maintain a normal tracking method if the user feels relaxed. Furthermore, if the user feels an emergency, the tracking unit can cause the drone to track anomalies at the fastest speed. This allows for more appropriate tracking by adjusting the tracking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The tracking unit can automatically adjust the drone's camera angle during tracking to provide optimal footage. For example, when a drone tracks a suspicious person, the tracking unit adjusts the camera angle to capture the face. When a drone tracks a fire, the tracking unit can also adjust the camera angle to capture the source of the fire. Furthermore, when a drone tracks a gas leak, the tracking unit can adjust the camera angle to capture the leak location. This automatically adjusts the camera angle to provide optimal footage and enable more accurate detection of abnormalities.

[0110] The tracking unit can dynamically change the drone's speed during tracking depending on the type of anomaly. For example, when the drone is tracking a suspicious person, the tracking unit increases the speed to quickly track the suspicious person. The tracking unit can also adjust the speed to safely track a fire. Furthermore, when the drone is tracking a gas leak, the tracking unit can adjust the speed to collect detailed data. This allows the drone's speed to be changed depending on the type of anomaly, enabling fast and safe tracking.

[0111] The tracking unit allows a drone to coordinate with other drones to track anomalies. For example, multiple drones can work together to track a suspicious individual and block their escape route. The tracking unit can also coordinate with multiple drones to track the spread of a fire and minimize damage. Furthermore, the tracking unit can also coordinate with multiple drones to identify the extent of a gas leak and respond quickly. This allows multiple drones to coordinate to track anomalies, enabling efficient monitoring of a wide area.

[0112] The tracking unit can estimate the user's emotions and determine tracking priorities based on the estimated user emotions. For example, if the user feels anxious, the tracking unit prioritizes tracking of important anomalies. Furthermore, if the user feels relaxed, the tracking unit can also perform tracking with normal priority. Furthermore, if the user feels an emergency, the tracking unit can also prioritize tracking of the most important anomalies. In this way, by determining tracking priorities according to the user's emotions, important anomalies can be quickly tracked. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The tracking unit can provide security guards with data collected by the drone during tracking in real time. For example, when a drone is tracking a suspicious person, the tracking unit can provide security guards with video footage in real time. The tracking unit can also provide security guards with temperature data in real time when a drone is tracking a fire. Furthermore, the tracking unit can provide security guards with odor data in real time when a drone is tracking a gas leak. In this way, security guards can respond quickly by providing collected data in real time.

[0114] The tracking unit can select different tracking methods depending on the location of the abnormality. For example, if an abnormality occurs inside a building, the tracking unit can have the drone provide detailed images at a low altitude. If an abnormality occurs outdoors, the tracking unit can also have the drone monitor a wide area at a high altitude. Furthermore, if an abnormality occurs in a parking lot, the tracking unit can have the drone track the vehicle's movement at a medium altitude. This allows for quick and accurate tracking by selecting the appropriate tracking method depending on the location of the abnormality.

[0115] The tracking unit can apply different tracking algorithms depending on the type of anomaly. For example, the tracking unit can apply a facial recognition algorithm to track a suspicious person. The tracking unit can also apply a temperature analysis algorithm to track a fire. Furthermore, the tracking unit can apply an odor analysis algorithm to track a gas leak. This allows the drone to accurately track anomalies by applying the appropriate tracking algorithm depending on the type of anomaly.

[0116] The battery management unit can estimate the user's emotions and adjust the timing of battery charging based on the estimated user emotions. For example, if the user feels anxious, the battery management unit can advance the timing of battery charging to always maintain a high remaining battery level. The battery management unit can also maintain a normal charging timing when the user feels relaxed. Furthermore, if the user feels an emergency, the battery management unit can optimize the timing of battery charging to charge quickly. In this way, by adjusting the timing of battery charging according to the user's emotions, it is possible to always maintain an optimal remaining battery level. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The battery management unit can optimize the charging method according to the drone's usage status when managing the battery. For example, if the drone is used frequently, the battery management unit can perform rapid charging. The battery management unit can also maintain the normal charging method if the drone is not used for a long period of time. Furthermore, if the drone is used during a specific time period, the battery management unit can also charge according to that time period. This allows for efficient battery management by optimizing the charging method according to the drone's usage status.

[0118] The battery management unit can improve charging efficiency by referring to past battery usage data when managing the battery. For example, the battery management unit can refer to past battery usage data to determine the optimal charging timing. The battery management unit can also analyze past battery usage data to optimize the charging method. Furthermore, the battery management unit can also suggest a charging method to extend the battery life based on the past battery usage data. In this way, by referring to past battery usage data, charging efficiency can be improved and the battery life can be extended.

[0119] The battery management unit can estimate the user's emotions and adjust the timing of battery replacement based on the estimated user emotions. For example, if the user feels anxious, the battery management unit can advance the timing of battery replacement to always maintain a high remaining battery level. The battery management unit can also maintain the normal replacement timing when the user feels relaxed. Furthermore, if the user feels an emergency, the battery management unit can optimize the timing of battery replacement and replace it quickly. In this way, by adjusting the timing of battery replacement according to the user's emotions, it is possible to always maintain an optimal remaining battery level. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] When managing batteries, the battery management unit can select the optimal charging station by taking into account the drone's flight route. For example, if the drone is flying a long distance, the battery management unit selects a station where it can charge along the way. In addition, if the drone is patrolling a specific area, the battery management unit can also select a charging station within that area. Furthermore, if the drone is used frequently, the battery management unit can also select the nearest charging station. This enables efficient charging by taking into account the drone's flight route.

[0121] The battery management unit can provide an energy saving mode to extend the drone's flight time during battery management. For example, when the drone is flying for a long time, the battery management unit enables the energy saving mode to reduce battery consumption. In addition, when the drone is patrolling a specific area, the battery management unit can enable the energy saving mode to allow efficient patrol. Furthermore, when the drone is tracking an abnormality, the battery management unit can disable the energy saving mode to quickly track it. Thus, by providing the energy saving mode, the drone's flight time can be extended, enabling efficient security.

[0122] The route setting unit can estimate the user's emotions and adjust the patrol route based on the estimated user emotions. For example, if the user feels anxious, the route setting unit can cause the drone to patrol more frequently and focus on monitoring a specific area. Furthermore, if the user feels relaxed, the route setting unit can cause the drone to maintain its normal patrol route and prioritize energy efficiency. Furthermore, if the user feels an emergency, the route setting unit can cause the drone to patrol important areas via the shortest route and quickly detect abnormalities. This allows for more appropriate security by adjusting the patrol route according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] When setting a route, the route setting unit can refer to past patrol data to generate an optimal route. For example, the route setting unit references past patrol data to generate the most efficient patrol route. The route setting unit can also analyze past patrol data to generate a route that prioritizes patrol of areas where abnormalities frequently occur. Furthermore, the route setting unit can also generate a route that minimizes battery consumption based on past patrol data. In this way, by referring to past patrol data, an efficient patrol route can be generated and abnormalities can be detected early.

[0124] The route setting unit can optimize the route by taking environmental conditions into consideration when setting the route. For example, the route setting unit prioritizes covered routes and underground passages when it is raining. The route setting unit can also set a route to patrol at night using an infrared camera. Furthermore, the route setting unit can also set a route that is less affected by wind on a windy day. In this way, by taking environmental conditions into consideration, an efficient patrol route can be set and abnormalities can be detected early.

[0125] The route setting unit can estimate the user's emotions and adjust the patrol frequency based on the estimated user emotions. For example, if the user feels anxious, the route setting unit can increase the patrol frequency to provide a sense of security. The route setting unit can also maintain the normal patrol frequency if the user feels relaxed. Furthermore, if the user feels an emergency, the route setting unit can maximize the patrol frequency. This allows for more appropriate security by adjusting the patrol frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0126] When setting a route, the route setting unit allows a drone to patrol an area in cooperation with other drones. For example, the route setting unit allows multiple drones to share an area and efficiently monitor a wide area. In addition, if a drone detects an abnormality, the route setting unit can also have other drones concentrate on monitoring that area. Furthermore, if a drone runs out of battery, the route setting unit can have other drones cover that area. This allows multiple drones to patrol in cooperation with each other, enabling efficient monitoring of a wide area.

[0127] When setting a route, the route setting unit can set areas to be patrolled intensively during specific time periods. For example, the route setting unit can patrol specific areas intensively at night to detect abnormalities early. The route setting unit can also patrol areas with many people intensively during the day to ensure safety. Furthermore, the route setting unit can patrol areas around commercial facilities intensively on weekends to prevent crime. In this way, by setting areas to be patrolled intensively during specific time periods, abnormalities can be detected early. === Hard Collateral 1-1 === Each of the multiple elements, including the patrol unit, analysis unit, notification unit, and tracking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the patrol unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the patrol unit patrols the security area using the camera 42 and sensors of the smart device 14 and collects data in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to detect abnormalities. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and issues an alarm and notifies security guards when an abnormality is detected. The tracking unit, for example, automatically tracks the area where the abnormality occurred using the camera 42 of the smart device 14 and provides detailed video. === Hard Collateral 1-2 === Each of the multiple elements, including the patrol unit, analysis unit, notification unit, and tracking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the patrol unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the patrol unit patrols the security area using the camera 42 and sensors of the smart glasses 214 and collects data in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to detect abnormalities. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and issues an alarm and notifies security personnel when an abnormality is detected. The tracking unit, for example, automatically tracks the area where the abnormality occurred using the camera 42 of the smart glasses 214 and provides detailed video. === Hard Collateral 1-3 === Each of the multiple elements including the patrol unit, analysis unit, notification unit, and tracking unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the patrol unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the patrol unit patrols the security area using the camera 42 and sensors of the headset terminal 314 and collects data in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to detect abnormalities. The notification unit is realized, for example, by the control unit 46A of the headset terminal 314 and issues an alarm and notifies security personnel when an abnormality is detected. The tracking unit automatically tracks the area where the abnormality occurred using, for example, the camera 42 of the headset terminal 314 and provides detailed video images. === Hard Collateral 1-4 === Each of the multiple elements including the patrol unit, analysis unit, notification unit, and tracking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the patrol unit is realized by at least one of the robot 414 and the data processing device 12. For example, the patrol unit patrols the security area using the camera 42 and sensors of the robot 414 and collects data in real time. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to detect abnormalities. The notification unit is realized, for example, by the control unit 46A of the robot 414 and issues an alarm and notifies security personnel when an abnormality is detected. The tracking unit, for example, automatically tracks the area where the abnormality occurred using the camera 42 of the robot 414 and provides detailed video images.

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] The security system can further include a voice recognition unit. The voice recognition unit can analyze the voice data collected by the drone and detect abnormalities. For example, the voice recognition unit can detect suspicious voices or screams and issue an alarm. The voice recognition unit can also detect the sound of breaking glass or metal crashing. Furthermore, the voice recognition unit can detect the sound of a fire alarm and respond quickly. This allows for more comprehensive detection of abnormalities by analyzing voice data.

[0130] The security system can further include an environmental monitoring unit. The environmental monitoring unit can analyze the environmental data collected by the drone and detect abnormalities. For example, the environmental monitoring unit can detect harmful substances in the air and issue an alarm. The environmental monitoring unit can also detect abnormalities in water quality. Furthermore, the environmental monitoring unit can detect abnormalities in radiation levels and respond quickly. This allows for more comprehensive detection of abnormalities by analyzing environmental data.

[0131] The security system can further include a facial recognition unit. The facial recognition unit can analyze video data collected by the drone and identify suspicious individuals. For example, the facial recognition unit can detect the faces of suspicious individuals who have been registered in the past and issue an alarm. The facial recognition unit can also monitor people entering and exiting a specific area and detect abnormalities. Furthermore, the facial recognition unit can issue an alarm if a specific person enters or leaves the area during a specific time period. This makes it possible to quickly identify and respond to suspicious individuals using facial recognition technology.

[0132] The security system can further include a motion analysis unit. The motion analysis unit can analyze video data collected by the drone and detect abnormal behavior. For example, the motion analysis unit can detect a person behaving suspiciously and issue an alarm. The motion analysis unit can also detect abnormal behavior in a specific area. Furthermore, the motion analysis unit can monitor people behaving abnormally during specific times of the day and respond quickly. Thus, by using motion analysis technology, abnormal behavior can be detected and responded to quickly.

[0133] The security system can further include an object recognition unit. The object recognition unit can analyze the video data collected by the drone and detect abnormal objects. For example, the object recognition unit can detect suspicious luggage or abandoned objects and issue an alarm. The object recognition unit can also detect abnormal objects placed in a specific area. Furthermore, the object recognition unit can issue an alarm if an abnormal object is placed during a specific time period. Thus, by using object recognition technology, abnormal objects can be quickly detected and responded to.

[0134] The patrol unit can estimate the user's emotions and adjust its patrol route based on the estimated user's emotions. For example, if the user feels anxious, the drone will patrol more frequently and focus on monitoring specific areas. Alternatively, if the user feels relaxed, the patrol unit can have the drone maintain its normal patrol route, emphasizing energy efficiency. Furthermore, if the user feels an emergency, the patrol unit can have the drone patrol key areas via the shortest route and quickly detect any abnormalities. This allows for more appropriate security by adjusting the patrol route according to the user's emotions.

[0135] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide more appropriate information.

[0136] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible notification method can be provided. If the user is relaxed, the notification unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the notification unit can also provide a notification method that focuses on the main points. In this way, by adjusting the notification method according to the user's emotions, more appropriate information can be provided.

[0137] The tracking unit can estimate the user's emotions and adjust the tracking method based on the estimated user's emotions. For example, if the user feels anxious, the drone will provide more detailed footage. The tracking unit can also maintain the normal tracking method if the user feels relaxed. Furthermore, the tracking unit can also track anomalies at the fastest speed if the user feels an emergency. This allows for more appropriate tracking by adjusting the tracking method according to the user's emotions.

[0138] The battery management unit can estimate the user's emotions and adjust the timing of battery charging based on the estimated user's emotions. For example, if the user feels anxious, the battery charging timing can be accelerated to maintain a high remaining battery level. The battery management unit can also maintain a normal charging timing when the user feels relaxed. Furthermore, if the user feels an emergency, the battery management unit can optimize the timing of battery charging to charge quickly. In this way, by adjusting the timing of battery charging according to the user's emotions, the remaining battery level can always be maintained at an optimum level.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The patrol unit patrols the security area. For example, the patrol unit flies a drone over the security area and monitors the surrounding situation in real time using cameras and sensors. The patrol unit can also set the drone's flight route and patrol frequency. For example, the patrol unit can optimize the drone's patrol route to achieve efficient security. Step 2: The analysis unit analyzes the data collected by the patrol unit. For example, the analysis unit analyzes camera footage and sensor data to detect abnormalities. The analysis unit uses AI to analyze the data and can detect abnormalities such as the intrusion of a suspicious person or the outbreak of a fire. Step 3: The notification unit issues an alarm and notifies a security guard if an abnormality is detected by the analysis unit. For example, the notification unit sounds an alarm when an abnormality is detected and notifies the security guard of the location. The notification unit can also select different notification means depending on the type of abnormality. Step 4: The tracking unit tracks the area where the abnormality notified by the notification unit has occurred. The tracking unit automatically tracks the area where the abnormality has occurred using, for example, a drone and provides detailed video. The tracking unit can also automatically adjust the camera angle of the drone to provide optimal video.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0188] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Explanation of symbols]

[0213] 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 patrol unit that patrols the security area; an analysis unit that analyzes the data collected by the patrol unit; a notification unit that issues an alarm and notifies a security guard when an abnormality is detected by the analysis unit; a tracking unit that tracks the area where the abnormality notified by the notification unit has occurred; A system characterized by:

2. It has a battery management department that is responsible for managing the drone's battery and charging methods.

2. The system of claim 1.

3. Equipped with a route setting unit that sets the drone's patrol route and patrol frequency 2. The system of claim 1.

4. The circulating unit Monitor the surroundings in real time using cameras or sensors 2. The system of claim 1.

5. The analysis unit Analyze camera images or sensor data to detect abnormalities 2. The system of claim 1.

6. The notification unit If an abnormality is detected, an alarm is issued and security personnel are notified.

2. The system of claim 1.

7. The tracking unit Automatically tracks areas where abnormalities occur and provides detailed video 2. The system of claim 1.

8. The circulating unit Estimate the user's emotions and adjust the tour route based on the estimated user emotions.

2. The system of claim 1.

9. The circulating unit During patrols, the drone's altitude is automatically adjusted to ensure an optimal surveillance viewpoint.

2. The system of claim 1.

Images