system

Abstract

The system according to the embodiment aims to efficiently patrol high-crime areas and detect abnormal behavior. [Solution] A system according to an embodiment includes a patrol unit, a detection unit, an action unit, and a reporting unit. The patrol unit patrols high-crime areas. The detection unit analyzes data collected by the patrol unit and detects abnormal behavior. The action unit causes the drone to take action based on the abnormal behavior detected by the detection unit. The reporting unit reports to the police based on the action taken by the action unit.

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 technologies are not efficient at patrolling high-crime areas or detecting abnormal behavior, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently patrol high-crime areas and detect abnormal behavior. [Means for solving the problem]

[0006] The system according to the embodiment includes a patrol unit, a detection unit, an action unit, and a reporting unit. The patrol unit patrols high-crime areas. The detection unit analyzes data collected by the patrol unit and detects abnormal behavior. The action unit causes the drone to take action based on the abnormal behavior detected by the detection unit. The reporting unit reports to the police based on the action taken by the action unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently patrol high-crime areas and detect abnormal behavior. [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 drone patrol system according to an embodiment of the present invention efficiently patrols high-crime areas, with a generation AI detecting and immediately responding to abnormal behavior and situations. This drone patrol system, equipped with a generation AI mounted on a drone, detects abnormal behavior and situations during patrol. For example, it detects suspicious behavior such as a suspicious individual staying in a particular location for a long time or suddenly starting to run. The generation AI analyzes these abnormalities in real time and causes the drone to take immediate action. The drone approaches the location where an abnormality is detected and issues a warning or advice through a speaker. For example, it may issue a message such as, "This is not safe. Please move immediately." It also contacts the police if necessary. For example, if abnormal behavior is determined to be a sign of a crime, the drone automatically notifies the police and transmits video footage of the scene. This reduces crime in the area and ensures the safety and security of residents. First, a patrol unit is established for the drone to patrol high-crime areas. Next, a detection unit is established for the generation AI to detect abnormal behavior. This detection unit uses image analysis and voice recognition technologies to detect abnormal behavior. For example, it can detect suspicious individuals staying in a particular location for a long time or suddenly running away. Furthermore, an action unit is provided to enable the drone to take immediate action if abnormal behavior is detected. This action unit issues warnings and advice through a speaker. For example, it could issue a message such as, "This is not safe. Please move quickly." Finally, a reporting unit is provided to notify the police if necessary. This reporting unit will notify the police if the generative AI determines that abnormal behavior is a sign of a crime. For example, if abnormal behavior is determined to be a sign of a crime, the drone will automatically notify the police and transmit video footage of the scene. This can help prevent crime in the area and ensure the safety and security of residents. This drone patrol system can help prevent crime in the area and ensure the safety and security of residents.

[0029] A drone patrol system according to an embodiment includes a patrol unit, a detection unit, an action unit, and a reporting unit. The patrol unit patrols high-crime areas. For example, the patrol unit patrols a specific route using a drone. The patrol route is set based on past crime data and local characteristics. For example, the patrol unit prioritizes patrols times and locations where crime is prevalent. The patrol unit can also dynamically change the patrol route depending on the drone's remaining battery level and weather conditions. For example, if the battery level is low, the patrol unit sets a route to return to a charging station. The detection unit uses a generation AI to detect abnormal behavior during patrol. The detection unit detects abnormal behavior using, for example, image analysis technology or voice recognition technology. For example, the detection unit detects when a suspicious person stays in a specific location for a long time or suddenly starts running. The generation AI analyzes data collected using, for example, a camera or microphone and detects abnormal behavior in real time. The action unit causes the drone to take action based on the abnormal behavior detected by the detection unit. The action unit issues warnings and advice, for example, through a speaker. For example, it can issue a message such as, "This is not safe. Please move quickly." The action unit can also control the drone's movement and approach the location where abnormal behavior occurs. The reporting unit notifies the police as needed based on the action performed by the action unit. For example, if the abnormal behavior is determined to be a sign of a crime, the reporting unit notifies the police and transmits footage of the scene. The reporting unit can also automatically generate report content based on data analyzed by the generation AI. For example, the reporting unit generates report content including information such as the type of abnormal behavior, the location and time of occurrence, and sends it to the police. This allows the drone patrol system to prevent crime in the area and ensure the safety and security of residents.

[0030] The detection unit can detect abnormal behavior using image analysis technology or voice recognition technology. Image analysis technology includes, for example, facial recognition, motion detection, and object detection. For example, the detection unit can analyze video data collected using a camera to detect suspicious individuals or abnormal behavior. Facial recognition technology can identify specific individuals and monitor their behavior. Motion detection technology can detect sudden or abnormal movements. Object detection technology can detect the presence of specific objects and track their movements. Voice recognition technology includes, for example, voice command recognition and abnormal sound detection. For example, the detection unit can analyze voice data collected using a microphone to detect abnormal or suspicious sounds. Voice command recognition technology can respond to specific voice commands and take action. Abnormal sound detection technology can detect abnormal sounds such as screams or the sound of glass breaking. As a result, the use of image analysis technology and voice recognition technology improves the accuracy of detecting abnormal behavior. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit may input data acquired by a camera or microphone into the generation AI and cause the generation AI to detect abnormal behavior.

[0031] The action unit can issue warnings and advice through a speaker. For example, the action unit issues warnings and advice through a speaker. For example, it can issue a message such as, "This is not safe. Please move quickly." The action unit can also approach a location where abnormal behavior has occurred and issue a direct warning or advice. For example, if a drone detects abnormal behavior, it approaches the location and issues a warning message through a speaker. Furthermore, the action unit can issue different messages depending on the type of abnormal behavior. For example, if a suspicious person stays in a specific location for a long time, it can issue a message such as, "This is not safe. Please move quickly." If a person suddenly starts running, it can issue a message such as, "Do not run. Please ensure your safety." This allows for immediate response to abnormal behavior by issuing warnings and advice through a speaker. Some or all of the above-described processing in the action unit may be performed using, or without, a generation AI. For example, the action unit can issue a message generated by a generation AI through a speaker.

[0032] The reporting unit can notify the police and transmit video of the scene when it determines that abnormal behavior is a sign of a crime. For example, the reporting unit can notify the police and transmit video of the scene when it determines that abnormal behavior is a sign of a crime. The reporting unit can automatically generate report content based on data analyzed by the generation AI. For example, the reporting unit generates report content including information such as the type of abnormal behavior, the location and time of occurrence, and transmits it to the police. The reporting unit can also transmit audio data along with the video of the scene. For example, the reporting unit can transmit environmental sounds and audio evidence along with the video of the scene. Furthermore, the reporting unit can simultaneously notify related organizations other than the police. For example, the reporting unit can notify the fire department and ambulance team together with the police. This enables a rapid response by notifying the police and transmitting video of the scene when abnormal behavior is determined to be a sign of a crime. Some or all of the above-mentioned processing in the reporting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reporting unit can transmit the report content generated by the generation AI to the police.

[0033] The detection unit can detect when a suspicious person stays in a specific location for a long time or suddenly starts running. The detection unit, for example, detects when a suspicious person stays in a specific location for a long time. For example, the detection unit can analyze video data collected using a camera to detect a person staying in a specific location for a long time. The detection unit can also detect a person who suddenly starts running. For example, the detection unit can detect a person who suddenly starts running using motion detection technology. This detection of suspicious behavior enables early detection of abnormal behavior. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can input video data acquired by a camera into the generation AI and have the generation AI detect suspicious behavior.

[0034] The action unit can transmit a message such as, "This is not safe. Please move quickly." For example, if a drone detects abnormal behavior, it approaches the location and transmits a message such as, "This is not safe. Please move quickly." through a speaker. The action unit can also transmit different messages depending on the type of abnormal behavior. For example, if a suspicious person stays in a specific location for a long time, it can transmit a message such as, "This is not safe. Please move quickly." If a person suddenly starts running, it can transmit a message such as, "Do not run. Please stay safe." This enables effective attention to abnormal behavior by transmitting specific messages. Some or all of the above-described processing in the action unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the action unit can transmit a message generated by a generation AI through a speaker.

[0035] The patrol unit can analyze past crime data and generate the most effective patrol pattern. The patrol unit, for example, analyzes past crime data and generates the most effective patrol pattern. For example, based on past crime data, it sets patrol routes to match times when crimes are most prevalent. It focuses patrols on specific areas depending on the type of crime. It adjusts the frequency of patrol based on the frequency of crimes. In this way, by analyzing past crime data, it is possible to generate effective patrol patterns. Some or all of the above-mentioned processing in the patrol unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the patrol unit can input past crime data into a generation AI and have the generation AI generate a patrol pattern.

[0036] The patrol unit can adjust the patrol frequency according to the weather and the time of day. The patrol unit adjusts the patrol frequency according to the weather and the time of day, for example. For example, it increases the patrol frequency at night or when it is raining. It decreases the patrol frequency during the day or when it is sunny. It increases the patrol frequency during times when a specific event is held. In this way, efficient patrol is possible by adjusting the patrol frequency according to the weather and the time of day. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, the generation AI, for example. For example, the patrol unit can input data on the weather and the time of day into the generation AI and have the generation AI adjust the patrol frequency.

[0037] The patrol unit can optimize patrol routes based on geographical crime rates. The patrol unit optimizes patrol routes based on geographical crime rates, for example. For example, it prioritizes patrol in areas with high crime rates. It postpones areas with low crime rates. It dynamically changes patrol routes according to fluctuations in crime rates. This enables efficient patrols by optimizing patrol routes based on geographical crime rates. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, a generation AI, for example. For example, the patrol unit can input geographical crime rate data into the generation AI and have the generation AI optimize the patrol routes.

[0038] The patrol unit can adjust the patrol route according to the remaining battery level of the drone. The patrol unit adjusts the patrol route according to, for example, the remaining battery level of the drone. For example, if the remaining battery level is low, the patrol route is shortened. If the remaining battery level is sufficient, the patrol route is extended. A route that passes through a charging station is set according to the remaining battery level. This enables efficient patrol by adjusting the patrol route according to the remaining battery level of the drone. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, the generation AI. For example, the patrol unit may input remaining battery level data of the drone into the generation AI and have the generation AI adjust the patrol route.

[0039] When detecting abnormal behavior, the detection unit can evaluate the degree of abnormality by comparing with past data. For example, when detecting abnormal behavior, the detection unit evaluates the degree of abnormality by comparing with past data. For example, when detecting abnormal behavior, the detection unit evaluates the frequency of abnormal behavior by comparing with past data. For example, the detection unit evaluates the type of abnormal behavior by comparing with past data. For example, the detection unit evaluates the time period of abnormal behavior by comparing with past data. In this way, the accuracy of detecting abnormal behavior is improved by evaluating the degree of abnormality by comparing with past data. Some or all of the above-mentioned processing in the detection unit may be performed using, or without using, the generation AI. For example, the detection unit may input past data into the generation AI and have the generation AI evaluate the degree of abnormality.

[0040] The detection unit can filter surrounding environmental sounds and background noise when detecting abnormal behavior. The detection unit, for example, filters surrounding environmental sounds and background noise when detecting abnormal behavior. For example, the detection unit may filter surrounding environmental sounds to detect abnormal behavior, filter background noise to detect abnormal behavior, or filter specific sound patterns to detect abnormal behavior. By filtering surrounding environmental sounds and background noise, the accuracy of detecting abnormal behavior is improved. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI, for example. For example, the detection unit may input data on environmental sounds and background noise into the generation AI and have the generation AI perform the filtering.

[0041] The detection unit can improve detection accuracy based on ambient lighting conditions when detecting abnormal behavior. For example, when detecting abnormal behavior, the detection unit improves detection accuracy by taking ambient lighting conditions into consideration. For example, the detection accuracy of abnormal behavior is improved by taking ambient lighting conditions into consideration. When lighting is dark, the detection accuracy is increased. When lighting is bright, the detection accuracy is decreased. In this way, the detection accuracy of abnormal behavior is improved by taking ambient lighting conditions into consideration. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI, for example. For example, the detection unit can input lighting condition data into the generation AI and have the generation AI improve detection accuracy.

[0042] The detection unit can perform cooperative operations with other drones when detecting abnormal behavior. The detection unit performs cooperative operations with other drones when detecting abnormal behavior, for example. For example, it cooperates with other drones to detect abnormal behavior. It shares information with other drones to detect abnormal behavior. It cooperates with other drones to identify the range of abnormal behavior. By performing cooperative operations with other drones, the accuracy of detecting abnormal behavior is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can input data from other drones into the generation AI and have the generation AI perform cooperative operations.

[0043] The action unit can execute different actions depending on the type of abnormal behavior. The action unit executes different actions depending on the type of abnormal behavior, for example. For example, if a suspicious person stays in a specific place for a long time, the action unit may issue a warning. If a person suddenly starts running, the action unit may issue a warning. If a person is destroying something, the action unit may notify the police. This enables effective response by executing appropriate actions depending on the type of abnormal behavior. Some or all of the above-mentioned processing in the action unit may be performed using, or without, a generation AI. For example, the action unit may input the type of abnormal behavior into the generation AI and have the generation AI execute an appropriate action.

[0044] The action unit can monitor the surrounding situation in real time when executing an action and take an appropriate response. The action unit, for example, monitors the surrounding situation in real time when executing an action and takes an appropriate response. For example, it monitors the surrounding situation in real time and gives appropriate warnings or advice. It monitors the surrounding situation in real time and issues appropriate warnings. It monitors the surrounding situation in real time and makes appropriate reports. In this way, by monitoring the surrounding situation in real time, an appropriate response becomes possible. Some or all of the above-mentioned processing in the action unit may be performed using, or without, the generation AI, for example. For example, the action unit can input surrounding situation data into the generation AI and cause the generation AI to take an appropriate response.

[0045] The action unit can cooperate with other drones when executing an action to achieve an effective response. The action unit, for example, cooperates with other drones when executing an action to achieve an effective response. For example, it can cooperate with other drones to give warnings or advice. It can cooperate with other drones to issue warnings. It can cooperate with other drones to make reports. In this way, cooperation with other drones enables an effective response. Some or all of the above-described processing in the action unit may be performed using, or without, the generation AI, for example. For example, the action unit can input data from other drones into the generation AI and have the generation AI execute a coordinated action.

[0046] The action unit can select the optimal action by taking into consideration the surrounding environmental information when executing an action. For example, the action unit selects the optimal action by taking into consideration the surrounding environmental information when executing an action. For example, the action unit provides optimal warnings or advice by taking into consideration the surrounding environmental information. The action unit issues optimal warnings by taking into consideration the surrounding environmental information. The action unit makes optimal notifications by taking into consideration the surrounding environmental information. In this way, the optimal action can be selected by taking into consideration the surrounding environmental information. Some or all of the above-described processing in the action unit may be performed using, or without using, a generation AI. For example, the action unit can input the surrounding environmental information into the generation AI and have the generation AI select the optimal action.

[0047] The reporting unit can select the optimal reporting method by referring to past reporting history when reporting. The reporting unit, for example, selects the optimal reporting method by referring to past reporting history when reporting. For example, the reporting unit selects the optimal reporting method based on the past reporting history. The reporting unit selects the optimal reporting content based on the past reporting history. The reporting unit selects the optimal reporting timing based on the past reporting history. In this way, the optimal reporting method can be selected by referring to the past reporting history. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without using, the generation AI, for example. For example, the reporting unit can input the past reporting history into the generation AI and have the generation AI select the optimal reporting method.

[0048] The reporting unit can also transmit audio data along with the video of the scene when reporting. The reporting unit, for example, transmits audio data along with the video of the scene when reporting. For example, when reporting, audio data is transmitted along with the video of the scene. When reporting, environmental sounds are transmitted along with the video of the scene. When reporting, evidential audio is transmitted along with the video of the scene. In this way, by transmitting audio data along with the video of the scene, more detailed information can be provided. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit can input the video and audio data of the scene into the generation AI and have the generation AI execute the transmission.

[0049] The reporting unit can simultaneously notify related organizations other than the police when making a report. The reporting unit, for example, simultaneously notifies related organizations other than the police when making a report. For example, when making a report, it notifies the fire department along with the police. When making a report, it notifies an ambulance team along with the police. When making a report, it notifies a local crime prevention association along with the police. By simultaneously notifying related organizations other than the police, a rapid response is possible. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit can input the content of the report into the generation AI and have the generation AI execute reporting to related organizations.

[0050] The reporting unit can automatically translate the report content at the time of reporting and provide multilingual support. The reporting unit, for example, automatically translates the report content at the time of reporting and provides multilingual support. For example, at the time of reporting, the report content is automatically translated and the report is made in English. At the time of reporting, the report content is automatically translated and the report is made in Spanish. At the time of reporting, the report content is automatically translated and the report is made in Chinese. In this way, multilingual support is possible by automatically translating the report content. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without using, a generation AI, for example. For example, the reporting unit can input the report content into a generation AI and have the generation AI perform translation and multilingual support.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The patrol unit can optimize its patrol routes by taking into account the geographical characteristics of high-crime areas. For example, it can set routes that avoid areas where drones are difficult to fly, such as narrow alleys or between high-rise buildings. The patrol unit can also collect feedback from local residents and adjust its patrol routes based on that information. For example, if residents feel unsafe in a particular area, it can change its route to prioritize patrolling that area. Furthermore, the patrol unit can obtain information about local events and focus on patrolling the surrounding area when an event is taking place. This enables effective patrols that reflect the characteristics of the area and the opinions of residents.

[0053] When detecting abnormal behavior, the detection unit can improve detection accuracy by taking into account environmental data such as ambient temperature and humidity. For example, when the temperature is high, there is a risk of heatstroke, so the detection sensitivity of abnormal behavior can be increased. Also, when the humidity is high, there is a possibility that visibility will be reduced, so the detection accuracy can be adjusted. Furthermore, the detection unit can dynamically change the detection range of abnormal behavior by taking into account meteorological data such as wind speed and wind direction. In this way, by taking environmental data into account, the detection accuracy of abnormal behavior is improved.

[0054] When the action unit detects abnormal behavior, it can turn on the drone's lights to illuminate the surroundings. For example, if abnormal behavior is detected at night, the drone's lights can be turned on to brighten the surroundings and improve visibility. The action unit can also attract attention by changing the light's lighting pattern. For example, by flashing the lights, it can notify people in the vicinity that abnormal behavior is occurring. Furthermore, the action unit can change the color of the lights to respond according to the type of abnormal behavior. This makes it possible to respond effectively to abnormal behavior through visual actions.

[0055] When the reporting unit detects abnormal behavior, it can connect with the local security camera system to obtain more detailed video data. For example, if a drone detects abnormal behavior, it can obtain footage from security cameras in the surrounding area and add it to the report. The reporting unit can also integrate security camera footage with drone footage to provide a wider field of view. Furthermore, the reporting unit can obtain real-time data from the security camera system and accurately determine the location and time of the abnormal behavior. This allows for more detailed and accurate reporting by connecting with the local security camera system.

[0056] When detecting abnormal behavior, the detection unit can use the zoom function of the drone's camera to obtain detailed images. For example, if abnormal behavior is detected, the detection unit can approach the location and use the camera's zoom function to obtain detailed images. The detection unit can also use the zoom function to focus on a specific person or object. Furthermore, the detection unit can use the zoom function to identify the location where the abnormal behavior occurred and grasp the surrounding situation in detail. As a result, by utilizing the zoom function, detailed information about the abnormal behavior can be obtained, improving detection accuracy.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The patrol unit patrols high-crime areas. For example, the patrol unit uses drones to patrol specific routes. The patrol route is set based on past crime data and the characteristics of the area. For example, the patrol unit prioritizes patrols at times and in places where crime is most prevalent. The patrol unit can also dynamically change its patrol route depending on the drone's remaining battery power and weather conditions. For example, if the battery power is low, the patrol unit sets a route that returns to a charging station. Step 2: The detection unit uses the generation AI to detect abnormal behavior during patrols. The detection unit detects abnormal behavior using, for example, image analysis technology or voice recognition technology. For example, the detection unit detects when a suspicious person stays in a particular location for a long time or suddenly starts running. The generation AI analyzes data collected using, for example, a camera or microphone, and detects abnormal behavior in real time. Step 3: The action unit causes the drone to take action based on the abnormal behavior detected by the detection unit. The action unit, for example, issues a warning or advice through a speaker. For example, it may issue a message such as, "This is not safe. Please move quickly." The action unit can also control the drone's movement and move closer to the location where the abnormal behavior occurred. Step 4: The reporting unit notifies the police as necessary based on the action taken by the action unit. For example, if the reporting unit determines that abnormal behavior is a sign of a crime, it will notify the police and send video footage of the scene. The reporting unit can also automatically generate report content based on the data analyzed by the generation AI. For example, the reporting unit generates report content including information such as the type of abnormal behavior, the location and time of occurrence, and sends it to the police.

[0059] (Example 2) A drone patrol system according to an embodiment of the present invention efficiently patrols high-crime areas, with a generation AI detecting and immediately responding to abnormal behavior and situations. This drone patrol system, equipped with a generation AI mounted on a drone, detects abnormal behavior and situations during patrol. For example, it detects suspicious behavior such as a suspicious individual staying in a particular location for a long time or suddenly starting to run. The generation AI analyzes these abnormalities in real time and causes the drone to take immediate action. The drone approaches the location where an abnormality is detected and issues a warning or advice through a speaker. For example, it may issue a message such as, "This is not safe. Please move immediately." It also contacts the police if necessary. For example, if abnormal behavior is determined to be a sign of a crime, the drone automatically notifies the police and transmits video footage of the scene. This reduces crime in the area and ensures the safety and security of residents. First, a patrol unit is established for the drone to patrol high-crime areas. Next, a detection unit is established for the generation AI to detect abnormal behavior. This detection unit uses image analysis and voice recognition technologies to detect abnormal behavior. For example, it can detect suspicious individuals staying in a particular location for a long time or suddenly running away. Furthermore, an action unit is provided to enable the drone to take immediate action if abnormal behavior is detected. This action unit issues warnings and advice through a speaker. For example, it could issue a message such as, "This is not safe. Please move quickly." Finally, a reporting unit is provided to notify the police if necessary. This reporting unit will notify the police if the generative AI determines that abnormal behavior is a sign of a crime. For example, if abnormal behavior is determined to be a sign of a crime, the drone will automatically notify the police and transmit video footage of the scene. This can help prevent crime in the area and ensure the safety and security of residents. This drone patrol system can help prevent crime in the area and ensure the safety and security of residents.

[0060] A drone patrol system according to an embodiment includes a patrol unit, a detection unit, an action unit, and a reporting unit. The patrol unit patrols high-crime areas. For example, the patrol unit patrols a specific route using a drone. The patrol route is set based on past crime data and local characteristics. For example, the patrol unit prioritizes patrols times and locations where crime is prevalent. The patrol unit can also dynamically change the patrol route depending on the drone's remaining battery level and weather conditions. For example, if the battery level is low, the patrol unit sets a route to return to a charging station. The detection unit uses a generation AI to detect abnormal behavior during patrol. The detection unit detects abnormal behavior using, for example, image analysis technology or voice recognition technology. For example, the detection unit detects when a suspicious person stays in a specific location for a long time or suddenly starts running. The generation AI analyzes data collected using, for example, a camera or microphone and detects abnormal behavior in real time. The action unit causes the drone to take action based on the abnormal behavior detected by the detection unit. The action unit issues warnings and advice, for example, through a speaker. For example, it can issue a message such as, "This is not safe. Please move quickly." The action unit can also control the drone's movement and approach the location where abnormal behavior occurs. The reporting unit notifies the police as needed based on the action performed by the action unit. For example, if the abnormal behavior is determined to be a sign of a crime, the reporting unit notifies the police and transmits footage of the scene. The reporting unit can also automatically generate report content based on data analyzed by the generation AI. For example, the reporting unit generates report content including information such as the type of abnormal behavior, the location and time of occurrence, and sends it to the police. This allows the drone patrol system to prevent crime in the area and ensure the safety and security of residents.

[0061] The detection unit can detect abnormal behavior using image analysis technology or voice recognition technology. Image analysis technology includes, for example, facial recognition, motion detection, and object detection. For example, the detection unit can analyze video data collected using a camera to detect suspicious individuals or abnormal behavior. Facial recognition technology can identify specific individuals and monitor their behavior. Motion detection technology can detect sudden or abnormal movements. Object detection technology can detect the presence of specific objects and track their movements. Voice recognition technology includes, for example, voice command recognition and abnormal sound detection. For example, the detection unit can analyze voice data collected using a microphone to detect abnormal or suspicious sounds. Voice command recognition technology can respond to specific voice commands and take action. Abnormal sound detection technology can detect abnormal sounds such as screams or the sound of glass breaking. As a result, the use of image analysis technology and voice recognition technology improves the accuracy of detecting abnormal behavior. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit may input data acquired by a camera or microphone into the generation AI and cause the generation AI to detect abnormal behavior.

[0062] The action unit can issue warnings and advice through a speaker. For example, the action unit issues warnings and advice through a speaker. For example, it can issue a message such as, "This is not safe. Please move quickly." The action unit can also approach a location where abnormal behavior has occurred and issue a direct warning or advice. For example, if a drone detects abnormal behavior, it approaches the location and issues a warning message through a speaker. Furthermore, the action unit can issue different messages depending on the type of abnormal behavior. For example, if a suspicious person stays in a specific location for a long time, it can issue a message such as, "This is not safe. Please move quickly." If a person suddenly starts running, it can issue a message such as, "Do not run. Please ensure your safety." This allows for immediate response to abnormal behavior by issuing warnings and advice through a speaker. Some or all of the above-described processing in the action unit may be performed using, or without, a generation AI. For example, the action unit can issue a message generated by a generation AI through a speaker.

[0063] The reporting unit can notify the police and transmit video of the scene when it determines that abnormal behavior is a sign of a crime. For example, the reporting unit can notify the police and transmit video of the scene when it determines that abnormal behavior is a sign of a crime. The reporting unit can automatically generate report content based on data analyzed by the generation AI. For example, the reporting unit generates report content including information such as the type of abnormal behavior, the location and time of occurrence, and transmits it to the police. The reporting unit can also transmit audio data along with the video of the scene. For example, the reporting unit can transmit environmental sounds and audio evidence along with the video of the scene. Furthermore, the reporting unit can simultaneously notify related organizations other than the police. For example, the reporting unit can notify the fire department and ambulance team together with the police. This enables a rapid response by notifying the police and transmitting video of the scene when abnormal behavior is determined to be a sign of a crime. Some or all of the above-mentioned processing in the reporting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reporting unit can transmit the report content generated by the generation AI to the police.

[0064] The detection unit can detect when a suspicious person stays in a specific location for a long time or suddenly starts running. The detection unit, for example, detects when a suspicious person stays in a specific location for a long time. For example, the detection unit can analyze video data collected using a camera to detect a person staying in a specific location for a long time. The detection unit can also detect a person who suddenly starts running. For example, the detection unit can detect a person who suddenly starts running using motion detection technology. This detection of suspicious behavior enables early detection of abnormal behavior. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can input video data acquired by a camera into the generation AI and have the generation AI detect suspicious behavior.

[0065] The action unit can transmit a message such as, "This is not safe. Please move quickly." For example, if a drone detects abnormal behavior, it approaches the location and transmits a message such as, "This is not safe. Please move quickly." through a speaker. The action unit can also transmit different messages depending on the type of abnormal behavior. For example, if a suspicious person stays in a specific location for a long time, it can transmit a message such as, "This is not safe. Please move quickly." If a person suddenly starts running, it can transmit a message such as, "Do not run. Please stay safe." This enables effective attention to abnormal behavior by transmitting specific messages. Some or all of the above-described processing in the action unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the action unit can transmit a message generated by a generation AI through a speaker.

[0066] The patrol unit can estimate the user's emotions and dynamically change the patrol route based on the estimated user's emotions. The patrol unit, for example, estimates the user's emotions and dynamically changes the patrol route based on the estimated user's emotions. For example, if the user feels anxious, the patrol unit prioritizes patrolling that area. If the user feels safe, the patrol unit prioritizes patrolling other high-crime areas. If the user feels nervous, the patrol unit frequently patrols that area. This enables more effective patrol by dynamically changing the patrol route based on the user's emotions. Some or all of the above-described processing in the patrol unit may be performed using, or without, a generation AI. For example, the patrol unit may input user emotion data into the generation AI and cause the generation AI to dynamically change the patrol route.

[0067] The patrol unit can analyze past crime data and generate the most effective patrol pattern. The patrol unit, for example, analyzes past crime data and generates the most effective patrol pattern. For example, based on past crime data, it sets patrol routes to match times when crimes are most prevalent. It focuses patrols on specific areas depending on the type of crime. It adjusts the frequency of patrol based on the frequency of crimes. In this way, by analyzing past crime data, it is possible to generate effective patrol patterns. Some or all of the above-mentioned processing in the patrol unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the patrol unit can input past crime data into a generation AI and have the generation AI generate a patrol pattern.

[0068] The patrol unit can adjust the patrol frequency according to the weather and the time of day. The patrol unit adjusts the patrol frequency according to the weather and the time of day, for example. For example, it increases the patrol frequency at night or when it is raining. It decreases the patrol frequency during the day or when it is sunny. It increases the patrol frequency during times when a specific event is held. In this way, efficient patrol is possible by adjusting the patrol frequency according to the weather and the time of day. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, the generation AI, for example. For example, the patrol unit can input data on the weather and the time of day into the generation AI and have the generation AI adjust the patrol frequency.

[0069] The patrol unit can estimate the user's emotions and determine the patrol priority based on the estimated user's emotions. The patrol unit, for example, estimates the user's emotions and determines the patrol priority based on the estimated user's emotions. For example, it prioritizes patrol of areas where the user feels anxious. It postpones patrol of areas where the user feels safe. It frequently patrols areas where the user feels nervous. In this way, determining the patrol priority based on the user's emotions enables more effective patrol. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, the generation AI. For example, the patrol unit can input the user's emotion data into the generation AI and have the generation AI determine the patrol priority.

[0070] The patrol unit can optimize patrol routes based on geographical crime rates. The patrol unit optimizes patrol routes based on geographical crime rates, for example. For example, it prioritizes patrol in areas with high crime rates. It postpones areas with low crime rates. It dynamically changes patrol routes according to fluctuations in crime rates. This enables efficient patrols by optimizing patrol routes based on geographical crime rates. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, a generation AI, for example. For example, the patrol unit can input geographical crime rate data into the generation AI and have the generation AI optimize the patrol routes.

[0071] The patrol unit can adjust the patrol route according to the remaining battery level of the drone. The patrol unit adjusts the patrol route according to, for example, the remaining battery level of the drone. For example, if the remaining battery level is low, the patrol route is shortened. If the remaining battery level is sufficient, the patrol route is extended. A route that passes through a charging station is set according to the remaining battery level. This enables efficient patrol by adjusting the patrol route according to the remaining battery level of the drone. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, the generation AI. For example, the patrol unit may input remaining battery level data of the drone into the generation AI and have the generation AI adjust the patrol route.

[0072] The detection unit can estimate the user's emotions and adjust the detection sensitivity of abnormal behavior based on the estimated user emotions. The detection unit, for example, estimates the user's emotions and adjusts the detection sensitivity of abnormal behavior based on the estimated user emotions. For example, if the user feels anxious, the detection sensitivity is increased. If the user feels relieved, the detection sensitivity is decreased. If the user feels nervous, the detection sensitivity is set to a medium level. This enables more effective detection of abnormal behavior by adjusting the detection sensitivity of abnormal behavior based on the user's emotions. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit may input user emotion data into the generation AI and have the generation AI adjust the detection sensitivity.

[0073] When detecting abnormal behavior, the detection unit can evaluate the degree of abnormality by comparing with past data. For example, when detecting abnormal behavior, the detection unit evaluates the degree of abnormality by comparing with past data. For example, when detecting abnormal behavior, the detection unit evaluates the frequency of abnormal behavior by comparing with past data. For example, the detection unit evaluates the type of abnormal behavior by comparing with past data. For example, the detection unit evaluates the time period of abnormal behavior by comparing with past data. In this way, the accuracy of detecting abnormal behavior is improved by evaluating the degree of abnormality by comparing with past data. Some or all of the above-mentioned processing in the detection unit may be performed using, or without using, the generation AI. For example, the detection unit may input past data into the generation AI and have the generation AI evaluate the degree of abnormality.

[0074] The detection unit can filter surrounding environmental sounds and background noise when detecting abnormal behavior. The detection unit, for example, filters surrounding environmental sounds and background noise when detecting abnormal behavior. For example, the detection unit may filter surrounding environmental sounds to detect abnormal behavior, filter background noise to detect abnormal behavior, or filter specific sound patterns to detect abnormal behavior. By filtering surrounding environmental sounds and background noise, the accuracy of detecting abnormal behavior is improved. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI, for example. For example, the detection unit may input data on environmental sounds and background noise into the generation AI and have the generation AI perform the filtering.

[0075] The detection unit can estimate the user's emotions and determine the priority of abnormal behaviors based on the estimated user emotions. The detection unit, for example, estimates the user's emotions and determines the priority of abnormal behaviors based on the estimated user emotions. For example, if the user feels anxious, the priority of abnormal behaviors is increased. If the user feels relieved, the priority of abnormal behaviors is decreased. If the user feels nervous, the priority of abnormal behaviors is set to medium. This enables more effective response to abnormal behaviors by determining the priority of abnormal behaviors based on the user's emotions. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit may input user emotion data into the generation AI and have the generation AI determine the priority of abnormal behaviors.

[0076] The detection unit can improve detection accuracy based on ambient lighting conditions when detecting abnormal behavior. For example, when detecting abnormal behavior, the detection unit improves detection accuracy by taking ambient lighting conditions into consideration. For example, the detection accuracy of abnormal behavior is improved by taking ambient lighting conditions into consideration. When lighting is dark, the detection accuracy is increased. When lighting is bright, the detection accuracy is decreased. In this way, the detection accuracy of abnormal behavior is improved by taking ambient lighting conditions into consideration. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI, for example. For example, the detection unit can input lighting condition data into the generation AI and have the generation AI improve detection accuracy.

[0077] The detection unit can perform cooperative operations with other drones when detecting abnormal behavior. The detection unit performs cooperative operations with other drones when detecting abnormal behavior, for example. For example, it cooperates with other drones to detect abnormal behavior. It shares information with other drones to detect abnormal behavior. It cooperates with other drones to identify the range of abnormal behavior. By performing cooperative operations with other drones, the accuracy of detecting abnormal behavior is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can input data from other drones into the generation AI and have the generation AI perform cooperative operations.

[0078] The action unit can estimate the user's emotions and adjust the content of the warnings and advice based on the estimated user emotions. The action unit, for example, estimates the user's emotions and adjusts the content of the warnings and advice based on the estimated user emotions. For example, if the user is feeling anxious, the action unit gives the warnings and advice in a gentle tone. If the user is feeling relieved, the action unit gives the warnings and advice in a calm tone. If the user is feeling nervous, the action unit gives the warnings and advice in a calm tone. This allows for a more effective response by adjusting the content of the warnings and advice based on the user's emotions. Some or all of the above-described processing in the action unit may be performed using, or without, a generation AI. For example, the action unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the warnings and advice.

[0079] The action unit can execute different actions depending on the type of abnormal behavior. The action unit executes different actions depending on the type of abnormal behavior, for example. For example, if a suspicious person stays in a specific place for a long time, the action unit may issue a warning. If a person suddenly starts running, the action unit may issue a warning. If a person is destroying something, the action unit may notify the police. This enables effective response by executing appropriate actions depending on the type of abnormal behavior. Some or all of the above-mentioned processing in the action unit may be performed using, or without, a generation AI. For example, the action unit may input the type of abnormal behavior into the generation AI and have the generation AI execute an appropriate action.

[0080] The action unit can monitor the surrounding situation in real time when executing an action and take an appropriate response. The action unit, for example, monitors the surrounding situation in real time when executing an action and takes an appropriate response. For example, it monitors the surrounding situation in real time and gives appropriate warnings or advice. It monitors the surrounding situation in real time and issues appropriate warnings. It monitors the surrounding situation in real time and makes appropriate reports. In this way, by monitoring the surrounding situation in real time, an appropriate response becomes possible. Some or all of the above-mentioned processing in the action unit may be performed using, or without, the generation AI, for example. For example, the action unit can input surrounding situation data into the generation AI and cause the generation AI to take an appropriate response.

[0081] The action unit can estimate the user's emotions and determine the priority of actions based on the estimated user emotions. The action unit, for example, estimates the user's emotions and determines the priority of actions based on the estimated user emotions. For example, if the user feels anxious, the priority of actions is increased. If the user feels relieved, the priority of actions is decreased. If the user feels nervous, the priority of actions is set to medium. This enables a more effective response by determining the priority of actions based on the user's emotions. Some or all of the above-described processing in the action unit may be performed using, or without, a generation AI. For example, the action unit can input user emotion data into the generation AI and have the generation AI determine the priority of actions.

[0082] The action unit can cooperate with other drones when executing an action to achieve an effective response. The action unit, for example, cooperates with other drones when executing an action to achieve an effective response. For example, it can cooperate with other drones to give warnings or advice. It can cooperate with other drones to issue warnings. It can cooperate with other drones to make reports. In this way, cooperation with other drones enables an effective response. Some or all of the above-described processing in the action unit may be performed using, or without, the generation AI, for example. For example, the action unit can input data from other drones into the generation AI and have the generation AI execute a coordinated action.

[0083] The action unit can select the optimal action by taking into consideration the surrounding environmental information when executing an action. For example, the action unit selects the optimal action by taking into consideration the surrounding environmental information when executing an action. For example, the action unit provides optimal warnings or advice by taking into consideration the surrounding environmental information. The action unit issues optimal warnings by taking into consideration the surrounding environmental information. The action unit makes optimal notifications by taking into consideration the surrounding environmental information. In this way, the optimal action can be selected by taking into consideration the surrounding environmental information. Some or all of the above-described processing in the action unit may be performed using, or without using, a generation AI. For example, the action unit can input the surrounding environmental information into the generation AI and have the generation AI select the optimal action.

[0084] The reporting unit can estimate the user's emotions and adjust the content of the report based on the estimated user emotions. The reporting unit, for example, estimates the user's emotions and adjusts the content of the report based on the estimated user emotions. For example, if the user is feeling anxious, detailed content of the report is provided. If the user is feeling relieved, concise content of the report is provided. If the user is feeling nervous, content that focuses on the main points is provided. This allows for more effective reporting by adjusting the content of the report based on the user's emotions. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI, for example. For example, the reporting unit can input user emotion data into the generation AI and have the generation AI adjust the content of the report.

[0085] The reporting unit can select the optimal reporting method by referring to past reporting history when reporting. The reporting unit, for example, selects the optimal reporting method by referring to past reporting history when reporting. For example, the reporting unit selects the optimal reporting method based on the past reporting history. The reporting unit selects the optimal reporting content based on the past reporting history. The reporting unit selects the optimal reporting timing based on the past reporting history. In this way, the optimal reporting method can be selected by referring to the past reporting history. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without using, the generation AI, for example. For example, the reporting unit can input the past reporting history into the generation AI and have the generation AI select the optimal reporting method.

[0086] The reporting unit can also transmit audio data along with the video of the scene when reporting. The reporting unit, for example, transmits audio data along with the video of the scene when reporting. For example, when reporting, audio data is transmitted along with the video of the scene. When reporting, environmental sounds are transmitted along with the video of the scene. When reporting, evidential audio is transmitted along with the video of the scene. In this way, by transmitting audio data along with the video of the scene, more detailed information can be provided. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit can input the video and audio data of the scene into the generation AI and have the generation AI execute the transmission.

[0087] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated user emotions. The reporting unit, for example, estimates the user's emotions and determines the priority of reports based on the estimated user emotions. For example, if the user feels anxious, the priority of reports is increased. If the user feels relieved, the priority of reports is decreased. If the user feels nervous, the priority of reports is set to medium. This enables more effective reporting by determining the priority of reports based on the user's emotions. Some or all of the above-described processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit can input user emotion data into the generation AI and have the generation AI determine the priority of reports.

[0088] The reporting unit can simultaneously notify related organizations other than the police when making a report. The reporting unit, for example, simultaneously notifies related organizations other than the police when making a report. For example, when making a report, it notifies the fire department along with the police. When making a report, it notifies an ambulance team along with the police. When making a report, it notifies a local crime prevention association along with the police. By simultaneously notifying related organizations other than the police, a rapid response is possible. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit can input the content of the report into the generation AI and have the generation AI execute reporting to related organizations.

[0089] The reporting unit can automatically translate the report content at the time of reporting and provide multilingual support. The reporting unit, for example, automatically translates the report content at the time of reporting and provides multilingual support. For example, at the time of reporting, the report content is automatically translated and the report is made in English. At the time of reporting, the report content is automatically translated and the report is made in Spanish. At the time of reporting, the report content is automatically translated and the report is made in Chinese. In this way, multilingual support is possible by automatically translating the report content. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without using, a generation AI, for example. For example, the reporting unit can input the report content into a generation AI and have the generation AI perform translation and multilingual support. === Hard Collateral 1-1 === Each of the multiple elements, including the patrol unit, detection unit, action unit, and reporting 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 by the smart device 14, and the drone's control unit 46A patrols a specific route. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects abnormal behavior using the camera 42 and microphone 38B. The action unit is realized, for example, by the control unit 46A of the smart device 14, and issues warnings or advice through the speaker 40B. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the police and transmits video of the scene if abnormal behavior is determined to be a sign of a crime. === Hard Collateral 1-2 === Each of the multiple elements including the patrol unit, detection unit, action unit, and reporting 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 by the smart glasses 214, and the drone's control unit 46A patrols a specific route. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects abnormal behavior using the camera 42 and microphone 238. The action unit is realized, for example, by the control unit 46A of the smart glasses 214, and issues warnings or advice through the speaker 240. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the police and transmits video of the scene if abnormal behavior is determined to be a sign of a crime. === Hard Collateral 1-3 === Each of the multiple elements including the patrol unit, detection unit, action unit, and reporting unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the patrol unit is realized by the headset-type terminal 314, and the drone's control unit 46A patrols a specific route. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects abnormal behavior using the camera 42 and microphone 238. The action unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and issues warnings and advice through the speaker 240. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the police and transmits video of the scene if abnormal behavior is determined to be a sign of a crime. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned patrol unit, detection unit, action unit, and reporting unit 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 the robot 414, and the drone control unit 46A patrols a specific route. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and detects abnormal behavior using the camera 42 and microphone 238. The action unit is realized, for example, by the control unit 46A of the robot 414, and issues warnings and advice through the speaker 240. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the police and transmits video of the scene if abnormal behavior is determined to be a sign of a crime.

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

[0091] The patrol unit can optimize its patrol routes by taking into account the geographical characteristics of high-crime areas. For example, it can set routes that avoid areas where drones are difficult to fly, such as narrow alleys or between high-rise buildings. The patrol unit can also collect feedback from local residents and adjust its patrol routes based on that information. For example, if residents feel unsafe in a particular area, it can change its route to prioritize patrolling that area. Furthermore, the patrol unit can obtain information about local events and focus on patrolling the surrounding area when an event is taking place. This enables effective patrols that reflect the characteristics of the area and the opinions of residents.

[0092] When detecting abnormal behavior, the detection unit can improve detection accuracy by taking into account environmental data such as ambient temperature and humidity. For example, when the temperature is high, there is a risk of heatstroke, so the detection sensitivity of abnormal behavior can be increased. Also, when the humidity is high, there is a possibility that visibility will be reduced, so the detection accuracy can be adjusted. Furthermore, the detection unit can dynamically change the detection range of abnormal behavior by taking into account meteorological data such as wind speed and wind direction. In this way, by taking environmental data into account, the detection accuracy of abnormal behavior is improved.

[0093] When the action unit detects abnormal behavior, it can turn on the drone's lights to illuminate the surroundings. For example, if abnormal behavior is detected at night, the drone's lights can be turned on to brighten the surroundings and improve visibility. The action unit can also attract attention by changing the light's lighting pattern. For example, by flashing the lights, it can notify people in the vicinity that abnormal behavior is occurring. Furthermore, the action unit can change the color of the lights to respond according to the type of abnormal behavior. This makes it possible to respond effectively to abnormal behavior through visual actions.

[0094] When the reporting unit detects abnormal behavior, it can connect with the local security camera system to obtain more detailed video data. For example, if a drone detects abnormal behavior, it can obtain footage from security cameras in the surrounding area and add it to the report. The reporting unit can also integrate security camera footage with drone footage to provide a wider field of view. Furthermore, the reporting unit can obtain real-time data from the security camera system and accurately determine the location and time of the abnormal behavior. This allows for more detailed and accurate reporting by connecting with the local security camera system.

[0095] When detecting abnormal behavior, the detection unit can use the zoom function of the drone's camera to obtain detailed images. For example, if abnormal behavior is detected, the detection unit can approach the location and use the camera's zoom function to obtain detailed images. The detection unit can also use the zoom function to focus on a specific person or object. Furthermore, the detection unit can use the zoom function to identify the location where the abnormal behavior occurred and grasp the surrounding situation in detail. As a result, by utilizing the zoom function, detailed information about the abnormal behavior can be obtained, improving detection accuracy.

[0096] The patrol unit can estimate the user's emotions and dynamically change the patrol route based on the estimated user's emotions. For example, if the user feels anxious, the patrol unit will prioritize patrolling that area. If the user feels safe, the patrol unit will prioritize patrolling other high-crime areas. If the user feels nervous, the patrol unit will frequently patrol that area. This allows for more effective patrol by dynamically changing the patrol route based on the user's emotions. Some or all of the above-mentioned processing in the patrol unit may be performed using, or without, a generation AI. For example, the patrol unit can input user emotion data into the generation AI and have the generation AI dynamically change the patrol route.

[0097] The detection unit can estimate the user's emotions and adjust the detection sensitivity of abnormal behavior based on the estimated user emotions. For example, if the user feels anxious, the detection sensitivity is increased. If the user feels relieved, the detection sensitivity is decreased. If the user feels nervous, the detection sensitivity is set to a medium level. This allows for more effective detection of abnormal behavior by adjusting the detection sensitivity of abnormal behavior based on the user's emotions. Some or all of the above-described processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input user emotion data into the generation AI and have the generation AI adjust the detection sensitivity.

[0098] The action unit can estimate the user's emotions and adjust the content of the warnings and advice based on the estimated user emotions. For example, if the user feels anxious, the warnings and advice are given in a gentle tone. If the user feels relieved, the warnings and advice are given in a calm tone. If the user feels nervous, the warnings and advice are given in a calm tone. This allows for a more effective response by adjusting the content of the warnings and advice based on the user's emotions. Some or all of the above-mentioned processing in the action unit may be performed using, or without, a generation AI. For example, the action unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the warnings and advice.

[0099] The reporting unit can estimate the user's emotions and adjust the content of the report based on the estimated user's emotions. For example, if the user feels anxious, detailed content of the report is provided. If the user feels relieved, concise content of the report is provided. If the user feels nervous, content that focuses on the main points is provided. This allows for more effective reporting by adjusting the content of the report based on the user's emotions. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit can input user emotion data into the generation AI and have the generation AI adjust the content of the report.

[0100] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated user emotions. For example, if the user feels anxious, the priority of reports can be increased. If the user feels relieved, the priority of reports can be decreased. If the user feels nervous, the priority of reports can be set to medium. This allows for more effective reporting by determining the priority of reports based on the user's emotions. Some or all of the above-described processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit can input user emotion data into the generation AI and have the generation AI determine the priority of reports.

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

[0102] Step 1: The patrol unit patrols high-crime areas. For example, the patrol unit uses drones to patrol specific routes. The patrol route is set based on past crime data and the characteristics of the area. For example, the patrol unit prioritizes patrols at times and in places where crime is most prevalent. The patrol unit can also dynamically change its patrol route depending on the drone's remaining battery power and weather conditions. For example, if the battery power is low, the patrol unit sets a route that returns to a charging station. Step 2: The detection unit uses the generation AI to detect abnormal behavior during patrols. The detection unit detects abnormal behavior using, for example, image analysis technology or voice recognition technology. For example, the detection unit detects when a suspicious person stays in a particular location for a long time or suddenly starts running. The generation AI analyzes data collected using, for example, a camera or microphone, and detects abnormal behavior in real time. Step 3: The action unit causes the drone to take action based on the abnormal behavior detected by the detection unit. The action unit, for example, issues a warning or advice through a speaker. For example, it may issue a message such as, "This is not safe. Please move quickly." The action unit can also control the drone's movement and move closer to the location where the abnormal behavior occurred. Step 4: The reporting unit notifies the police as necessary based on the action taken by the action unit. For example, if the reporting unit determines that abnormal behavior is a sign of a crime, it will notify the police and send video footage of the scene. The reporting unit can also automatically generate report content based on the data analyzed by the generation AI. For example, the reporting unit generates report content including information such as the type of abnormal behavior, the location and time of occurrence, and sends it to the police.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. The patrol division patrols high-crime areas, a detection unit that analyzes the data collected by the patrol unit and detects abnormal behavior; an action unit that causes the drone to take action based on the abnormal behavior detected by the detection unit; a reporting unit that reports to the police based on the action taken by the action unit; Equipped with A system characterized by:

2. The detection unit Detecting abnormal behavior using image analysis or voice recognition technology 2. The system of claim 1.

3. The action unit is Give warnings and advice through a speaker 2. The system of claim 1.

4. The reporting unit If abnormal behavior is determined to be a sign of a crime, the system will notify the police and send footage of the scene 2. The system of claim 1.

5. The detection unit Detects suspicious individuals staying in a particular location for a long time or suddenly taking off running 2. The system of claim 1.

6. The action unit is Sending messages encouraging people to move 2. The system of claim 1.

7. The circulating unit Estimate the user's emotions and dynamically change the tour route based on the estimated user emotions.

2. The system of claim 1.

8. The circulating unit Analyze historical crime data to generate the most effective patrol patterns 2. The system of claim 1.

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