system

Abstract

The system according to this embodiment aims to analyze surveillance camera footage in real time, detect signs of crime, and respond immediately. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit collects video from surveillance cameras. The analysis unit analyzes the video collected by the collection unit. The alert unit issues an alert based on the results of the analysis by the analysis unit. The notification unit notifies the police based on the alert issued by the alert unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to analyze the video of a surveillance camera in real time, detect signs of crime, and respond immediately.

[0005] The system according to the embodiment aims to analyze the video of a surveillance camera in real time, detect signs of crime, and respond immediately.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit collects video footage from surveillance cameras. The analysis unit analyzes the video footage collected by the collection unit. The alert unit issues an alert based on the results of the analysis by the analysis unit. The notification unit notifies the police based on the alert issued by the alert unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze surveillance camera footage in real time, detect signs of crime, and respond immediately. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The surveillance system according to an embodiment of the present invention is a system that analyzes video recorded by surveillance cameras in real time, uses AI to analyze pre-crime footage and crime records, issues alerts, and notifies the police. This surveillance system detects risk from "video footage of the few minutes before and after an incident or accident" captured by surveillance cameras, and automatically notifies the police in near real time only when a risk is actually detected. Specifically, it identifies "the suspect's physical characteristics" and "actions at a scene where an incident or accident is likely" from the image, performs the following matching / inference using a generating AI fed with past crime records, and simultaneously transmits the above image to the police in real time, automatically generates the following information and notifies / recommends it to the police. For example, it infers the suspect's next actions (such as specific escape methods) derived from similar past cases, and infers the next actions the police should take in real time derived from similar past cases. This surveillance system enables a higher level of incident and accident resolution and crime deterrence than existing security and safety providers, which can lead to a reduction in the number of crimes. First, AI analyzes the footage captured by surveillance cameras in real time. During this process, the AI ​​identifies the suspect's physical characteristics and actions likely to occur at a crime or accident scene. For example, it can automatically detect intruders attempting to break glass or suspicious individuals trying to force their way through office gates. Next, the AI ​​reads past crime records and infers the suspect's next actions and the next steps the police should take, based on similar cases. For instance, it can infer the escape route chosen by a suspect in a similar past case and the actions the police should take, and notify the police. Furthermore, the AI ​​generates natural language output of the estimated suspect's next actions and the police's next course of action, attaching it to the video and automatically sending it to the police. This enables the police to respond quickly and appropriately. Because this surveillance system analyzes surveillance camera footage in real time, it significantly improves crime deterrence. Additionally, direct reporting to the police allows for a swift response without the need for security guards, preventing criminals from escaping. Furthermore, AI-powered reasoning and recommendations will support police decision-making and contribute to solving crimes and accidents. This will enable the surveillance system to achieve early detection and rapid response to crimes, thereby contributing to crime deterrence.

[0029] The surveillance system according to this embodiment comprises a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit collects video from surveillance cameras. The collection unit can, for example, collect video from surveillance cameras in real time. The collection unit can also collect video from surveillance cameras periodically. The collection unit can, for example, collect video from surveillance cameras in high resolution. The analysis unit analyzes the video collected by the collection unit. The analysis unit can, for example, identify "physical characteristics of the suspect" and "actions at a crime scene with a high probability of an incident or accident" from the images. The analysis unit can also read past crime records and infer the suspect's next actions and the next actions the police should take, derived from similar cases. The analysis unit can, for example, use image analysis technology to identify the physical characteristics of the suspect. The analysis unit can, for example, use motion analysis technology to identify actions at a crime scene with a high probability of an incident or accident. The alert unit issues an alert based on the results analyzed by the analysis unit. The alert unit can, for example, send alerts to security guards or police based on the analysis results. The alert unit can, for example, customize the content of the alerts before sending them. The alert unit can, for example, adjust the timing of the alerts. The reporting unit reports to the police based on the alerts sent by the alert unit. The reporting unit can, for example, generate predictions of the suspect's next actions and the next actions the police should take in natural language, attach them to the video, and automatically send them to the police. The reporting unit can, for example, update the report content in real time and generate the optimal report content according to changes in the situation. In addition to the report content, the reporting unit can, for example, attach maps and detailed information about the scene so that the police can respond quickly. As a result, the surveillance system according to the embodiment can analyze surveillance camera footage in real time, analyze pre-crime footage and crime records using AI, send alerts, and report to the police.

[0030] The collection unit collects video footage from surveillance cameras. For example, the collection unit can collect video footage in real time. Specifically, surveillance cameras capture high-resolution video and transmit it to the collection unit via a network. The collection unit receives this video in real time and stores it in a central database. The collection unit can also collect video footage from surveillance cameras periodically. For example, it can capture video at specific time intervals and store it in the database. This allows the collection unit to not only monitor in real time but also refer to past footage. The collection unit can collect video footage from surveillance cameras in high resolution. High-resolution video clearly shows even the smallest details, which is useful for identifying suspects and conducting detailed analysis of incidents. The collection unit can efficiently manage this video and collaborate with other systems and departments as needed. For example, collected video can be stored on a cloud server, making it accessible to the analysis and alerting units. Furthermore, the collection unit can adjust the frequency and resolution of video collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to efficiently and effectively collect video footage, improving the overall performance of the system.

[0031] The analysis unit analyzes the video footage collected by the collection unit. For example, the analysis unit can identify "the suspect's physical characteristics" and "actions likely to occur at a crime / accident scene" from the images. Specifically, it uses image analysis technology to identify the suspect's physical characteristics such as face, clothing, and body type. This involves the use of deep learning-based facial recognition algorithms and object detection algorithms. It also uses motion analysis technology to identify actions likely to occur at a crime / accident scene. For example, motion analysis algorithms are used to detect abnormal behavior or suspicious movements. The analysis unit can also read past crime records and infer the suspect's next actions and the next actions the police should take, derived from similar cases. This involves using natural language processing technology to analyze past crime records and extract patterns. The analysis unit can, for example, use image analysis technology to identify the suspect's physical characteristics. This allows the analysis unit to quickly and accurately analyze the collected video footage and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas or time periods based on past crime data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The alert unit issues alerts based on the results analyzed by the analysis unit. For example, the alert unit can issue alerts to security guards or police officers based on the analysis results. Specifically, it receives information from the analysis unit and sends notifications to security guards or police officers in real time. The alert unit can, for example, customize the content of alerts it issues. For example, it can generate alerts that include information such as the suspect's physical characteristics, details of their actions, and the location where the incident occurred. The alert unit can, for example, adjust the timing of alert issuance. For example, it can be set to issue alerts only when specific conditions are met. This allows the alert unit to provide necessary information quickly and accurately, helping security guards and police officers take appropriate action. Furthermore, the alert unit manages the alert issuance history and can refer to past alert information. This allows the alert unit to perform analysis and make improvements based on past cases. In addition, the alert unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the alert unit to provide users with quick and reliable instructions and minimize the risk of disaster.

[0033] The reporting unit notifies the police based on alerts issued by the alert unit. For example, the reporting unit can generate natural language data for the police, including estimated results of the suspect's next actions and the actions the police should take, and automatically send this data attached to the video. Specifically, based on information from the analysis unit, it estimates the suspect's next actions and describes the actions the police should take in natural language. This uses natural language generation technology to provide information in a format that the police can easily understand. For example, the reporting unit can update the report content in real time and generate the most appropriate report content according to changes in the situation. For example, if the suspect's location or actions change, it immediately updates the report content and provides the police with the latest information. In addition to the report content, the reporting unit can attach maps and detailed information about the scene to enable the police to respond quickly. This allows the police to accurately grasp the situation at the scene and take a swift and appropriate response. Furthermore, the reporting unit can manage the report history and refer to past report content. This allows the reporting unit to perform analysis and make improvements based on past cases. In addition, the reporting unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the reporting department to provide information to the police quickly and reliably, supporting the early detection and rapid response to crimes.

[0034] The analysis unit can identify the suspect's physical characteristics and actions at a crime scene that are likely to be the site of an incident or accident from an image. For example, the analysis unit can use image analysis technology to identify the suspect's physical characteristics. For example, the analysis unit can identify physical characteristics such as height, weight, and clothing. For example, the analysis unit can use motion analysis technology to identify actions at a crime scene that are likely to be the site of an incident or accident. For example, the analysis unit can identify suspicious movements or fleeing behavior. For example, the analysis unit can combine image analysis technology and motion analysis technology to comprehensively identify the suspect's physical characteristics and actions. This makes it possible to detect crimes early by identifying the suspect's physical characteristics and actions at a crime scene that are likely to be the site of an incident or accident from an image.

[0035] The analysis unit can read past crime records and infer the suspect's next actions and the next actions the police should take, derived from similar cases. For example, the analysis unit reads past crime records from a database and analyzes the suspect's behavior patterns. For example, the analysis unit can infer the suspect's next actions based on past crime records. For example, the analysis unit can infer the next actions the police should take based on past crime records. For example, the analysis unit can learn the suspect's behavior patterns from past crime records using machine learning algorithms and infer the next actions. For example, the analysis unit can learn the actions the police should take from past crime records using machine learning algorithms and infer the next actions. This enables a quick and appropriate response by inferring the suspect's next actions and the next actions the police should take based on past crime records.

[0036] The reporting unit can generate predictions of the suspect's next actions and the next actions the police should take in natural language, attach them to the video, and automatically send them to the police. For example, the reporting unit can use a generative AI to generate predictions of the suspect's next actions in natural language. For example, the reporting unit's generative AI can generate a prediction that "the suspect is likely to choose an escape route next." For example, the reporting unit can use a generative AI to generate a prediction that "the police should block the escape route next." For example, the reporting unit can use a generative AI to automatically attach the prediction results to the video and send them automatically. For example, the reporting unit can attach the prediction results generated by the generative AI to the video and automatically send them to the police. This supports the police's swift and appropriate response by generating predictions of the suspect's next actions and the next actions the police should take in natural language, attaching them to the video, and automatically sending them to the police.

[0037] The collection unit can collect surveillance camera footage in real time. For example, the collection unit can collect surveillance camera footage in real time and transmit it to the analysis unit. For example, the collection unit can collect surveillance camera footage in real time and store it in a database. For example, the collection unit can collect surveillance camera footage in real time and notify security guards. This allows for the early detection of crimes by collecting surveillance camera footage in real time.

[0038] The alert unit can issue alerts based on analysis results. For example, the alert unit can issue alerts to security guards or police officers based on analysis results. For example, the alert unit can issue an alert to security guards based on analysis results, allowing them to rush to the scene. For example, the alert unit can issue an alert to police officers based on analysis results, allowing them to rush to the scene. For example, the alert unit can customize the content of alerts based on analysis results before issuing them. For example, the alert unit can provide detailed information about the alert based on analysis results and notify security guards or police officers accordingly. For example, the alert unit can adjust the timing of alert issuance based on analysis results. For example, the alert unit can optimize the timing of alert issuance based on analysis results, enabling a rapid response. This makes it possible to detect crimes early by issuing alerts based on analysis results.

[0039] The data collection unit can dynamically change the placement and angle of surveillance cameras to collect optimal footage. For example, the data collection unit can automatically change the placement of surveillance cameras to reduce blind spots. For example, the data collection unit can dynamically adjust the angle of surveillance cameras to constantly monitor important areas. For example, the data collection unit can periodically change the placement of surveillance cameras to make it difficult to predict criminal activity. This allows for optimal footage collection by dynamically changing the placement and angle of surveillance cameras.

[0040] The acquisition unit can evaluate the quality of the collected video in real time and adjust the acquisition method as needed. For example, the acquisition unit can evaluate the resolution of the video in real time and adjust the camera settings as needed. For example, the acquisition unit can evaluate the brightness and contrast of the video in real time and acquire the optimal video. For example, the acquisition unit can evaluate the noise level of the video in real time and perform noise reduction as needed. This enables optimal video acquisition by evaluating the quality of the collected video in real time and adjusting the acquisition method as needed.

[0041] The data collection unit can collect not only video from surveillance cameras but also other sensor data such as audio and temperature. For example, the data collection unit can collect ambient audio data in addition to video from surveillance cameras. For example, the data collection unit can collect ambient temperature data in addition to video from surveillance cameras. For example, the data collection unit can collect ambient vibration data in addition to video from surveillance cameras. This allows for more multifaceted monitoring by collecting not only video from surveillance cameras but also other sensor data such as audio and temperature.

[0042] The data collection unit can integrate footage from multiple surveillance cameras to perform wide-area surveillance. For example, the data collection unit can integrate footage from multiple surveillance cameras to perform wide-area surveillance. For example, the data collection unit can integrate footage from multiple surveillance cameras in real time to monitor important areas. For example, the data collection unit can integrate footage from multiple surveillance cameras to reduce blind spots. This enables wide-area surveillance by integrating footage from multiple surveillance cameras.

[0043] The analysis unit can analyze not only video but also other sensor data such as audio and temperature. For example, the analysis unit can analyze audio data in addition to video data to detect abnormal sounds. For example, the analysis unit can analyze temperature data in addition to video data to detect abnormal temperature changes. For example, the analysis unit can analyze vibration data in addition to video data to detect abnormal vibrations. This allows for a more multifaceted analysis by analyzing not only video but also other sensor data such as audio and temperature.

[0044] The analysis unit can update analysis results in real time and adjust the analysis method according to changes in the situation. For example, the analysis unit can update analysis results in real time and adjust the analysis method according to changes in the situation. For example, the analysis unit can update analysis results in real time and adjust the analysis method based on new information. For example, the analysis unit can update analysis results in real time and adjust the analysis method when an anomaly is detected. This allows for more appropriate analysis by updating analysis results in real time and adjusting the analysis method according to changes in the situation.

[0045] The analysis unit can perform analysis by referring not only to past crime records but also to current crime trends and local crime data. For example, the analysis unit can perform analysis by referring to current crime trends in addition to past crime records. For example, the analysis unit can perform analysis by referring to local crime data in addition to past crime records. For example, the analysis unit can perform analysis by integrating current crime trends and local crime data in addition to past crime records. This allows for more appropriate analysis by referring not only to past crime records but also to current crime trends and local crime data.

[0046] The analysis unit can share the results of video analysis in conjunction with other systems and propose comprehensive crime prevention measures. For example, the analysis unit can share the results of video analysis in conjunction with other security systems. For example, the analysis unit can share the results of video analysis in conjunction with police systems. For example, the analysis unit can share the results of video analysis in conjunction with other systems and propose comprehensive crime prevention measures. This allows for the proposal of comprehensive crime prevention measures by sharing the results of video analysis in conjunction with other systems.

[0047] The alert unit can not only issue alerts but also customize the content of notifications sent to security guards and police. For example, the alert unit can customize the content of notifications sent to security guards in addition to issuing alerts. For example, the alert unit can customize the content of notifications sent to police in addition to issuing alerts. For example, the alert unit can customize the content of notifications sent to security guards and police in addition to issuing alerts. This allows for more appropriate responses by customizing the content of notifications sent to security guards and police, in addition to issuing alerts.

[0048] The alert unit can adjust the timing of alert transmission in real time, enabling it to transmit alerts at the optimal time. For example, the alert unit can adjust the timing of alert transmission in real time, enabling it to transmit alerts based on new information. For example, the alert unit can adjust the timing of alert transmission in real time, enabling it to transmit alerts when an anomaly is detected. This real-time adjustment of alert transmission timing allows for the transmission of alerts at a more appropriate time.

[0049] The alert unit can diversify its methods of sending alerts, enabling alerts to be sent via multiple methods, such as voice and text messages. For example, the alert unit can diversify its methods of sending alerts, enabling alerts to be sent via voice. For example, the alert unit can diversify its methods of sending alerts, enabling alerts to be sent via text messages. By diversifying the methods of sending alerts, alerts can be sent through a wider variety of means.

[0050] The notification unit can update notification content in real time and generate the most appropriate notification content in response to changes in the situation. For example, the notification unit can update notification content in real time and generate the most appropriate notification content in response to changes in the situation. For example, the notification unit can update notification content in real time and generate the most appropriate notification content based on new information. For example, the notification unit can update notification content in real time and generate the most appropriate notification content when an anomaly is detected. This enables more appropriate notifications by updating notification content in real time and generating the most appropriate notification content in response to changes in the situation.

[0051] The reporting department can attach maps and detailed information about the scene in addition to the content of the report, enabling the police to respond quickly. For example, the reporting department can attach maps in addition to the content of the report, enabling the police to respond quickly. For example, the reporting department can attach maps and detailed information about the scene in addition to the content of the report, enabling the police to respond quickly. This allows for more appropriate reporting by attaching maps and detailed information about the scene in addition to the content of the report, enabling the police to respond quickly.

[0052] The reporting department can generate reports in multiple languages ​​and respond to international crimes. For example, the reporting department can generate reports in multiple languages ​​and respond to international crimes. For example, the reporting department can generate reports in multiple languages ​​and respond to foreign police officers. For example, the reporting department can generate reports in multiple languages ​​and respond quickly to international crimes. This allows for the generation of reports in multiple languages, enabling responses to international crimes.

[0053] The reporting department can share reported information in conjunction with other security systems and propose comprehensive crime prevention measures. For example, the reporting department can share reported information in conjunction with other security systems. For example, the reporting department can share reported information in conjunction with police systems. For example, the reporting department can share reported information in conjunction with other security systems and propose comprehensive crime prevention measures. This allows for the proposal of comprehensive crime prevention measures by sharing reported information in conjunction with other security systems.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The monitoring system may also include a voice recognition unit. This unit can collect and analyze ambient sounds in addition to the video feed from the surveillance camera. For example, the voice recognition unit can detect abnormal sounds such as the sound of breaking glass or shouting and transmit them to the analysis unit. The voice recognition unit can also detect specific keywords and notify the alert unit. This allows for the early detection of anomalies that cannot be detected by video alone, by analyzing the audio data.

[0056] The analysis unit can further analyze local crime data. For example, it can analyze patterns of crimes that frequently occur in a particular area and predict the behavior of suspects. Based on local crime data, the analysis unit can infer the next actions that the police should take. For example, it can predict escape routes in a particular area and notify the police. This allows for more accurate analysis by utilizing local crime data.

[0057] The collection unit can also use drones to collect footage from surveillance cameras. For example, the collection unit can fly drones to collect footage over a wide area. The collection unit can use drones to collect footage from high places or dangerous locations. The collection unit can use drones to track suspects on the move and collect footage. This makes it possible to conduct wide-area surveillance that is difficult with conventional fixed cameras by utilizing drones.

[0058] The analysis unit can analyze temperature data in addition to video data. For example, it can analyze temperature data and detect abnormal temperature changes. Based on the temperature data, the analysis unit can detect the possibility of a fire and notify the police or fire department. Based on the temperature data, the analysis unit can identify the location where a suspect is hiding. In this way, by utilizing temperature data, anomalies that cannot be detected by video alone can be detected at an early stage.

[0059] The analysis unit can further analyze audio data and detect abnormal sounds. For example, it can detect abnormal sounds such as the sound of breaking glass or screaming and notify the police. Based on the audio data, the analysis unit can pinpoint the location of a suspect. Based on the audio data, the analysis unit can infer details of the incident. In this way, by analyzing audio data, it is possible to detect anomalies that cannot be detected by video alone at an early stage.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects footage from surveillance cameras. The collection unit can, for example, collect footage from surveillance cameras in real time. It can also collect footage periodically and in high resolution. Step 2: The analysis unit analyzes the video footage collected by the collection unit. For example, the analysis unit can identify "the suspect's physical characteristics" and "actions at a crime / accident scene that are likely to occur" from the images. It can also read past crime records and infer the suspect's next actions and the next actions the police should take, based on similar cases. Step 3: The alert unit issues an alert based on the results analyzed by the analysis unit. For example, the alert unit can issue an alert to security guards or police officers based on the analysis results. It is also possible to customize the content of the alert and adjust the timing of the alert. Step 4: The reporting unit notifies the police based on the alerts issued by the alert unit. For example, the reporting unit can generate predictions of the suspect's next actions and the next actions the police should take in natural language, attach them to the video, and automatically send them to the police. It can also update the report content in real time, generate the most appropriate report content according to changes in the situation, and attach maps and detailed information about the scene.

[0062] (Example of form 2) The surveillance system according to an embodiment of the present invention is a system that analyzes video recorded by surveillance cameras in real time, uses AI to analyze pre-crime footage and crime records, issues alerts, and notifies the police. This surveillance system detects risk from "video footage of the few minutes before and after an incident or accident" captured by surveillance cameras, and automatically notifies the police in near real time only when a risk is actually detected. Specifically, it identifies "the suspect's physical characteristics" and "actions at a scene where an incident or accident is likely" from the image, performs the following matching / inference using a generating AI fed with past crime records, and simultaneously transmits the above image to the police in real time, automatically generates the following information and notifies / recommends it to the police. For example, it infers the suspect's next actions (such as specific escape methods) derived from similar past cases, and infers the next actions the police should take in real time derived from similar past cases. This surveillance system enables a higher level of incident and accident resolution and crime deterrence than existing security and safety providers, which can lead to a reduction in the number of crimes. First, AI analyzes the footage captured by surveillance cameras in real time. During this process, the AI ​​identifies the suspect's physical characteristics and actions likely to occur at a crime or accident scene. For example, it can automatically detect intruders attempting to break glass or suspicious individuals trying to force their way through office gates. Next, the AI ​​reads past crime records and infers the suspect's next actions and the next steps the police should take, based on similar cases. For instance, it can infer the escape route chosen by a suspect in a similar past case and the actions the police should take, and notify the police. Furthermore, the AI ​​generates natural language output of the estimated suspect's next actions and the police's next course of action, attaching it to the video and automatically sending it to the police. This enables the police to respond quickly and appropriately. Because this surveillance system analyzes surveillance camera footage in real time, it significantly improves crime deterrence. Additionally, direct reporting to the police allows for a swift response without the need for security guards, preventing criminals from escaping. Furthermore, AI-powered reasoning and recommendations will support police decision-making and contribute to solving crimes and accidents. This will enable the surveillance system to achieve early detection and rapid response to crimes, thereby contributing to crime deterrence.

[0063] The surveillance system according to this embodiment comprises a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit collects video from surveillance cameras. The collection unit can, for example, collect video from surveillance cameras in real time. The collection unit can also collect video from surveillance cameras periodically. The collection unit can, for example, collect video from surveillance cameras in high resolution. The analysis unit analyzes the video collected by the collection unit. The analysis unit can, for example, identify "physical characteristics of the suspect" and "actions at a crime scene with a high probability of an incident or accident" from the images. The analysis unit can also read past crime records and infer the suspect's next actions and the next actions the police should take, derived from similar cases. The analysis unit can, for example, use image analysis technology to identify the physical characteristics of the suspect. The analysis unit can, for example, use motion analysis technology to identify actions at a crime scene with a high probability of an incident or accident. The alert unit issues an alert based on the results analyzed by the analysis unit. The alert unit can, for example, send alerts to security guards or police based on the analysis results. The alert unit can, for example, customize the content of the alerts before sending them. The alert unit can, for example, adjust the timing of the alerts. The reporting unit reports to the police based on the alerts sent by the alert unit. The reporting unit can, for example, generate predictions of the suspect's next actions and the next actions the police should take in natural language, attach them to the video, and automatically send them to the police. The reporting unit can, for example, update the report content in real time and generate the optimal report content according to changes in the situation. In addition to the report content, the reporting unit can, for example, attach maps and detailed information about the scene so that the police can respond quickly. As a result, the surveillance system according to the embodiment can analyze surveillance camera footage in real time, analyze pre-crime footage and crime records using AI, send alerts, and report to the police.

[0064] The collection unit collects video footage from surveillance cameras. For example, the collection unit can collect video footage in real time. Specifically, surveillance cameras capture high-resolution video and transmit it to the collection unit via a network. The collection unit receives this video in real time and stores it in a central database. The collection unit can also collect video footage from surveillance cameras periodically. For example, it can capture video at specific time intervals and store it in the database. This allows the collection unit to not only monitor in real time but also refer to past footage. The collection unit can collect video footage from surveillance cameras in high resolution. High-resolution video clearly shows even the smallest details, which is useful for identifying suspects and conducting detailed analysis of incidents. The collection unit can efficiently manage this video and collaborate with other systems and departments as needed. For example, collected video can be stored on a cloud server, making it accessible to the analysis and alerting units. Furthermore, the collection unit can adjust the frequency and resolution of video collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to efficiently and effectively collect video footage, improving the overall performance of the system.

[0065] The analysis unit analyzes the video footage collected by the collection unit. For example, the analysis unit can identify "the suspect's physical characteristics" and "actions likely to occur at a crime / accident scene" from the images. Specifically, it uses image analysis technology to identify the suspect's physical characteristics such as face, clothing, and body type. This involves the use of deep learning-based facial recognition algorithms and object detection algorithms. It also uses motion analysis technology to identify actions likely to occur at a crime / accident scene. For example, motion analysis algorithms are used to detect abnormal behavior or suspicious movements. The analysis unit can also read past crime records and infer the suspect's next actions and the next actions the police should take, derived from similar cases. This involves using natural language processing technology to analyze past crime records and extract patterns. The analysis unit can, for example, use image analysis technology to identify the suspect's physical characteristics. This allows the analysis unit to quickly and accurately analyze the collected video footage and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas or time periods based on past crime data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0066] The alert unit issues alerts based on the results analyzed by the analysis unit. For example, the alert unit can issue alerts to security guards or police officers based on the analysis results. Specifically, it receives information from the analysis unit and sends notifications to security guards or police officers in real time. The alert unit can, for example, customize the content of alerts it issues. For example, it can generate alerts that include information such as the suspect's physical characteristics, details of their actions, and the location where the incident occurred. The alert unit can, for example, adjust the timing of alert issuance. For example, it can be set to issue alerts only when specific conditions are met. This allows the alert unit to provide necessary information quickly and accurately, helping security guards and police officers take appropriate action. Furthermore, the alert unit manages the alert issuance history and can refer to past alert information. This allows the alert unit to perform analysis and make improvements based on past cases. In addition, the alert unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the alert unit to provide users with quick and reliable instructions and minimize the risk of disaster.

[0067] The reporting unit notifies the police based on alerts issued by the alert unit. For example, the reporting unit can generate natural language data for the police, including estimated results of the suspect's next actions and the actions the police should take, and automatically send this data attached to the video. Specifically, based on information from the analysis unit, it estimates the suspect's next actions and describes the actions the police should take in natural language. This uses natural language generation technology to provide information in a format that the police can easily understand. For example, the reporting unit can update the report content in real time and generate the most appropriate report content according to changes in the situation. For example, if the suspect's location or actions change, it immediately updates the report content and provides the police with the latest information. In addition to the report content, the reporting unit can attach maps and detailed information about the scene to enable the police to respond quickly. This allows the police to accurately grasp the situation at the scene and take a swift and appropriate response. Furthermore, the reporting unit can manage the report history and refer to past report content. This allows the reporting unit to perform analysis and make improvements based on past cases. In addition, the reporting unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the reporting department to provide information to the police quickly and reliably, supporting the early detection and rapid response to crimes.

[0068] The analysis unit can identify the suspect's physical characteristics and actions at a crime scene that are likely to be the site of an incident or accident from an image. For example, the analysis unit can use image analysis technology to identify the suspect's physical characteristics. For example, the analysis unit can identify physical characteristics such as height, weight, and clothing. For example, the analysis unit can use motion analysis technology to identify actions at a crime scene that are likely to be the site of an incident or accident. For example, the analysis unit can identify suspicious movements or fleeing behavior. For example, the analysis unit can combine image analysis technology and motion analysis technology to comprehensively identify the suspect's physical characteristics and actions. This makes it possible to detect crimes early by identifying the suspect's physical characteristics and actions at a crime scene that are likely to be the site of an incident or accident from an image.

[0069] The analysis unit can read past crime records and infer the suspect's next actions and the next actions the police should take, derived from similar cases. For example, the analysis unit reads past crime records from a database and analyzes the suspect's behavior patterns. For example, the analysis unit can infer the suspect's next actions based on past crime records. For example, the analysis unit can infer the next actions the police should take based on past crime records. For example, the analysis unit can learn the suspect's behavior patterns from past crime records using machine learning algorithms and infer the next actions. For example, the analysis unit can learn the actions the police should take from past crime records using machine learning algorithms and infer the next actions. This enables a quick and appropriate response by inferring the suspect's next actions and the next actions the police should take based on past crime records.

[0070] The reporting unit can generate predictions of the suspect's next actions and the next actions the police should take in natural language, attach them to the video, and automatically send them to the police. For example, the reporting unit can use a generative AI to generate predictions of the suspect's next actions in natural language. For example, the reporting unit's generative AI can generate a prediction that "the suspect is likely to choose an escape route next." For example, the reporting unit can use a generative AI to generate a prediction that "the police should block the escape route next." For example, the reporting unit can use a generative AI to automatically attach the prediction results to the video and send them automatically. For example, the reporting unit can attach the prediction results generated by the generative AI to the video and automatically send them to the police. This supports the police's swift and appropriate response by generating predictions of the suspect's next actions and the next actions the police should take in natural language, attaching them to the video, and automatically sending them to the police.

[0071] The collection unit can collect surveillance camera footage in real time. For example, the collection unit can collect surveillance camera footage in real time and transmit it to the analysis unit. For example, the collection unit can collect surveillance camera footage in real time and store it in a database. For example, the collection unit can collect surveillance camera footage in real time and notify security guards. This allows for the early detection of crimes by collecting surveillance camera footage in real time.

[0072] The alert unit can issue alerts based on analysis results. For example, the alert unit can issue alerts to security guards or police officers based on analysis results. For example, the alert unit can issue an alert to security guards based on analysis results, allowing them to rush to the scene. For example, the alert unit can issue an alert to police officers based on analysis results, allowing them to rush to the scene. For example, the alert unit can customize the content of alerts based on analysis results before issuing them. For example, the alert unit can provide detailed information about the alert based on analysis results and notify security guards or police officers accordingly. For example, the alert unit can adjust the timing of alert issuance based on analysis results. For example, the alert unit can optimize the timing of alert issuance based on analysis results, enabling a rapid response. This makes it possible to detect crimes early by issuing alerts based on analysis results.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of surveillance camera video collection based on the estimated emotions. For example, if the user is tense, the data collection unit can increase the frequency of surveillance camera video collection. For example, if the user is relaxed, the data collection unit can return the frequency of video collection to normal. For example, if the user is excited, the data collection unit can focus on collecting video from a specific area. This allows for more appropriate video collection by adjusting the timing of surveillance camera video collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The data collection unit can dynamically change the placement and angle of surveillance cameras to collect optimal footage. For example, the data collection unit can automatically change the placement of surveillance cameras to reduce blind spots. For example, the data collection unit can dynamically adjust the angle of surveillance cameras to constantly monitor important areas. For example, the data collection unit can periodically change the placement of surveillance cameras to make it difficult to predict criminal activity. This allows for optimal footage collection by dynamically changing the placement and angle of surveillance cameras.

[0075] The acquisition unit can evaluate the quality of the collected video in real time and adjust the acquisition method as needed. For example, the acquisition unit can evaluate the resolution of the video in real time and adjust the camera settings as needed. For example, the acquisition unit can evaluate the brightness and contrast of the video in real time and acquire the optimal video. For example, the acquisition unit can evaluate the noise level of the video in real time and perform noise reduction as needed. This enables optimal video acquisition by evaluating the quality of the collected video in real time and adjusting the acquisition method as needed.

[0076] The collection unit can estimate the user's emotions and determine the priority of the video to collect based on the estimated emotions. For example, if the user is tense, the collection unit will prioritize collecting video of important areas. For example, if the user is relaxed, the collection unit can perform normal video collection. For example, if the user is excited, the collection unit can prioritize collecting video of specific areas. This allows for more appropriate video collection by determining the priority of the video to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data collection unit can collect not only video from surveillance cameras but also other sensor data such as audio and temperature. For example, the data collection unit can collect ambient audio data in addition to video from surveillance cameras. For example, the data collection unit can collect ambient temperature data in addition to video from surveillance cameras. For example, the data collection unit can collect ambient vibration data in addition to video from surveillance cameras. This allows for more multifaceted monitoring by collecting not only video from surveillance cameras but also other sensor data such as audio and temperature.

[0078] The data collection unit can integrate footage from multiple surveillance cameras to perform wide-area surveillance. For example, the data collection unit can integrate footage from multiple surveillance cameras to perform wide-area surveillance. For example, the data collection unit can integrate footage from multiple surveillance cameras in real time to monitor important areas. For example, the data collection unit can integrate footage from multiple surveillance cameras to reduce blind spots. This enables wide-area surveillance by integrating footage from multiple surveillance cameras.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The analysis unit can analyze not only video but also other sensor data such as audio and temperature. For example, the analysis unit can analyze audio data in addition to video data to detect abnormal sounds. For example, the analysis unit can analyze temperature data in addition to video data to detect abnormal temperature changes. For example, the analysis unit can analyze vibration data in addition to video data to detect abnormal vibrations. This allows for a more multifaceted analysis by analyzing not only video but also other sensor data such as audio and temperature.

[0081] The analysis unit can update analysis results in real time and adjust the analysis method according to changes in the situation. For example, the analysis unit can update analysis results in real time and adjust the analysis method according to changes in the situation. For example, the analysis unit can update analysis results in real time and adjust the analysis method based on new information. For example, the analysis unit can update analysis results in real time and adjust the analysis method when an anomaly is detected. This allows for more appropriate analysis by updating analysis results in real time and adjusting the analysis method according to changes in the situation.

[0082] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can display normal analysis results. For example, if the user is excited, the analysis unit can prioritize displaying specific analysis results. This allows for the provision of more appropriate information by prioritizing analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can perform analysis by referring not only to past crime records but also to current crime trends and local crime data. For example, the analysis unit can perform analysis by referring to current crime trends in addition to past crime records. For example, the analysis unit can perform analysis by referring to local crime data in addition to past crime records. For example, the analysis unit can perform analysis by integrating current crime trends and local crime data in addition to past crime records. This allows for more appropriate analysis by referring not only to past crime records but also to current crime trends and local crime data.

[0084] The analysis unit can share the results of video analysis in conjunction with other systems and propose comprehensive crime prevention measures. For example, the analysis unit can share the results of video analysis in conjunction with other security systems. For example, the analysis unit can share the results of video analysis in conjunction with police systems. For example, the analysis unit can share the results of video analysis in conjunction with other systems and propose comprehensive crime prevention measures. This allows for the proposal of comprehensive crime prevention measures by sharing the results of video analysis in conjunction with other systems.

[0085] The alert unit can estimate the user's emotions and adjust how alerts are delivered based on those emotions. For example, if the user is stressed, the alert unit can deliver a simple, highly visible alert. If the user is relaxed, the alert unit can deliver an alert containing detailed information. If the user is in a hurry, the alert unit can deliver a concise alert. By adjusting how alerts are delivered based on the user's emotions, more appropriate alerts can be delivered. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The alert unit can not only issue alerts but also customize the content of notifications sent to security guards and police. For example, the alert unit can customize the content of notifications sent to security guards in addition to issuing alerts. For example, the alert unit can customize the content of notifications sent to police in addition to issuing alerts. For example, the alert unit can customize the content of notifications sent to security guards and police in addition to issuing alerts. This allows for more appropriate responses by customizing the content of notifications sent to security guards and police, in addition to issuing alerts.

[0087] The alert unit can adjust the timing of alert transmission in real time, enabling it to transmit alerts at the optimal time. For example, the alert unit can adjust the timing of alert transmission in real time, enabling it to transmit alerts based on new information. For example, the alert unit can adjust the timing of alert transmission in real time, enabling it to transmit alerts when an anomaly is detected. This real-time adjustment of alert transmission timing allows for the transmission of alerts at a more appropriate time.

[0088] The alert unit can estimate the user's emotions and prioritize alerts based on those emotions. For example, if the user is stressed, the alert unit will prioritize important alerts. If the user is relaxed, the alert unit can prioritize normal alerts. If the user is excited, the alert unit can prioritize specific alerts. This allows for more appropriate alert delivery by prioritizing alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The alert unit can diversify its methods of sending alerts, enabling alerts to be sent via multiple methods, such as voice and text messages. For example, the alert unit can diversify its methods of sending alerts, enabling alerts to be sent via voice. For example, the alert unit can diversify its methods of sending alerts, enabling alerts to be sent via text messages. By diversifying the methods of sending alerts, alerts can be sent through a wider variety of means.

[0090] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated emotions. For example, if the user is nervous, the notification unit can provide a simple and easy-to-read notification. For example, if the user is relaxed, the notification unit can provide a notification that includes detailed information. For example, if the user is in a hurry, the notification unit can provide a notification that gets straight to the point. This allows for more appropriate notifications by adjusting the notification content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The notification unit can update notification content in real time and generate the most appropriate notification content in response to changes in the situation. For example, the notification unit can update notification content in real time and generate the most appropriate notification content in response to changes in the situation. For example, the notification unit can update notification content in real time and generate the most appropriate notification content based on new information. For example, the notification unit can update notification content in real time and generate the most appropriate notification content when an anomaly is detected. This enables more appropriate notifications by updating notification content in real time and generating the most appropriate notification content in response to changes in the situation.

[0092] The reporting department can attach maps and detailed information about the scene in addition to the content of the report, enabling the police to respond quickly. For example, the reporting department can attach maps in addition to the content of the report, enabling the police to respond quickly. For example, the reporting department can attach maps and detailed information about the scene in addition to the content of the report, enabling the police to respond quickly. This allows for more appropriate reporting by attaching maps and detailed information about the scene in addition to the content of the report, enabling the police to respond quickly.

[0093] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize sending important notifications. For example, if the user is relaxed, the notification unit can send normal notifications. For example, if the user is agitated, the notification unit can prioritize sending specific notifications. This allows for more appropriate notifications by prioritizing notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The reporting department can generate reports in multiple languages ​​and respond to international crimes. For example, the reporting department can generate reports in multiple languages ​​and respond to international crimes. For example, the reporting department can generate reports in multiple languages ​​and respond to foreign police officers. For example, the reporting department can generate reports in multiple languages ​​and respond quickly to international crimes. This allows for the generation of reports in multiple languages, enabling responses to international crimes.

[0095] The reporting department can share reported information in conjunction with other security systems and propose comprehensive crime prevention measures. For example, the reporting department can share reported information in conjunction with other security systems. For example, the reporting department can share reported information in conjunction with police systems. For example, the reporting department can share reported information in conjunction with other security systems and propose comprehensive crime prevention measures. This allows for the proposal of comprehensive crime prevention measures by sharing reported information in conjunction with other security systems.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The monitoring system may also include a voice recognition unit. This unit can collect and analyze ambient sounds in addition to the video feed from the surveillance camera. For example, the voice recognition unit can detect abnormal sounds such as the sound of breaking glass or shouting and transmit them to the analysis unit. The voice recognition unit can also detect specific keywords and notify the alert unit. This allows for the early detection of anomalies that cannot be detected by video alone, by analyzing the audio data.

[0098] The analysis unit can further estimate the suspect's emotions using its emotion estimation function. For example, it can estimate emotions such as tension or anxiety from the suspect's facial expressions and movements. Based on the estimated emotions, the analysis unit can more accurately predict the suspect's next actions. For example, if the suspect is tense, it can determine that there is a high possibility of escape and notify the police. This enables analysis that takes the suspect's emotions into account, allowing for a more appropriate response.

[0099] The analysis unit can further analyze local crime data. For example, it can analyze patterns of crimes that frequently occur in a particular area and predict the behavior of suspects. Based on local crime data, the analysis unit can infer the next actions that the police should take. For example, it can predict escape routes in a particular area and notify the police. This allows for more accurate analysis by utilizing local crime data.

[0100] The reporting system can further use emotion estimation to adjust the content of reports based on the officer's emotions. For example, if the officer is tense, the system can provide a simple and easy-to-read report. If the officer is relaxed, the system can provide a report with more detailed information. If the officer is in a hurry, the system can provide a report that gets straight to the point. By adjusting the content of reports based on the officer's emotions, more appropriate reports can be made.

[0101] The collection unit can also use drones to collect footage from surveillance cameras. For example, the collection unit can fly drones to collect footage over a wide area. The collection unit can use drones to collect footage from high places or dangerous locations. The collection unit can use drones to track suspects on the move and collect footage. This makes it possible to conduct wide-area surveillance that is difficult with conventional fixed cameras by utilizing drones.

[0102] The alert unit can further adjust how alerts are issued based on the security guard's emotions using emotion estimation functionality. For example, if the security guard is tense, the alert unit can issue a simple, highly visible alert. If the security guard is relaxed, the alert unit can issue an alert containing detailed information. If the security guard is in a hurry, the alert unit can issue a concise alert. By adjusting how alerts are issued based on the security guard's emotions, more appropriate alerts can be issued.

[0103] The analysis unit can analyze temperature data in addition to video data. For example, it can analyze temperature data and detect abnormal temperature changes. Based on the temperature data, the analysis unit can detect the possibility of a fire and notify the police or fire department. Based on the temperature data, the analysis unit can identify the location where a suspect is hiding. In this way, by utilizing temperature data, anomalies that cannot be detected by video alone can be detected at an early stage.

[0104] The data collection unit can also use emotion estimation to dynamically change the placement of surveillance cameras based on the user's emotions. For example, if the user is stressed, the data collection unit can change the placement of the surveillance cameras to monitor important areas. If the user is relaxed, the data collection unit can return to the normal placement. If the user is excited, the data collection unit can focus the surveillance cameras on specific areas. This allows for more appropriate monitoring by dynamically changing the placement of surveillance cameras based on the user's emotions.

[0105] The analysis unit can further analyze audio data and detect abnormal sounds. For example, it can detect abnormal sounds such as the sound of breaking glass or screaming and notify the police. Based on the audio data, the analysis unit can pinpoint the location of a suspect. Based on the audio data, the analysis unit can infer details of the incident. In this way, by analyzing audio data, it is possible to detect anomalies that cannot be detected by video alone at an early stage.

[0106] The reporting system can further use emotion estimation to prioritize reports based on the user's emotions. For example, if the user is stressed, the reporting system can prioritize important reports. If the user is relaxed, the reporting system can prioritize regular reports. If the user is agitated, the reporting system can prioritize specific reports. This allows for more appropriate reports by prioritizing them based on the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects footage from surveillance cameras. The collection unit can, for example, collect footage from surveillance cameras in real time. It can also collect footage periodically and in high resolution. Step 2: The analysis unit analyzes the video footage collected by the collection unit. For example, the analysis unit can identify "the suspect's physical characteristics" and "actions at a crime / accident scene that are likely to occur" from the images. It can also read past crime records and infer the suspect's next actions and the next actions the police should take, based on similar cases. Step 3: The alert unit issues an alert based on the results analyzed by the analysis unit. For example, the alert unit can issue an alert to security guards or police officers based on the analysis results. It is also possible to customize the content of the alert and adjust the timing of the alert. Step 4: The reporting unit notifies the police based on the alerts issued by the alert unit. For example, the reporting unit can generate predictions of the suspect's next actions and the next actions the police should take in natural language, attach them to the video, and automatically send them to the police. It can also update the report content in real time, generate the most appropriate report content according to changes in the situation, and attach maps and detailed information about the scene.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, alert unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects surveillance camera footage using the camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected footage. The alert unit is implemented in the specific processing unit 46A of the smart device 14 and issues an alert based on the analysis results. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0114] As shown in Figure 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, alert unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects images from a surveillance camera using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected images. The alert unit is implemented, for example, in the control unit 46A of the smart glasses 214, and issues an alert based on the analysis results. The notification unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, alert unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects surveillance camera footage using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected footage. The alert unit is implemented in the specific processing unit 46A of the headset terminal 314 and issues an alert based on the analysis results. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, alert unit, and notification unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects images from a surveillance camera using the camera 42 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected images. The alert unit is implemented in the control unit 46A of the robot 414 and issues an alert based on the analysis results. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 and notifies the police. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The collection unit collects footage from surveillance cameras, An analysis unit analyzes the video collected by the aforementioned collection unit, An alert unit that issues an alert based on the results analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies the police based on an alert issued by the aforementioned alert unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Identifying the "physical characteristics of the suspect" and "actions at a crime / accident scene" from images. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By reviewing past criminal records and inferring the suspect's next move and the next action the police should take based on similar cases, The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting unit, The system automatically generates predictions of the suspect's next actions and the next steps the police should take, in natural language, and attaches them to the video footage before sending them to the police. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect surveillance camera footage in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The alert unit is, An alert will be issued based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of surveillance camera footage collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Dynamically change the placement and angle of surveillance cameras to collect optimal footage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The quality of the collected video is evaluated in real time, and the collection method is adjusted as needed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is In addition to video footage from surveillance cameras, other sensor data such as audio and temperature will also be collected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Integrating footage from multiple surveillance cameras to perform wide-area monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, In addition to analyzing video, it also analyzes other sensor data such as audio and temperature. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The analysis results are updated in real time, and the analysis method is adjusted according to changes in the situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The analysis will refer not only to past crime records but also to current crime trends and local crime data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The results of video analysis are shared in conjunction with other systems, and comprehensive crime prevention measures are proposed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, In addition to issuing alerts, it also allows you to customize the content of notifications sent to security guards and police. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, The timing of alert notifications is adjusted in real time to ensure they are sent at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, Diversify the methods for sending alerts, including sending them via multiple methods such as voice and text messages. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the content of the report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, The system updates the notification content in real time and generates the most appropriate notification content in response to changes in the situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, In addition to the details of the report, attach maps and detailed information about the scene so that the police can respond quickly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, It generates reports in multiple languages ​​and can also handle international crime response. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, We share reported information with other security systems and propose comprehensive crime prevention measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects footage from surveillance cameras, An analysis unit analyzes the video collected by the aforementioned collection unit, An alert unit that issues an alert based on the results analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies the police based on an alert issued by the aforementioned alert unit. A system characterized by the following features.

2. The aforementioned analysis unit, Identifying the suspect's physical characteristics and actions likely to occur at a crime or accident scene from images. The system according to feature 1.

3. The aforementioned analysis unit, By reviewing past criminal records and inferring the suspect's next move and the next action the police should take based on similar cases, The system according to feature 1.

4. The aforementioned reporting unit, The system automatically generates predictions of the suspect's next actions and the next steps the police should take, in natural language, and attaches them to the video footage before sending them to the police. The system according to feature 1.

5. The aforementioned collection unit is Collect surveillance camera footage in real time. The system according to feature 1.

6. The alert unit is, An alert will be issued based on the analysis results. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of surveillance camera footage collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Dynamically change the placement and angle of surveillance cameras to collect optimal footage. The system according to feature 1.

9. The aforementioned collection unit is The quality of the collected video is evaluated in real time, and the collection method is adjusted as needed. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system according to feature 1.

Images