System

Abstract

An object of the system according to the embodiment is to automatically analyze a video of a monitoring camera, detect an abnormality, and issue an alert.SOLUTION: A system according to an embodiment includes a monitoring camera, a generation AI, an analyzing system, and an alert issuing unit. The surveillance camera collects a video of a surveillance site. The generation AI analyzes the video collected by the monitoring camera and generates a sentence based on the analysis result. The analyzing system analyzes the text generated by the generating AI. The alert issuing unit issues an alert based on the abnormality detected by the analysis system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of efficiently analyzing surveillance camera footage and detecting anomalies is done manually, resulting in problems of cost and a lack of manpower.

[0005] The system according to the embodiment aims to automatically analyze images from a surveillance camera, detect abnormalities, and issue an alert. [Means for solving the problem]

[0006] The system according to the embodiment includes a surveillance camera, a generation AI, an analysis system, and an alert issuing unit. The surveillance camera collects video footage from a surveillance site. The generation AI analyzes the video footage collected by the surveillance camera and generates text based on the analysis results. The analysis system analyzes the text generated by the generation AI. The alert issuing unit issues an alert based on an abnormality detected by the analysis system. [Effects of the Invention]

[0007] The system according to the embodiment can automatically analyze images from surveillance cameras, detect abnormalities, and issue alerts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A surveillance system according to an embodiment of the present invention is a system that automatically documents and analyzes images of a surveillance site and issues an alert, thereby enabling the surveillance system to efficiently document and analyze images of a surveillance site and automatically issue an alert.

[0029] A surveillance system according to an embodiment includes a surveillance camera, a generation AI, an analysis system, and an alert issuing unit. The surveillance camera collects video from a surveillance site. For example, the surveillance camera collects high-resolution video in real time. The surveillance camera also has a nighttime imaging function, allowing it to collect clear video even in dark places. The surveillance camera also uses a wide-angle lens to collect wide-area video. The generation AI analyzes the video collected by the surveillance camera. For example, the generation AI analyzes the video using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the video using a multimodal generation AI. The generation AI can also extract and analyze important parts of the video. For example, the text generation AI has learned large amounts of video data and has advanced video analysis capabilities. The multimodal generation AI can handle multiple modalities, including audio and text, in addition to video. The generation AI uses keyframe extraction technology to identify particularly important information in the video and perform analysis based on that information. The generation AI generates text based on the analysis results. For example, the generation AI concisely translates the contents of a video into text. The generation AI can also translate the contents of a video into detailed text. The generation AI can also summarize and translate the contents of a video. For example, the generation AI translates events that occurred in a video into text in chronological order. The generation AI issues an alert based on the content of the video. For example, the generation AI issues an alert if an abnormality is detected. The generation AI can also change the content of the alert depending on the type of abnormality. The generation AI can also identify the location of the abnormality and issue an alert. For example, if a fire breaks out, the generation AI issues an alert such as "A fire has broken out." The analysis system analyzes the text generated by the generation AI. For example, the analysis system analyzes the content of the text to detect an abnormality. The analysis system can also identify the type of abnormality based on the content of the text. The analysis system can also identify the location of the abnormality based on the content of the text. For example, if the text says "A fire has broken out," the analysis system can identify the location of the fire.The alert issuing unit issues an alert based on an abnormality detected by the analysis system. For example, the alert issuing unit issues an audio alert when an abnormality is detected. The alert issuing unit can also issue an email alert when an abnormality is detected. The alert issuing unit can also issue an SMS alert when an abnormality is detected. For example, the alert issuing unit issues an audio alert such as "A fire has occurred" when a fire breaks out. This allows the monitoring system according to the embodiment to efficiently document and analyze the video of the monitored site and automatically issue an alert. For example, the output unit notifies relevant parties of the alert via a web application or a mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email allows for a quick response by sending the results directly to relevant parties.

[0030] A surveillance camera equipped with a voice recognition function can send the voice data along with the video data to a generation AI for analysis when it detects an abnormal sound. For example, if a surveillance camera is equipped with a voice recognition function and detects an abnormal sound such as the sound of breaking glass or a scream, it can send the voice data along with the video data to a generation AI for analysis. This makes it possible to detect abnormalities from both voice and video. Furthermore, when a surveillance camera detects an abnormal sound using a voice recognition function, it can analyze the voice data in real time to identify the type of abnormality. For example, if a scream is detected, it can evaluate the possibility of an emergency. Furthermore, when a surveillance camera equipped with a voice recognition function detects an abnormal sound, it can integrate the voice data with the video data for analysis. This makes it possible to more accurately determine the location and situation of the abnormality. This makes it possible to detect abnormalities from both voice and video.

[0031] When converting video data into text, the generation AI can refer to past similar cases from a database and generate detailed text based on specific cases. When converting video data into text, the generation AI, for example, refers to past similar cases from a database and generates detailed text based on specific cases. For example, it generates text by referencing past cases of suspicious intrusions. When converting video data into text, the generation AI also refers to past similar cases and generates detailed text that includes specific situations and response methods. For example, it generates text based on past cases of fires. When converting video data into text, the generation AI also refers to past similar cases and generates detailed text based on specific cases. This improves the accuracy and specificity of the text. This improves the accuracy and specificity of the text.

[0032] When converting video data into text, the generation AI can describe events in chronological order in detail, including time information. For example, when converting video data into text, the generation AI will describe events in chronological order in detail, including time information from the video data. For example, it will generate a sentence such as, "At 10:30 AM, a suspicious person entered the facility." The generation AI will also describe events in chronological order in detail based on the time information from the video data. For example, it will generate a sentence such as, "At 2:15 PM, a fire broke out." The generation AI will also describe events in chronological order in detail, including time information, when converting video data into text. This allows the flow of events to be conveyed accurately. This allows the flow of events to be conveyed accurately.

[0033] When the generation AI detects an anomaly, it can compare it with past anomaly cases, evaluate the severity of the anomaly, and set the priority of the alert. For example, when the generation AI detects an anomaly, it compares it with past anomaly cases and evaluates the severity of the anomaly. For example, it compares it with past fire cases and evaluates the severity of the current fire, and sets the priority of the alert. Furthermore, when the generation AI detects an anomaly, it refers to past anomaly cases, evaluates the severity of the anomaly, and sets the priority of the alert. For example, it compares it with cases of suspicious intrusion, and evaluates the severity of the current situation. Furthermore, when the generation AI detects an anomaly, it compares it with past anomaly cases, evaluates the severity of the anomaly, and sets the priority of the alert. This enables a quick and appropriate response. This enables a quick and appropriate response.

[0034] When an abnormality is detected, the generating AI can display the location of the abnormality on a map, supporting a rapid response. For example, the generating AI builds a system that displays the location of the abnormality on a map when an abnormality is detected. For example, if a fire occurs in a specific location within a facility, the location will be displayed on a map. Furthermore, when an abnormality is detected, the generating AI can display the location of the abnormality on a map, supporting a rapid response. For example, it can display the location of a suspicious person's intrusion on a map. Furthermore, when an abnormality is detected, the generating AI can display the location of the abnormality on a map, enabling a rapid response. This allows relevant parties to accurately determine the location of the abnormality and take appropriate action. This enables a rapid response.

[0035] When the generating AI detects an abnormality, it can work in cooperation with other security systems to achieve a rapid response. For example, when the generating AI detects an abnormality, it can work in cooperation with an emergency notification system to achieve a rapid response. For example, if a fire breaks out, it can automatically activate the emergency notification system. Furthermore, when the generating AI detects an abnormality, it can work in cooperation with other security systems to achieve a rapid response. For example, if it detects a suspicious intrusion, it can automatically notify security guards. Furthermore, when the generating AI detects an abnormality, it can work in cooperation with other security systems to achieve a rapid response. This allows for early detection of the occurrence of an abnormality and appropriate response to be taken. This enables a rapid response.

[0036] When a generation AI detects an anomaly, it can automatically generate specific response procedures according to the type of anomaly and provide them to the relevant parties. For example, when a generation AI detects an anomaly, it automatically generates specific response procedures according to the type of anomaly and provides them to the relevant parties. For example, in the event of a fire, it provides evacuation procedures. When a generation AI detects an anomaly, it automatically generates specific response procedures according to the type of anomaly and provides them to the relevant parties. For example, in the event of a suspicious intrusion, it provides response procedures for security guards. When a generation AI detects an anomaly, it automatically generates specific response procedures according to the type of anomaly, allowing the relevant parties to respond quickly and appropriately. This allows the occurrence of an anomaly to be resolved early. This allows the relevant parties to respond quickly and appropriately.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The surveillance system can further include an environmental sensor unit. The environmental sensor unit can collect environmental data such as temperature, humidity, and air pressure and detect abnormalities. For example, a temperature sensor can detect abnormal temperature increases and assess the possibility of a fire. A humidity sensor can detect abnormal humidity changes and assess the possibility of a water leak or flooding. A barometric pressure sensor can detect abnormal air pressure changes and assess the possibility of an explosion. This allows the surveillance system to detect abnormalities not only from video data but also from environmental data, enabling more comprehensive surveillance.

[0039] The surveillance system can further include a biometric authentication unit. The biometric authentication unit can identify people entering and leaving the surveillance site using technologies such as facial recognition and fingerprint authentication. For example, facial recognition technology can be used to compare the faces of people captured on surveillance cameras with a database to identify suspicious individuals. Fingerprint authentication technology can also be used to authenticate people using fingerprint scanners installed at entrances and exits to prevent unauthorized entry. Furthermore, iris authentication technology can be used to scan the irises of people captured on surveillance cameras to provide more accurate authentication. This allows the surveillance system to detect abnormalities not only from video data but also from biometric data, thereby strengthening security.

[0040] The surveillance system can further include a drone unit. The drone unit can collect video from the sky above the monitoring site and send it to the generating AI. For example, when wide-area monitoring is required, the drone can fly automatically and collect video from the sky. Also, when an abnormality is detected in a specific area, the drone can quickly fly to that area and collect detailed video. Furthermore, the drone has night-time imaging capabilities, allowing it to collect clear video even in dark places. This allows the surveillance system to detect abnormalities not only from the ground but also from the sky, enabling wider-area monitoring.

[0041] The monitoring system can further include a voice synthesis unit. The voice synthesis unit can convert the text generated by the generation AI into voice and notify relevant parties. For example, if an abnormality is detected, the voice synthesis unit can issue a voice alert such as "A fire has broken out." The voice synthesis unit can also issue different voice alerts depending on the type of abnormality. For example, if a suspicious person has broken in, the voice alert can be issued such as "A suspicious person has broken in." Furthermore, the voice synthesis unit can identify the location of the abnormality and issue a voice alert. This allows the monitoring system to detect abnormalities not only visually but also aurally, supporting rapid response.

[0042] The monitoring system can further include a vibration sensor unit. The vibration sensor unit can detect abnormal vibrations and send them to the generation AI. For example, the vibration sensor can detect abnormal vibrations such as those caused by earthquakes or explosions and send the data to the generation AI. The vibration sensor can also detect vibrations in the event of an abnormality in the building structure, allowing for early detection of the abnormality. Furthermore, the vibration sensor can detect abnormal machine operation and evaluate the possibility of a malfunction. This allows the monitoring system to detect abnormalities not only from video data but also from vibration data, enabling more multifaceted monitoring.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The surveillance camera collects video from the surveillance site. For example, the surveillance camera can collect high-resolution video in real time, has night-time shooting capabilities, and can collect clear video even in dark places. It can also use a wide-angle lens to collect video over a wide area. Step 2: The generative AI analyzes the video collected by the surveillance camera and generates text based on the analysis results. For example, the generative AI may use text generation AI (e.g., LLM) or multimodal generative AI to analyze the video and extract and analyze important parts of the video. The generative AI may generate text that is concise, detailed, or summarized from the video content. Step 3: The analysis system analyzes the text generated by the generative AI. For example, the analysis system can analyze the content of the text to detect anomalies and identify the type and location of the anomaly. Step 4: The alert issuing unit issues an alert based on the anomaly detected by the analysis system. For example, the alert issuing unit can issue a voice alert, an email alert, or an SMS alert when an anomaly is detected.

[0045] (Example 2) A surveillance system according to an embodiment of the present invention is a system that automatically documents and analyzes images of a surveillance site and issues an alert, thereby enabling the surveillance system to efficiently document and analyze images of a surveillance site and automatically issue an alert.

[0046] A surveillance system according to an embodiment includes a surveillance camera, a generation AI, an analysis system, and an alert issuing unit. The surveillance camera collects video from a surveillance site. For example, the surveillance camera collects high-resolution video in real time. The surveillance camera also has a nighttime imaging function, allowing it to collect clear video even in dark places. The surveillance camera also uses a wide-angle lens to collect wide-area video. The generation AI analyzes the video collected by the surveillance camera. For example, the generation AI analyzes the video using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the video using a multimodal generation AI. The generation AI can also extract and analyze important parts of the video. For example, the text generation AI has learned large amounts of video data and has advanced video analysis capabilities. The multimodal generation AI can handle multiple modalities, including audio and text, in addition to video. The generation AI uses keyframe extraction technology to identify particularly important information in the video and perform analysis based on that information. The generation AI generates text based on the analysis results. For example, the generation AI concisely translates the contents of a video into text. The generation AI can also translate the contents of a video into detailed text. The generation AI can also summarize and translate the contents of a video. For example, the generation AI translates events that occurred in a video into text in chronological order. The generation AI issues an alert based on the content of the video. For example, the generation AI issues an alert if an abnormality is detected. The generation AI can also change the content of the alert depending on the type of abnormality. The generation AI can also identify the location of the abnormality and issue an alert. For example, if a fire breaks out, the generation AI issues an alert such as "A fire has broken out." The analysis system analyzes the text generated by the generation AI. For example, the analysis system analyzes the content of the text to detect an abnormality. The analysis system can also identify the type of abnormality based on the content of the text. The analysis system can also identify the location of the abnormality based on the content of the text. For example, if the text says "A fire has broken out," the analysis system can identify the location of the fire.The alert issuing unit issues an alert based on an abnormality detected by the analysis system. For example, the alert issuing unit issues an audio alert when an abnormality is detected. The alert issuing unit can also issue an email alert when an abnormality is detected. The alert issuing unit can also issue an SMS alert when an abnormality is detected. For example, the alert issuing unit issues an audio alert such as "A fire has occurred" when a fire breaks out. This allows the monitoring system according to the embodiment to efficiently document and analyze the video of the monitored site and automatically issue an alert. For example, the output unit notifies relevant parties of the alert via a web application or a mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email allows for a quick response by sending the results directly to relevant parties.

[0047] A surveillance camera equipped with a voice recognition function can send the voice data along with the video data to a generation AI for analysis when it detects an abnormal sound. For example, if a surveillance camera is equipped with a voice recognition function and detects an abnormal sound such as the sound of breaking glass or a scream, it can send the voice data along with the video data to a generation AI for analysis. This makes it possible to detect abnormalities from both voice and video. Furthermore, when a surveillance camera detects an abnormal sound using a voice recognition function, it can analyze the voice data in real time to identify the type of abnormality. For example, if a scream is detected, it can evaluate the possibility of an emergency. Furthermore, when a surveillance camera equipped with a voice recognition function detects an abnormal sound, it can integrate the voice data with the video data for analysis. This makes it possible to more accurately determine the location and situation of the abnormality. This makes it possible to detect abnormalities from both voice and video.

[0048] When converting video data into text, the generation AI can refer to past similar cases from a database and generate detailed text based on specific cases. When converting video data into text, the generation AI, for example, refers to past similar cases from a database and generates detailed text based on specific cases. For example, it generates text by referencing past cases of suspicious intrusions. When converting video data into text, the generation AI also refers to past similar cases and generates detailed text that includes specific situations and response methods. For example, it generates text based on past cases of fires. When converting video data into text, the generation AI also refers to past similar cases and generates detailed text based on specific cases. This improves the accuracy and specificity of the text. This improves the accuracy and specificity of the text.

[0049] When converting video data into text, the generation AI can describe events in chronological order in detail, including time information. For example, when converting video data into text, the generation AI will describe events in chronological order in detail, including time information from the video data. For example, it will generate a sentence such as, "At 10:30 AM, a suspicious person entered the facility." The generation AI will also describe events in chronological order in detail based on the time information from the video data. For example, it will generate a sentence such as, "At 2:15 PM, a fire broke out." The generation AI will also describe events in chronological order in detail, including time information, when converting video data into text. This allows the flow of events to be conveyed accurately. This allows the flow of events to be conveyed accurately.

[0050] When converting video data into text, the generation AI uses an emotion estimation function to generate text that reflects the emotions of the people in the video, making it possible to accurately convey the level of tension in the situation. For example, when converting video data into text, the generation AI uses an emotion estimation function to generate text that reflects the emotions of the people in the video. For example, it generates a text such as, "A suspicious person has entered the facility with a look of fear on their face." The generation AI also uses an emotion estimation function to generate text that reflects the emotions of the people in the video, making it possible to accurately convey the level of tension in the situation. For example, it generates a text such as, "A fire has broken out and evacuees are in a state of panic." The generation AI also uses an emotion estimation function to generate text that reflects the emotions of the people in the video, making it possible to accurately convey the level of tension in the situation.

[0051] When the generation AI detects an anomaly, it can compare it with past anomaly cases, evaluate the severity of the anomaly, and set the priority of the alert. For example, when the generation AI detects an anomaly, it compares it with past anomaly cases and evaluates the severity of the anomaly. For example, it compares it with past fire cases and evaluates the severity of the current fire, and sets the priority of the alert. Furthermore, when the generation AI detects an anomaly, it refers to past anomaly cases, evaluates the severity of the anomaly, and sets the priority of the alert. For example, it compares it with cases of suspicious intrusion, and evaluates the severity of the current situation. Furthermore, when the generation AI detects an anomaly, it compares it with past anomaly cases, evaluates the severity of the anomaly, and sets the priority of the alert. This enables a quick and appropriate response. This enables a quick and appropriate response.

[0052] When an abnormality is detected, the generating AI can display the location of the abnormality on a map, supporting a rapid response. For example, the generating AI builds a system that displays the location of the abnormality on a map when an abnormality is detected. For example, if a fire occurs in a specific location within a facility, the location will be displayed on a map. Furthermore, when an abnormality is detected, the generating AI can display the location of the abnormality on a map, supporting a rapid response. For example, it can display the location of a suspicious person's intrusion on a map. Furthermore, when an abnormality is detected, the generating AI can display the location of the abnormality on a map, enabling a rapid response. This allows relevant parties to accurately determine the location of the abnormality and take appropriate action. This enables a rapid response.

[0053] When the generation AI detects an anomaly, it uses the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties, encouraging them to take an appropriate action. For example, when the generation AI detects an anomaly, it uses the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties. For example, it generates a message that encourages a calm response to an emergency. Furthermore, when the generation AI detects an anomaly, it uses the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties, encouraging them to take an appropriate action. For example, it generates a message that gives a sense of security. Furthermore, when the generation AI detects an anomaly, it uses the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties. This allows the relevant parties to take an appropriate action.

[0054] When the generating AI detects an abnormality, it can work in cooperation with other security systems to achieve a rapid response. For example, when the generating AI detects an abnormality, it can work in cooperation with an emergency notification system to achieve a rapid response. For example, if a fire breaks out, it can automatically activate the emergency notification system. Furthermore, when the generating AI detects an abnormality, it can work in cooperation with other security systems to achieve a rapid response. For example, if it detects a suspicious intrusion, it can automatically notify security guards. Furthermore, when the generating AI detects an abnormality, it can work in cooperation with other security systems to achieve a rapid response. This allows for early detection of the occurrence of an abnormality and appropriate response to be taken. This enables a rapid response.

[0055] When a generation AI detects an anomaly, it can automatically generate specific response procedures according to the type of anomaly and provide them to the relevant parties. For example, when a generation AI detects an anomaly, it automatically generates specific response procedures according to the type of anomaly and provides them to the relevant parties. For example, in the event of a fire, it provides evacuation procedures. When a generation AI detects an anomaly, it automatically generates specific response procedures according to the type of anomaly and provides them to the relevant parties. For example, in the event of a suspicious intrusion, it provides response procedures for security guards. When a generation AI detects an anomaly, it automatically generates specific response procedures according to the type of anomaly, allowing the relevant parties to respond quickly and appropriately. This allows the occurrence of an anomaly to be resolved early. This allows the relevant parties to respond quickly and appropriately.

[0056] When an abnormality is detected, the generation AI can use the emotion estimation function to monitor the emotional state of the relevant parties in real time and provide appropriate support. For example, when an abnormality is detected, the generation AI can use the emotion estimation function to monitor the emotional state of the relevant parties in real time and provide appropriate support. For example, it can send a message urging them to respond calmly to the emergency. Furthermore, when an abnormality is detected, the generation AI can use the emotion estimation function to monitor the emotional state of the relevant parties in real time and provide appropriate support. For example, it can send a message that gives them a sense of security. Furthermore, when an abnormality is detected, the generation AI can use the emotion estimation function to monitor the emotional state of the relevant parties in real time and provide appropriate support. This enables the relevant parties to respond quickly and appropriately. This enables the relevant parties to respond quickly and appropriately.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The surveillance system can further include an environmental sensor unit. The environmental sensor unit can collect environmental data such as temperature, humidity, and air pressure and detect abnormalities. For example, a temperature sensor can detect abnormal temperature increases and assess the possibility of a fire. A humidity sensor can detect abnormal humidity changes and assess the possibility of a water leak or flooding. A barometric pressure sensor can detect abnormal air pressure changes and assess the possibility of an explosion. This allows the surveillance system to detect abnormalities not only from video data but also from environmental data, enabling more comprehensive surveillance.

[0059] The surveillance system can further include a biometric authentication unit. The biometric authentication unit can identify people entering and leaving the surveillance site using technologies such as facial recognition and fingerprint authentication. For example, facial recognition technology can be used to compare the faces of people captured on surveillance cameras with a database to identify suspicious individuals. Fingerprint authentication technology can also be used to authenticate people using fingerprint scanners installed at entrances and exits to prevent unauthorized entry. Furthermore, iris authentication technology can be used to scan the irises of people captured on surveillance cameras to provide more accurate authentication. This allows the surveillance system to detect abnormalities not only from video data but also from biometric data, thereby strengthening security.

[0060] The surveillance system can further include a drone unit. The drone unit can collect video from the sky above the monitoring site and send it to the generating AI. For example, when wide-area monitoring is required, the drone can fly automatically and collect video from the sky. Also, when an abnormality is detected in a specific area, the drone can quickly fly to that area and collect detailed video. Furthermore, the drone has night-time imaging capabilities, allowing it to collect clear video even in dark places. This allows the surveillance system to detect abnormalities not only from the ground but also from the sky, enabling wider-area monitoring.

[0061] The monitoring system can further include a voice synthesis unit. The voice synthesis unit can convert the text generated by the generation AI into voice and notify relevant parties. For example, if an abnormality is detected, the voice synthesis unit can issue a voice alert such as "A fire has broken out." The voice synthesis unit can also issue different voice alerts depending on the type of abnormality. For example, if a suspicious person has broken in, the voice alert can be issued such as "A suspicious person has broken in." Furthermore, the voice synthesis unit can identify the location of the abnormality and issue a voice alert. This allows the monitoring system to detect abnormalities not only visually but also aurally, supporting rapid response.

[0062] The monitoring system can further include a vibration sensor unit. The vibration sensor unit can detect abnormal vibrations and send them to the generation AI. For example, the vibration sensor can detect abnormal vibrations such as those caused by earthquakes or explosions and send the data to the generation AI. The vibration sensor can also detect vibrations in the event of an abnormality in the building structure, allowing for early detection of the abnormality. Furthermore, the vibration sensor can detect abnormal machine operation and evaluate the possibility of a malfunction. This allows the monitoring system to detect abnormalities not only from video data but also from vibration data, enabling more multifaceted monitoring.

[0063] When converting video data into text, the generation AI can use its emotion estimation function to generate text that reflects the emotions of the people in the video. For example, it can generate text such as, "A suspicious person has entered the facility with a look of fear on their face." The generation AI can also use its emotion estimation function to generate text that reflects the emotions of the people in the video, accurately conveying the urgency of the situation. For example, it can generate text such as, "A fire has broken out and evacuees are in a state of panic." Furthermore, by using its emotion estimation function to generate text that reflects emotions when converting video data into text, the relevant parties can more accurately grasp the situation and take appropriate action.

[0064] When an abnormality is detected, the generation AI can use the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties. For example, it can generate a message that encourages a calm response to an emergency. The generation AI can also use the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties when an abnormality is detected, encouraging them to take an appropriate response. For example, it can generate a message that gives a sense of security. Furthermore, when an abnormality is detected, the generation AI can use the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties, allowing them to take an appropriate response.

[0065] When an abnormality is detected, the generative AI can use its emotion estimation function to monitor the emotional state of those involved in real time and provide appropriate support. For example, it can send a message encouraging them to remain calm in the event of an emergency. Furthermore, when an abnormality is detected, the generative AI can use its emotion estimation function to monitor the emotional state of those involved in real time and provide appropriate support. For example, it can send a message that gives a sense of security ..., allowing them to respond quickly and appropriately.

[0066] When converting video data into text, the generation AI can use its emotion estimation function to generate text that reflects the emotions of the people in the video. For example, it can generate text such as, "A suspicious person has entered the facility with a look of fear on their face." The generation AI can also use its emotion estimation function to generate text that reflects the emotions of the people in the video, accurately conveying the urgency of the situation. For example, it can generate text such as, "A fire has broken out and evacuees are in a state of panic." Furthermore, by using its emotion estimation function to generate text that reflects emotions when converting video data into text, the relevant parties can more accurately grasp the situation and take appropriate action.

[0067] When an abnormality is detected, the generation AI can use the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties. For example, it can generate a message that encourages a calm response to an emergency. The generation AI can also use the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties when an abnormality is detected, encouraging them to take an appropriate response. For example, it can generate a message that gives a sense of security. Furthermore, when an abnormality is detected, the generation AI can use the emotion estimation function to generate an alert message that takes into account the emotional state of the relevant parties, allowing them to take an appropriate response.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The surveillance camera collects video from the surveillance site. For example, the surveillance camera can collect high-resolution video in real time, has night-time shooting capabilities, and can collect clear video even in dark places. It can also use a wide-angle lens to collect video over a wide area. Step 2: The generative AI analyzes the video collected by the surveillance camera and generates text based on the analysis results. For example, the generative AI may use text generation AI (e.g., LLM) or multimodal generative AI to analyze the video and extract and analyze important parts of the video. The generative AI may generate text that is concise, detailed, or summarized from the video content. Step 3: The analysis system analyzes the text generated by the generative AI. For example, the analysis system can analyze the content of the text to detect anomalies and identify the type and location of the anomaly. Step 4: The alert issuing unit issues an alert based on the anomaly detected by the analysis system. For example, the alert issuing unit can issue a voice alert, an email alert, or an SMS alert when an anomaly is detected.

[0070] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0071] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0072] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0076] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0077] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0078] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0079] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0080] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0081] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0082] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0083] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0084] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0111] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0120] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0121] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0122] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0123] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0124] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0125] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0126] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0127] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0128] 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.

[0129] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0130] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0131] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0132] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0133] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0134] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0135] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0136] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0137] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Surveillance cameras and Generative AI and an analysis system; an alert issuing unit, The surveillance camera is Collecting footage from surveillance sites The generated AI is Analyzing the video collected by the surveillance camera; Generate sentences based on the analysis results, The analysis system comprises: Analyze the sentences generated by the generation AI, The alert issuing unit issuing an alert based on anomalies detected by the analytical system; A system characterized by:

2. The surveillance camera is Equipped with voice recognition function, When an abnormal sound is detected, the audio data is sent along with the video data to the AI ​​for analysis.

2. The system of claim 1.

3. The generated AI is When converting video data into text, a database of similar cases from the past is referenced, and detailed text is generated based on specific examples.

2. The system of claim 1.

4. The generated AI is When the abnormality is detected, the severity of the abnormality is evaluated by comparing it with past abnormal cases, and the priority of the alert is set.

2. The system of claim 1.

5. The generated AI is When converting video data into text, the system generates text that reflects the emotions of the people in the video, accurately conveying the level of tension in the situation.

2. The system of claim 1.

Images