System

Abstract

An object of the system according to the embodiment is to efficiently recognize a specific person from video data and display the information.SOLUTION: A system includes an acquisition unit, an analysis unit, a face authentication unit, and a display unit. The acquisition unit acquires video data. The analysis unit analyzes the video data acquired by the acquisition unit. The face authentication unit performs face authentication based on the data analyzed by the analysis unit. The display unit displays information on the person identified by the face authentication unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been able to efficiently recognize specific people from video data and display information about them, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently recognize a specific person from video data and display that information. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a face authentication unit, and a display unit. The acquisition unit acquires video data. The analysis unit analyzes the video data acquired by the acquisition unit. The face authentication unit performs face authentication based on the data analyzed by the analysis unit. The display unit displays information about the person identified by the face authentication unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently recognize a specific person from video data and display information about that person. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention uses facial recognition technology to identify and identify individual individuals from footage captured by fixed cameras or drones. This system acquires video data, analyzes it using AI, performs facial recognition, and displays information about the identified individuals. For example, video data is acquired from fixed cameras or drones. The cameras or drones capture wide-area footage in real time. The acquired video data is then analyzed by AI. The AI ​​analyzes the video data and identifies individual individuals using facial recognition technology. For example, the AI ​​can identify specific individuals by extracting facial features from the video data and comparing them with a pre-registered face database. Information about the identified individuals is then picked up within the system and displayed as needed. For example, if a specific individual is a surveillance target, the individual's information is displayed in real time. This allows for rapid and accurate identification of the specific individual. This system is effective for monitoring specific individuals in public places, event venues, and other locations. It can also be used for various purposes, such as criminal investigations and searches for missing persons.

[0029] A face authentication system according to an embodiment includes an acquisition unit, an analysis unit, a face authentication unit, and a display unit. The acquisition unit acquires video data. For example, the acquisition unit can acquire video data from a fixed camera or a drone. Fixed cameras include fixed cameras and pan-tilt-zoom cameras. Drones come in a variety of types, each with different flight altitudes and camera resolutions. The analysis unit analyzes the video data acquired by the acquisition unit. For example, the analysis unit analyzes the video data and extracts facial features. Facial features include the position of the eyes and nose, the facial contours, and the like. The face authentication unit performs face authentication based on the data analyzed by the analysis unit. For example, the face authentication unit compares the extracted facial features with a pre-registered face database. The face database includes the number of registered faces, the frequency of data updates, and the like. The display unit displays information about a person identified by the face authentication unit. For example, if a specific person is a surveillance target, the display unit displays information about that person in real time. This allows for the rapid and accurate identification of specific individuals by acquiring and analyzing video data, performing facial recognition, and displaying information about the identified individuals.

[0030] The acquisition unit can acquire video data from a fixed camera or a drone. The acquisition unit can acquire video data over a wide area using, for example, a fixed camera. Fixed cameras include fixed cameras and pan-tilt-zoom cameras. The acquisition unit can also acquire video data using a drone. There are various types of drones with different flight altitudes and camera resolutions. For example, a drone can capture video in a public place or an event venue. This makes it possible to acquire video data over a wide area by acquiring video data from a fixed camera or a drone.

[0031] The analysis unit can analyze the video data and extract facial features. The analysis unit, for example, analyzes the video data and extracts facial features. Facial features include the positions of the eyes and nose, facial contours, etc. For example, the analysis unit extracts facial features from the video data and compares them with a pre-registered face database. In this way, by analyzing the video data and extracting facial features, the accuracy of facial recognition is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the video data to a generation AI and cause the generation AI to extract facial features.

[0032] The face authentication unit can compare the extracted facial features with a pre-registered face database. The face authentication unit, for example, compares the extracted facial features with a pre-registered face database. The face database includes the number of registered faces and the frequency of data updates. For example, the face authentication unit can quickly and accurately identify a specific person by comparing the facial features with the face database. As a result, by comparing the facial features with the face database, a specific person can be quickly and accurately identified. Some or all of the above-described processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input facial feature data to a generation AI and cause the generation AI to perform comparison with the face database.

[0033] The display unit can display information about a specific person in real time when that person is a target of monitoring. For example, when a specific person is a target of monitoring, the display unit displays information about that person in real time. Targets of monitoring include people who engage in specific behaviors and people who are in specific locations. For example, when a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. As a result, when a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information about the person being monitored to a generation AI and have the generation AI display the information in real time.

[0034] The display unit may include specific response means. For example, if a specific person is a target of monitoring, the display unit displays information about the person in real time. Targets of monitoring include people engaging in specific behaviors and people in specific locations. For example, if a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. As a result, if a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input information about the person being monitored to a generation AI and cause the generation AI to display the information in real time.

[0035] When acquiring video data, the acquisition unit can automatically adjust the camera angle and zoom according to a specific event or situation. For example, when a specific speaker takes the stage at an event venue, the acquisition unit automatically adjusts the camera angle to acquire video centered on the speaker. Furthermore, when abnormal movement is detected in a public place, the acquisition unit can automatically adjust the camera zoom to acquire detailed video. Furthermore, when a drone arrives at a specific area, the acquisition unit can automatically adjust the camera angle to acquire wide-area video. This allows for more detailed video data to be acquired by automatically adjusting the camera angle and zoom according to a specific event or situation. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input specific event or situation data into the generation AI and cause the generation AI to automatically adjust the camera angle and zoom.

[0036] The acquisition unit can adjust the acquisition method based on environmental conditions when acquiring video data. For example, when it is raining, the acquisition unit activates the waterproof function of the camera and adjusts the video acquisition method. The acquisition unit can also acquire video using an infrared camera at night or in dark places. The acquisition unit can also adjust the acquisition method to maintain the stability of the drone when there is strong wind. This allows the acquisition of more appropriate video data by optimizing the acquisition method based on environmental conditions. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input environmental condition data to the generation AI and cause the generation AI to adjust the video data acquisition method.

[0037] The acquisition unit can detect specific sounds or movements when acquiring video data and automatically start recording. For example, the acquisition unit can automatically start recording when a loud sound is detected. The acquisition unit can also automatically start recording when a sudden movement is detected. The acquisition unit can also automatically start recording when a specific keyword is detected in the audio. This makes it possible to record important moments without missing them by automatically starting recording when specific sounds or movements are detected. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI, for example. For example, the acquisition unit can input audio and movement data to a generation AI and cause the generation AI to start recording.

[0038] When acquiring video data, the acquisition unit can prioritize acquiring highly relevant video by taking geographical location information into consideration. For example, when an event occurs in a specific area, the acquisition unit can prioritize acquiring video of that area. Furthermore, when abnormal activity is detected in a public place, the acquisition unit can prioritize acquiring video of that location. Furthermore, when a drone arrives in a specific area, the acquisition unit can prioritize acquiring video of that area. This allows important video data to be acquired efficiently by prioritizing the acquisition of highly relevant video by taking geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to prioritize the acquisition of highly relevant video.

[0039] The acquisition unit can analyze social media activity and acquire related video when acquiring video data. For example, if a specific event is trending on social media, the acquisition unit can prioritize acquiring video of the event. Furthermore, if a specific location is trending on social media, the acquisition unit can prioritize acquiring video of the location. Furthermore, if a specific person is trending on social media, the acquisition unit can prioritize acquiring video of the person. By analyzing social media activity and acquiring related video, important video data can be efficiently acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input social media activity data to a generation AI and cause the generation AI to acquire related video.

[0040] When acquiring video data, the acquisition unit can customize the acquisition method by reflecting past feedback. For example, the acquisition unit customizes the method for acquiring video at a specific time period based on past feedback. The acquisition unit can also customize the method for acquiring video at a specific location based on past feedback. The acquisition unit can also customize the method for acquiring video at a specific event based on past feedback. In this way, by customizing the acquisition method by reflecting past feedback, more appropriate video data can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0041] The analysis unit can apply an algorithm to detect specific movements or behavior patterns when analyzing the video data. For example, the analysis unit can apply an algorithm to detect the walking pattern of a specific person from the video data. The analysis unit can also apply an algorithm to detect the hand movements of a specific person from the video data. The analysis unit can also apply an algorithm to detect the facial expressions of a specific person from the video data. In this way, by applying an algorithm to detect specific movements or behavior patterns, more detailed analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the video data to a generation AI and cause the generation AI to detect specific movements or behavior patterns.

[0042] The analysis unit can adjust the analysis algorithm based on environmental conditions when analyzing video data. For example, the analysis unit applies an infrared video analysis algorithm when analyzing video data at night or in a dark place. The analysis unit can also apply an algorithm that removes the effects of rain when analyzing video data in rainy weather. The analysis unit can also apply an algorithm that removes the effects of wind when analyzing video data in strong winds. By adjusting the analysis algorithm based on environmental conditions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input environmental condition data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0043] When analyzing video data, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit can learn the characteristics of a specific person from past analysis results and improve the accuracy of the analysis. The analysis unit can also learn specific movement patterns from past analysis results and improve the accuracy of the analysis. The analysis unit can also learn analysis methods under specific environmental conditions from past analysis results and improve the accuracy of the analysis. In this way, by improving the accuracy of the analysis by referring to past analysis results, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] When analyzing video data, the analysis unit can detect specific sounds or movements and start the analysis. For example, the analysis unit can start the analysis when a loud sound is detected. The analysis unit can also start the analysis when a sudden movement is detected. The analysis unit can also start the analysis when a specific keyword is detected in the sound. In this way, by detecting specific sounds or movements and starting the analysis, it is possible to analyze without missing important moments. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the sound and movement data to the generation AI and have the generation AI start the analysis.

[0045] When analyzing video data, the analysis unit can improve the accuracy of the analysis by referring to related literature and data. The analysis unit can improve the accuracy of the analysis by referring to, for example, related academic papers. The analysis unit can also improve the accuracy of the analysis by referring to related databases. The analysis unit can also improve the accuracy of the analysis by referring to related past analysis results. In this way, by improving the accuracy of the analysis by referring to related literature and data, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature and data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can customize the analysis algorithm based on specific events or situations when analyzing video data. For example, when analyzing video data at an event venue, the analysis unit can apply an algorithm to detect the movement of a specific speaker. The analysis unit can also apply an algorithm to detect abnormal movement when analyzing video data at a public place. The analysis unit can also apply an algorithm to detect movement in a specific area when analyzing drone footage. This allows for customizing the analysis algorithm based on specific events or situations, making it possible to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input specific event or situation data into the generation AI and have the generation AI customize the analysis algorithm.

[0047] The face authentication unit can improve the accuracy of face authentication by detecting specific facial expressions and movements during face authentication. The face authentication unit can improve the accuracy of authentication by detecting specific facial expressions, such as smiling or anger. The face authentication unit can also improve the accuracy of authentication by detecting specific movements, such as head movement and eye movement. The face authentication unit can also improve the accuracy of authentication by detecting the direction and angle of the face. This allows for more accurate face authentication by detecting specific facial expressions and movements and improving the accuracy of authentication. Some or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input facial expression and movement data into a generation AI and have the generation AI improve the accuracy of authentication.

[0048] The face authentication unit can adjust the authentication algorithm based on environmental conditions during face authentication. For example, the face authentication unit applies an infrared image analysis algorithm when authenticating faces at night or in a dark place. The face authentication unit can also apply an algorithm that removes the effects of rain when authenticating faces in rainy weather. The face authentication unit can also apply an algorithm that removes the effects of wind when authenticating faces in strong winds. This allows for more accurate face authentication by adjusting the authentication algorithm based on environmental conditions. Some or all of the above-mentioned processing in the face authentication unit may be performed using AI, for example, or may be performed without using AI. For example, the face authentication unit can input environmental condition data to a generation AI and cause the generation AI to adjust the authentication algorithm.

[0049] The facial authentication unit can improve the accuracy of facial authentication by referring to past authentication results. For example, the facial authentication unit can learn the characteristics of a specific person from past authentication results and improve the accuracy of authentication. The facial authentication unit can also improve the accuracy of authentication by learning authentication methods under specific environmental conditions from past authentication results. The facial authentication unit can also improve the accuracy of authentication by learning specific behavior patterns from past authentication results. This allows for more accurate facial authentication by improving the accuracy of authentication by referring to past authentication results. Some or all of the above-mentioned processing in the facial authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial authentication unit can input past authentication result data into the generation AI and have the generation AI improve the accuracy of authentication.

[0050] The face authentication unit can start authentication by detecting a specific voice or movement during face authentication. For example, the face authentication unit can start face authentication when a specific voice command is detected. The face authentication unit can also start face authentication when a specific movement (for example, raising a hand) is detected. The face authentication unit can also start face authentication when a specific keyword is detected in voice. In this way, by detecting a specific voice or movement and starting authentication, it is possible to perform authentication without missing important moments. Some or all of the above-mentioned processing in the face authentication unit may be performed using AI, for example, or may be performed without using AI. For example, the face authentication unit can input voice and movement data into a generation AI and have the generation AI start authentication.

[0051] The face authentication unit can improve the accuracy of authentication by referring to related literature and data during face authentication. The face authentication unit can improve the accuracy of authentication by referring to, for example, related academic papers. The face authentication unit can also improve the accuracy of authentication by referring to related databases. The face authentication unit can also improve the accuracy of authentication by referring to related past authentication results. In this way, by improving the accuracy of authentication by referring to related literature and data, more accurate face authentication can be performed. Some or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input related literature and data into the generation AI and cause the generation AI to improve the accuracy of authentication.

[0052] The facial recognition unit can customize the authentication algorithm based on specific events or situations during facial recognition. For example, when performing facial recognition at an event venue, the facial recognition unit applies an algorithm that prioritizes authentication of the faces of specific speakers. Furthermore, when performing facial recognition in a public place, the facial recognition unit can also apply an algorithm that prioritizes authentication of the faces of people who have detected abnormal movement. Furthermore, when performing facial recognition on drone footage, the facial recognition unit can also apply an algorithm that prioritizes authentication of people in a specific area. This allows for more appropriate facial recognition by customizing the authentication algorithm based on specific events or situations. Some or all of the above-described processing in the facial recognition unit may be performed using, or without, AI. For example, the facial recognition unit can input specific event or situation data into the generation AI and have the generation AI customize the authentication algorithm.

[0053] The display unit can customize the display content according to a specific event or situation when displaying the information. For example, when a specific speaker takes the stage at an event venue, the display unit can prioritize displaying information about that speaker. Furthermore, when abnormal activity is detected in a public place, the display unit can prioritize displaying information about that location. Furthermore, when a drone arrives in a specific area, the display unit can prioritize displaying information about that area. This allows for customizing the display content according to a specific event or situation, making it possible to provide more appropriate information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input specific event or situation data into a generation AI and have the generation AI customize the display content.

[0054] The display unit can improve the accuracy of the display by referring to past display results when displaying. For example, the display unit can learn information about a specific person from past display results and improve the accuracy of the display. The display unit can also learn information about a specific location from past display results and improve the accuracy of the display. The display unit can also learn information about a specific event from past display results and improve the accuracy of the display. In this way, by improving the accuracy of the display by referring to past display results, more accurate information can be provided. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past display result data to a generation AI and cause the generation AI to improve the accuracy of the display.

[0055] The display unit can change the display content when displaying information by detecting specific sounds or movements. For example, when a loud sound is detected, the display unit can change the display content and display the information. Furthermore, when a sudden movement is detected, the display unit can change the display content and display the information. Furthermore, when a specific keyword is detected by sound, the display unit can change the display content and display the information. In this way, important information can be provided quickly by detecting specific sounds or movements and changing the display content. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input sound or movement data to a generation AI and have the generation AI change the display content.

[0056] The display unit can prioritize displaying highly relevant information by taking geographical location information into consideration when displaying the information. For example, if an event occurs in a specific area, the display unit can prioritize displaying information about that area. Furthermore, if abnormal activity is detected in a public place, the display unit can prioritize displaying information about that location. Furthermore, when a drone arrives in a specific area, the display unit can prioritize displaying information about that area. This makes it possible to efficiently provide important information by prioritizing displaying highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input geographical location information data to a generation AI and cause the generation AI to prioritize displaying highly relevant information.

[0057] The display unit can analyze social media activity and display related information when displaying the information. For example, if a specific event is trending on social media, the display unit can prioritize displaying information about the event. Furthermore, if a specific location is trending on social media, the display unit can prioritize displaying information about the location. Furthermore, if a specific person is trending on social media, the display unit can prioritize displaying information about the person. In this way, important information can be efficiently provided by analyzing social media activity and displaying related information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input social media activity data to a generation AI and cause the generation AI to display related information.

[0058] The display unit can customize the display method by reflecting past feedback when displaying information. For example, the display unit customizes the display method for a specific time period based on past feedback. The display unit can also customize the display method for a specific location based on past feedback. The display unit can also customize the display method for a specific event based on past feedback. In this way, by customizing the display method by reflecting past feedback, more appropriate information can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past feedback data into a generation AI and cause the generation AI to customize the display method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The acquisition unit can detect specific sounds or movements when acquiring video data and automatically start recording. For example, if a loud sound is detected, it can automatically start recording. The acquisition unit can also automatically start recording if a sudden movement is detected. Furthermore, the acquisition unit can automatically start recording if a specific keyword is detected in the sound. This makes it possible to record important moments without missing them by automatically starting recording when specific sounds or movements are detected. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI, for example. For example, the acquisition unit can input sound and movement data to a generation AI and have the generation AI start recording.

[0061] The analysis unit can apply an algorithm to detect specific movements or behavior patterns when analyzing video data. For example, the analysis unit can apply an algorithm to detect the walking pattern of a specific person from the video data. The analysis unit can also apply an algorithm to detect the hand movements of a specific person from the video data. Furthermore, the analysis unit can also apply an algorithm to detect the facial expressions of a specific person from the video data. By applying an algorithm to detect specific movements or behavior patterns, more detailed analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input video data to a generation AI and cause the generation AI to detect specific movements or behavior patterns.

[0062] The facial authentication unit can improve the accuracy of facial authentication by detecting specific facial expressions and movements. For example, it can improve the accuracy of authentication by detecting specific facial expressions such as smiling or anger. The facial authentication unit can also improve the accuracy of authentication by detecting specific movements such as head movement and eye movement. Furthermore, the facial authentication unit can improve the accuracy of authentication by detecting the direction and angle of the face. This allows for more accurate facial authentication by detecting specific facial expressions and movements. Some or all of the above-described processing in the facial authentication unit may be performed using, or without, AI, for example. For example, the facial authentication unit can input facial expression and movement data into a generation AI and have the generation AI improve the accuracy of authentication.

[0063] The display unit can change the display content when it detects a specific sound or movement. For example, if a loud sound is detected, it changes the display content and displays the information. Furthermore, if a sudden movement is detected, the display unit can also change the display content and display the information. Furthermore, if a specific keyword is detected by sound, the display unit can also change the display content and display the information. Thus, by detecting a specific sound or movement and changing the display content, important information can be provided quickly. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input sound or movement data to a generation AI and have the generation AI change the display content.

[0064] The acquisition unit can adjust the acquisition method based on environmental conditions when acquiring video data. For example, in rainy weather, the waterproof function of the camera is enabled and the video acquisition method is adjusted. The acquisition unit can also acquire video using an infrared camera at night or in dark places. Furthermore, the acquisition unit can adjust the acquisition method to maintain the stability of the drone in strong winds. This allows the acquisition of more appropriate video data by optimizing the acquisition method based on environmental conditions. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input environmental condition data to the generation AI and cause the generation AI to adjust the video data acquisition method.

[0065] When acquiring video data, the acquisition unit can prioritize acquiring highly relevant video by taking geographical location information into consideration. For example, if an event occurs in a specific area, the acquisition unit can prioritize acquiring video of that area. Furthermore, if abnormal activity is detected in a public place, the acquisition unit can also prioritize acquiring video of that location. Furthermore, when a drone arrives in a specific area, the acquisition unit can prioritize acquiring video of that area. Thus, by prioritizing the acquisition of highly relevant video by taking geographical location information into consideration, important video data can be acquired efficiently. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to prioritize the acquisition of highly relevant video.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The acquisition unit acquires video data. For example, the acquisition unit can acquire video data from a fixed camera or a drone. Fixed cameras include fixed cameras and pan-tilt-zoom cameras. There are various types of drones, each with different flight altitudes and camera resolutions. Step 2: The analysis unit analyzes the video data acquired by the acquisition unit. For example, the analysis unit analyzes the video data and extracts facial features. The facial features include the positions of the eyes and nose, the facial contours, etc. Step 3: The face recognition unit performs face recognition based on the data analyzed by the analysis unit. For example, the face recognition unit compares the extracted facial features with a pre-registered face database. The face database includes the number of registered faces and the frequency of data updates. Step 4: The display unit displays information about the person identified by the face recognition unit. For example, if a specific person is a surveillance target, the display unit displays information about that person in real time. This allows the specific person to be identified quickly and accurately by acquiring and analyzing video data, performing face recognition, and displaying information about the identified person.

[0068] (Example 2) A system according to an embodiment of the present invention uses facial recognition technology to identify and identify individual individuals from footage captured by fixed cameras or drones. This system acquires video data, analyzes it using AI, performs facial recognition, and displays information about the identified individuals. For example, video data is acquired from fixed cameras or drones. The cameras or drones capture wide-area footage in real time. The acquired video data is then analyzed by AI. The AI ​​analyzes the video data and identifies individual individuals using facial recognition technology. For example, the AI ​​can identify specific individuals by extracting facial features from the video data and comparing them with a pre-registered face database. Information about the identified individuals is then picked up within the system and displayed as needed. For example, if a specific individual is a surveillance target, the individual's information is displayed in real time. This allows for rapid and accurate identification of the specific individual. This system is effective for monitoring specific individuals in public places, event venues, and other locations. It can also be used for various purposes, such as criminal investigations and searches for missing persons.

[0069] A face authentication system according to an embodiment includes an acquisition unit, an analysis unit, a face authentication unit, and a display unit. The acquisition unit acquires video data. For example, the acquisition unit can acquire video data from a fixed camera or a drone. Fixed cameras include fixed cameras and pan-tilt-zoom cameras. Drones come in a variety of types, each with different flight altitudes and camera resolutions. The analysis unit analyzes the video data acquired by the acquisition unit. For example, the analysis unit analyzes the video data and extracts facial features. Facial features include the position of the eyes and nose, the facial contours, and the like. The face authentication unit performs face authentication based on the data analyzed by the analysis unit. For example, the face authentication unit compares the extracted facial features with a pre-registered face database. The face database includes the number of registered faces, the frequency of data updates, and the like. The display unit displays information about a person identified by the face authentication unit. For example, if a specific person is a surveillance target, the display unit displays information about that person in real time. This allows for the rapid and accurate identification of specific individuals by acquiring and analyzing video data, performing facial recognition, and displaying information about the identified individuals.

[0070] The acquisition unit can acquire video data from a fixed camera or a drone. The acquisition unit can acquire video data over a wide area using, for example, a fixed camera. Fixed cameras include fixed cameras and pan-tilt-zoom cameras. The acquisition unit can also acquire video data using a drone. There are various types of drones with different flight altitudes and camera resolutions. For example, a drone can capture video in a public place or an event venue. This makes it possible to acquire video data over a wide area by acquiring video data from a fixed camera or a drone.

[0071] The analysis unit can analyze the video data and extract facial features. The analysis unit, for example, analyzes the video data and extracts facial features. Facial features include the positions of the eyes and nose, facial contours, etc. For example, the analysis unit extracts facial features from the video data and compares them with a pre-registered face database. In this way, by analyzing the video data and extracting facial features, the accuracy of facial recognition is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the video data to a generation AI and cause the generation AI to extract facial features.

[0072] The face authentication unit can compare the extracted facial features with a pre-registered face database. The face authentication unit, for example, compares the extracted facial features with a pre-registered face database. The face database includes the number of registered faces and the frequency of data updates. For example, the face authentication unit can quickly and accurately identify a specific person by comparing the facial features with the face database. As a result, by comparing the facial features with the face database, a specific person can be quickly and accurately identified. Some or all of the above-described processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input facial feature data to a generation AI and cause the generation AI to perform comparison with the face database.

[0073] The display unit can display information about a specific person in real time when that person is a target of monitoring. For example, when a specific person is a target of monitoring, the display unit displays information about that person in real time. Targets of monitoring include people who engage in specific behaviors and people who are in specific locations. For example, when a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. As a result, when a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information about the person being monitored to a generation AI and have the generation AI display the information in real time.

[0074] The display unit may include specific response means. For example, if a specific person is a target of monitoring, the display unit displays information about the person in real time. Targets of monitoring include people engaging in specific behaviors and people in specific locations. For example, if a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. As a result, if a specific person is a target of monitoring, the display unit displays the information in real time, enabling a prompt response. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input information about the person being monitored to a generation AI and cause the generation AI to display the information in real time.

[0075] The acquisition unit can estimate the user's emotions and adjust the timing of video data acquisition based on the estimated emotions. For example, when the user is nervous, the acquisition unit can increase the frequency of video data acquisition and acquire more detailed images. Furthermore, when the user is relaxed, the acquisition unit can also reduce the frequency of video data acquisition and acquire the minimum amount of video necessary. Furthermore, when the user is in a hurry, the acquisition unit can adjust the timing to prioritize acquisition of important moments. This allows for more appropriate video data to be acquired by adjusting the timing of video data acquisition based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of video data acquisition.

[0076] When acquiring video data, the acquisition unit can automatically adjust the camera angle and zoom according to a specific event or situation. For example, when a specific speaker takes the stage at an event venue, the acquisition unit automatically adjusts the camera angle to acquire video centered on the speaker. Furthermore, when abnormal movement is detected in a public place, the acquisition unit can automatically adjust the camera zoom to acquire detailed video. Furthermore, when a drone arrives at a specific area, the acquisition unit can automatically adjust the camera angle to acquire wide-area video. This allows for more detailed video data to be acquired by automatically adjusting the camera angle and zoom according to a specific event or situation. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input specific event or situation data into the generation AI and cause the generation AI to automatically adjust the camera angle and zoom.

[0077] The acquisition unit can adjust the acquisition method based on environmental conditions when acquiring video data. For example, when it is raining, the acquisition unit activates the waterproof function of the camera and adjusts the video acquisition method. The acquisition unit can also acquire video using an infrared camera at night or in dark places. The acquisition unit can also adjust the acquisition method to maintain the stability of the drone when there is strong wind. This allows the acquisition of more appropriate video data by optimizing the acquisition method based on environmental conditions. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input environmental condition data to the generation AI and cause the generation AI to adjust the video data acquisition method.

[0078] The acquisition unit can detect specific sounds or movements when acquiring video data and automatically start recording. For example, the acquisition unit can automatically start recording when a loud sound is detected. The acquisition unit can also automatically start recording when a sudden movement is detected. The acquisition unit can also automatically start recording when a specific keyword is detected in the audio. This makes it possible to record important moments without missing them by automatically starting recording when specific sounds or movements are detected. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI, for example. For example, the acquisition unit can input audio and movement data to a generation AI and cause the generation AI to start recording.

[0079] The acquisition unit can estimate the user's emotions and determine the priority of video data to be acquired based on the estimated user's emotions. For example, when the user is nervous, the acquisition unit prioritizes acquiring important video data. Furthermore, when the user is relaxed, the acquisition unit can also prioritize acquiring general video data. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring important video data in a short time. Thus, by determining the priority of video data based on the user's emotions, important video data can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the video data.

[0080] When acquiring video data, the acquisition unit can prioritize acquiring highly relevant video by taking geographical location information into consideration. For example, when an event occurs in a specific area, the acquisition unit can prioritize acquiring video of that area. Furthermore, when abnormal activity is detected in a public place, the acquisition unit can prioritize acquiring video of that location. Furthermore, when a drone arrives in a specific area, the acquisition unit can prioritize acquiring video of that area. This allows important video data to be acquired efficiently by prioritizing the acquisition of highly relevant video by taking geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to prioritize the acquisition of highly relevant video.

[0081] The acquisition unit can analyze social media activity and acquire related video when acquiring video data. For example, if a specific event is trending on social media, the acquisition unit can prioritize acquiring video of the event. Furthermore, if a specific location is trending on social media, the acquisition unit can prioritize acquiring video of the location. Furthermore, if a specific person is trending on social media, the acquisition unit can prioritize acquiring video of the person. By analyzing social media activity and acquiring related video, important video data can be efficiently acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input social media activity data to a generation AI and cause the generation AI to acquire related video.

[0082] When acquiring video data, the acquisition unit can customize the acquisition method by reflecting past feedback. For example, the acquisition unit customizes the method for acquiring video at a specific time period based on past feedback. The acquisition unit can also customize the method for acquiring video at a specific location based on past feedback. The acquisition unit can also customize the method for acquiring video at a specific event based on past feedback. In this way, by customizing the acquisition method by reflecting past feedback, more appropriate video data can be acquired. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0084] The analysis unit can apply an algorithm to detect specific movements or behavior patterns when analyzing the video data. For example, the analysis unit can apply an algorithm to detect the walking pattern of a specific person from the video data. The analysis unit can also apply an algorithm to detect the hand movements of a specific person from the video data. The analysis unit can also apply an algorithm to detect the facial expressions of a specific person from the video data. In this way, by applying an algorithm to detect specific movements or behavior patterns, more detailed analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the video data to a generation AI and cause the generation AI to detect specific movements or behavior patterns.

[0085] The analysis unit can adjust the analysis algorithm based on environmental conditions when analyzing video data. For example, the analysis unit applies an infrared video analysis algorithm when analyzing video data at night or in a dark place. The analysis unit can also apply an algorithm that removes the effects of rain when analyzing video data in rainy weather. The analysis unit can also apply an algorithm that removes the effects of wind when analyzing video data in strong winds. By adjusting the analysis algorithm based on environmental conditions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input environmental condition data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0086] When analyzing video data, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit can learn the characteristics of a specific person from past analysis results and improve the accuracy of the analysis. The analysis unit can also learn specific movement patterns from past analysis results and improve the accuracy of the analysis. The analysis unit can also learn analysis methods under specific environmental conditions from past analysis results and improve the accuracy of the analysis. In this way, by improving the accuracy of the analysis by referring to past analysis results, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize providing important analysis results. Furthermore, if the user is relaxed, the analysis unit can prioritize providing general analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize providing important analysis results in a short time. Thus, by determining the analysis priority based on the user's emotions, important analysis results can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0088] When analyzing video data, the analysis unit can detect specific sounds or movements and start the analysis. For example, the analysis unit can start the analysis when a loud sound is detected. The analysis unit can also start the analysis when a sudden movement is detected. The analysis unit can also start the analysis when a specific keyword is detected in the sound. In this way, by detecting specific sounds or movements and starting the analysis, it is possible to analyze without missing important moments. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the sound and movement data to the generation AI and have the generation AI start the analysis.

[0089] When analyzing video data, the analysis unit can improve the accuracy of the analysis by referring to related literature and data. The analysis unit can improve the accuracy of the analysis by referring to, for example, related academic papers. The analysis unit can also improve the accuracy of the analysis by referring to related databases. The analysis unit can also improve the accuracy of the analysis by referring to related past analysis results. In this way, by improving the accuracy of the analysis by referring to related literature and data, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature and data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] The analysis unit can customize the analysis algorithm based on specific events or situations when analyzing video data. For example, when analyzing video data at an event venue, the analysis unit can apply an algorithm to detect the movement of a specific speaker. The analysis unit can also apply an algorithm to detect abnormal movement when analyzing video data at a public place. The analysis unit can also apply an algorithm to detect movement in a specific area when analyzing drone footage. This allows for customizing the analysis algorithm based on specific events or situations, making it possible to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input specific event or situation data into the generation AI and have the generation AI customize the analysis algorithm.

[0091] The facial recognition unit can estimate the user's emotions and adjust the accuracy of facial recognition based on the estimated user emotions. For example, if the user is nervous, the facial recognition unit extracts detailed features to improve the accuracy of facial recognition. Furthermore, if the user is relaxed, the facial recognition unit can extract general features to perform facial recognition. Furthermore, if the user is in a hurry, the facial recognition unit can extract key features to perform quick facial recognition. This allows for more accurate facial recognition by adjusting the accuracy of facial recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facial recognition unit may be performed using AI, or without AI. For example, the facial recognition unit can input the user's emotion data into the generation AI and have the generation AI adjust the accuracy of facial recognition.

[0092] The face authentication unit can improve the accuracy of face authentication by detecting specific facial expressions and movements during face authentication. The face authentication unit can improve the accuracy of authentication by detecting specific facial expressions, such as smiling or anger. The face authentication unit can also improve the accuracy of authentication by detecting specific movements, such as head movement and eye movement. The face authentication unit can also improve the accuracy of authentication by detecting the direction and angle of the face. This allows for more accurate face authentication by detecting specific facial expressions and movements and improving the accuracy of authentication. Some or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input facial expression and movement data into a generation AI and have the generation AI improve the accuracy of authentication.

[0093] The face authentication unit can adjust the authentication algorithm based on environmental conditions during face authentication. For example, the face authentication unit applies an infrared image analysis algorithm when authenticating faces at night or in a dark place. The face authentication unit can also apply an algorithm that removes the effects of rain when authenticating faces in rainy weather. The face authentication unit can also apply an algorithm that removes the effects of wind when authenticating faces in strong winds. This allows for more accurate face authentication by adjusting the authentication algorithm based on environmental conditions. Some or all of the above-mentioned processing in the face authentication unit may be performed using AI, for example, or may be performed without using AI. For example, the face authentication unit can input environmental condition data to a generation AI and cause the generation AI to adjust the authentication algorithm.

[0094] The facial authentication unit can improve the accuracy of facial authentication by referring to past authentication results. For example, the facial authentication unit can learn the characteristics of a specific person from past authentication results and improve the accuracy of authentication. The facial authentication unit can also improve the accuracy of authentication by learning authentication methods under specific environmental conditions from past authentication results. The facial authentication unit can also improve the accuracy of authentication by learning specific behavior patterns from past authentication results. This allows for more accurate facial authentication by improving the accuracy of authentication by referring to past authentication results. Some or all of the above-mentioned processing in the facial authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial authentication unit can input past authentication result data into the generation AI and have the generation AI improve the accuracy of authentication.

[0095] The facial authentication unit can estimate the user's emotions and determine the authentication priority based on the estimated user's emotions. For example, if the user is nervous, the facial authentication unit can prioritize important authentication. Furthermore, if the user is relaxed, the facial authentication unit can also prioritize general authentication. Furthermore, if the user is in a hurry, the facial authentication unit can prioritize important authentication in a short time. Thus, by determining the authentication priority based on the user's emotions, important authentication can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the facial authentication unit may be performed using AI, or may be performed without AI. For example, the facial authentication unit can input the user's emotion data into the generation AI and have the generation AI determine the authentication priority.

[0096] The face authentication unit can start authentication by detecting a specific voice or movement during face authentication. For example, the face authentication unit can start face authentication when a specific voice command is detected. The face authentication unit can also start face authentication when a specific movement (for example, raising a hand) is detected. The face authentication unit can also start face authentication when a specific keyword is detected in voice. In this way, by detecting a specific voice or movement and starting authentication, it is possible to perform authentication without missing important moments. Some or all of the above-mentioned processing in the face authentication unit may be performed using AI, for example, or may be performed without using AI. For example, the face authentication unit can input voice and movement data into a generation AI and have the generation AI start authentication.

[0097] The face authentication unit can improve the accuracy of authentication by referring to related literature and data during face authentication. The face authentication unit can improve the accuracy of authentication by referring to, for example, related academic papers. The face authentication unit can also improve the accuracy of authentication by referring to related databases. The face authentication unit can also improve the accuracy of authentication by referring to related past authentication results. In this way, by improving the accuracy of authentication by referring to related literature and data, more accurate face authentication can be performed. Some or all of the above-mentioned processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can input related literature and data into the generation AI and cause the generation AI to improve the accuracy of authentication.

[0098] The facial recognition unit can customize the authentication algorithm based on specific events or situations during facial recognition. For example, when performing facial recognition at an event venue, the facial recognition unit applies an algorithm that prioritizes authentication of the faces of specific speakers. Furthermore, when performing facial recognition in a public place, the facial recognition unit can also apply an algorithm that prioritizes authentication of the faces of people who have detected abnormal movement. Furthermore, when performing facial recognition on drone footage, the facial recognition unit can also apply an algorithm that prioritizes authentication of people in a specific area. This allows for more appropriate facial recognition by customizing the authentication algorithm based on specific events or situations. Some or all of the above-described processing in the facial recognition unit may be performed using, or without, AI. For example, the facial recognition unit can input specific event or situation data into the generation AI and have the generation AI customize the authentication algorithm.

[0099] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, when the user is nervous, the display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that focuses on the main points. By adjusting the display method based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0100] The display unit can customize the display content according to a specific event or situation when displaying the information. For example, when a specific speaker takes the stage at an event venue, the display unit can prioritize displaying information about that speaker. Furthermore, when abnormal activity is detected in a public place, the display unit can prioritize displaying information about that location. Furthermore, when a drone arrives in a specific area, the display unit can prioritize displaying information about that area. This allows for customizing the display content according to a specific event or situation, making it possible to provide more appropriate information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input specific event or situation data into a generation AI and have the generation AI customize the display content.

[0101] The display unit can improve the accuracy of the display by referring to past display results when displaying. For example, the display unit can learn information about a specific person from past display results and improve the accuracy of the display. The display unit can also learn information about a specific location from past display results and improve the accuracy of the display. The display unit can also learn information about a specific event from past display results and improve the accuracy of the display. In this way, by improving the accuracy of the display by referring to past display results, more accurate information can be provided. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past display result data to a generation AI and cause the generation AI to improve the accuracy of the display.

[0102] The display unit can change the display content when displaying information by detecting specific sounds or movements. For example, when a loud sound is detected, the display unit can change the display content and display the information. Furthermore, when a sudden movement is detected, the display unit can change the display content and display the information. Furthermore, when a specific keyword is detected by sound, the display unit can change the display content and display the information. In this way, important information can be provided quickly by detecting specific sounds or movements and changing the display content. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input sound or movement data to a generation AI and have the generation AI change the display content.

[0103] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. For example, when the user is nervous, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying general information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying important information in a short time. By determining display priorities based on the user's emotions, important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI determine the display priorities.

[0104] The display unit can prioritize displaying highly relevant information by taking geographical location information into consideration when displaying the information. For example, if an event occurs in a specific area, the display unit can prioritize displaying information about that area. Furthermore, if abnormal activity is detected in a public place, the display unit can prioritize displaying information about that location. Furthermore, when a drone arrives in a specific area, the display unit can prioritize displaying information about that area. This makes it possible to efficiently provide important information by prioritizing displaying highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input geographical location information data to a generation AI and cause the generation AI to prioritize displaying highly relevant information.

[0105] The display unit can analyze social media activity and display related information when displaying the information. For example, if a specific event is trending on social media, the display unit can prioritize displaying information about the event. Furthermore, if a specific location is trending on social media, the display unit can prioritize displaying information about the location. Furthermore, if a specific person is trending on social media, the display unit can prioritize displaying information about the person. In this way, important information can be efficiently provided by analyzing social media activity and displaying related information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input social media activity data to a generation AI and cause the generation AI to display related information.

[0106] The display unit can customize the display method by reflecting past feedback when displaying information. For example, the display unit customizes the display method for a specific time period based on past feedback. The display unit can also customize the display method for a specific location based on past feedback. The display unit can also customize the display method for a specific event based on past feedback. In this way, by customizing the display method by reflecting past feedback, more appropriate information can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past feedback data into a generation AI and cause the generation AI to customize the display method. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, face authentication unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires video data using the camera 42 of the smart device 14 or a drone camera. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the video data to extract facial features. The face authentication unit is realized by the identification processing unit 290 of the data processing device 12 and compares the extracted facial features with a face database. The display unit displays information about the identified person in real time on the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, analysis unit, face authentication unit, and display unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires video data using the camera 42 of the smart glasses 214 or a drone camera. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the video data to extract facial features. The face authentication unit is realized by the identification processing unit 290 of the data processing device 12 and compares the extracted facial features with a face database. The display unit displays information about the identified person on the display of the smart glasses 214 in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, face authentication unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires video data using the camera 42 of the headset type terminal 314 or a drone camera. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the video data to extract facial features. The face authentication unit is realized by the identification processing unit 290 of the data processing device 12 and compares the extracted facial features with a face database. The display unit displays information about the identified person in real time on the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, face authentication unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires video data using the camera 42 of the robot 414 or a camera of a drone. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the video data to extract facial features. The face authentication unit is realized by the identification processing unit 290 of the data processing device 12 and compares the extracted facial features with a face database. The display unit displays information about the identified person on the display of the robot 414 in real time.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The acquisition unit can detect specific sounds or movements when acquiring video data and automatically start recording. For example, if a loud sound is detected, it can automatically start recording. The acquisition unit can also automatically start recording if a sudden movement is detected. Furthermore, the acquisition unit can automatically start recording if a specific keyword is detected in the sound. This makes it possible to record important moments without missing them by automatically starting recording when specific sounds or movements are detected. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI, for example. For example, the acquisition unit can input sound and movement data to a generation AI and have the generation AI start recording.

[0109] The analysis unit can apply an algorithm to detect specific movements or behavior patterns when analyzing video data. For example, the analysis unit can apply an algorithm to detect the walking pattern of a specific person from the video data. The analysis unit can also apply an algorithm to detect the hand movements of a specific person from the video data. Furthermore, the analysis unit can also apply an algorithm to detect the facial expressions of a specific person from the video data. By applying an algorithm to detect specific movements or behavior patterns, more detailed analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input video data to a generation AI and cause the generation AI to detect specific movements or behavior patterns.

[0110] The facial authentication unit can improve the accuracy of facial authentication by detecting specific facial expressions and movements. For example, it can improve the accuracy of authentication by detecting specific facial expressions such as smiling or anger. The facial authentication unit can also improve the accuracy of authentication by detecting specific movements such as head movement and eye movement. Furthermore, the facial authentication unit can improve the accuracy of authentication by detecting the direction and angle of the face. This allows for more accurate facial authentication by detecting specific facial expressions and movements. Some or all of the above-described processing in the facial authentication unit may be performed using, or without, AI, for example. For example, the facial authentication unit can input facial expression and movement data into a generation AI and have the generation AI improve the accuracy of authentication.

[0111] The display unit can change the display content when it detects a specific sound or movement. For example, if a loud sound is detected, it changes the display content and displays the information. Furthermore, if a sudden movement is detected, the display unit can also change the display content and display the information. Furthermore, if a specific keyword is detected by sound, the display unit can also change the display content and display the information. Thus, by detecting a specific sound or movement and changing the display content, important information can be provided quickly. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input sound or movement data to a generation AI and have the generation AI change the display content.

[0112] The acquisition unit can adjust the acquisition method based on environmental conditions when acquiring video data. For example, in rainy weather, the waterproof function of the camera is enabled and the video acquisition method is adjusted. The acquisition unit can also acquire video using an infrared camera at night or in dark places. Furthermore, the acquisition unit can adjust the acquisition method to maintain the stability of the drone in strong winds. This allows the acquisition of more appropriate video data by optimizing the acquisition method based on environmental conditions. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input environmental condition data to the generation AI and cause the generation AI to adjust the video data acquisition method.

[0113] The acquisition unit can estimate the user's emotions and adjust the timing of video data acquisition based on the estimated emotions. For example, if the user is nervous, the acquisition unit can increase the frequency of video data acquisition and acquire more detailed images. Furthermore, if the user is relaxed, the acquisition unit can also reduce the frequency of video data acquisition and acquire the minimum amount of video necessary. Furthermore, if the user is in a hurry, the acquisition unit can adjust the timing to prioritize acquisition of important moments. This allows for more appropriate video data to be acquired by adjusting the timing of video data acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of video data acquisition.

[0114] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0115] The facial recognition unit can estimate the user's emotions and adjust the accuracy of facial recognition based on the estimated user emotions. For example, if the user is nervous, detailed features are extracted to improve the accuracy of facial recognition. Furthermore, if the user is relaxed, the facial recognition unit can extract general features to perform facial recognition. Furthermore, if the user is in a hurry, the facial recognition unit can extract key features to perform quick facial recognition. This allows for more accurate facial recognition by adjusting the accuracy of facial recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facial recognition unit can be performed using AI, or without AI. For example, the facial recognition unit can input the user's emotion data into the generation AI and have the generation AI adjust the accuracy of facial recognition.

[0116] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that focuses on the main points. By adjusting the display method based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0117] When acquiring video data, the acquisition unit can prioritize acquiring highly relevant video by taking geographical location information into consideration. For example, if an event occurs in a specific area, the acquisition unit can prioritize acquiring video of that area. Furthermore, if abnormal activity is detected in a public place, the acquisition unit can also prioritize acquiring video of that location. Furthermore, when a drone arrives in a specific area, the acquisition unit can prioritize acquiring video of that area. Thus, by prioritizing the acquisition of highly relevant video by taking geographical location information into consideration, important video data can be acquired efficiently. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to prioritize the acquisition of highly relevant video.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The acquisition unit acquires video data. For example, the acquisition unit can acquire video data from a fixed camera or a drone. Fixed cameras include fixed cameras and pan-tilt-zoom cameras. There are various types of drones, each with different flight altitudes and camera resolutions. Step 2: The analysis unit analyzes the video data acquired by the acquisition unit. For example, the analysis unit analyzes the video data and extracts facial features. The facial features include the positions of the eyes and nose, the facial contours, etc. Step 3: The face recognition unit performs face recognition based on the data analyzed by the analysis unit. For example, the face recognition unit compares the extracted facial features with a pre-registered face database. The face database includes the number of registered faces and the frequency of data updates. Step 4: The display unit displays information about the person identified by the face recognition unit. For example, if a specific person is a surveillance target, the display unit displays information about that person in real time. This allows the specific person to be identified quickly and accurately by acquiring and analyzing video data, performing face recognition, and displaying information about the identified person.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0154] 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.

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

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. an acquisition unit that acquires video data; an analysis unit that analyzes the video data acquired by the acquisition unit; a face authentication unit that performs face authentication based on the data analyzed by the analysis unit; a display unit that displays information about the person identified by the face authentication unit; Equipped with A system characterized by:

2. The acquisition unit Acquire video data from fixed cameras or drones 2. The system of claim 1.

3. The analysis unit Analyzing video data and extracting facial features 2. The system of claim 1.

4. The face authentication unit Matching the extracted facial features with a pre-registered face database 2. The system of claim 1.

5. The display unit If a specific person is under surveillance, the information of that person will be displayed in real time.

2. The system of claim 1.

6. The display unit Includes specific measures 2. The system of claim 1.

7. The acquisition unit Estimates the user's emotions and adjusts the timing of video data acquisition based on the estimated emotions.

2. The system of claim 1.

8. The acquisition unit When capturing video data, automatically adjust the camera angle and zoom to accommodate specific events or situations.

2. The system of claim 1.

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