System

Abstract

An object of a system according to an embodiment is to automatically add a caption to an aerial image.SOLUTION: A system according to an embodiment includes an aerial photography image acquirer, an analyzer, a subtitle generator, and a subtitle adder. The aerially-photographed image acquisition part acquires aerially-photographed images. The analysis part analyzes the aerial image acquired by the aerial image acquisition part. The caption generation unit generates a caption on the basis of a result analyzed by the analysis unit. The caption adder adds the caption generated by the caption generator to the aerial photography video.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, adding subtitles to aerial footage was often done manually, which was inefficient.

[0005] The system according to the embodiment aims to automatically add subtitles to aerial footage. [Means for solving the problem]

[0006] The system according to the embodiment includes an aerial video acquisition unit, an analysis unit, a subtitle generation unit, and a subtitle addition unit. The aerial video acquisition unit acquires aerial video. The analysis unit analyzes the aerial video acquired by the aerial video acquisition unit. The subtitle generation unit generates subtitles based on the results of the analysis by the analysis unit. The subtitle addition unit adds the subtitles generated by the subtitle generation unit to the aerial video. [Effects of the Invention]

[0007] The system according to the embodiment can automatically add subtitles to aerial footage. [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) The automatic captioning system according to an embodiment of the present invention automatically analyzes aerial footage shot by a drone, and uses a generation AI to generate appropriate captions and add them to the footage. This allows the automatic captioning system to make the content of the aerial footage easier for viewers to understand.

[0029] An automatic captioning system according to an embodiment includes an aerial video acquisition unit, an analysis unit, a subtitle generation unit, and a subtitle addition unit. The aerial video acquisition unit acquires aerial video captured by a drone. For example, high-resolution video is acquired using the drone's camera. The aerial video acquisition unit can also acquire video in real time. For example, it can acquire video from a drone in real time using live streaming technology. The aerial video acquisition unit can also acquire previously captured video from a database. For example, it can acquire video stored in cloud storage. The analysis unit analyzes the aerial video acquired by the aerial video acquisition unit. For example, the generation AI recognizes objects such as buildings, roads, and people in the video. The generation AI can also grasp the position and status of objects in the video. For example, it uses video recognition technology to analyze the collapsed state of buildings and the locations of evacuees. The analysis unit can also track movements and changes in the video in real time. For example, it uses a motion detection algorithm to track the movement paths of evacuees. The subtitle generation unit generates subtitles based on the results of the analysis by the analysis unit. For example, the generation AI generates subtitles such as "There is a collapsed building here" for video of a disaster site. The generation AI can also generate subtitles such as "Evacuees are gathering in this area" based on the locations of evacuees. Furthermore, the subtitle generation unit can generate dynamic subtitles based on the movement of objects in the video. For example, it generates subtitles such as "Evacuees are heading this way" to match the movement of evacuees. The subtitle addition unit adds the subtitles generated by the subtitle generation unit to aerial footage. For example, it displays subtitles for specific locations or objects in the video. The subtitle addition unit can also adjust the display position and format of the subtitles. For example, it can dynamically change the display position of the subtitles to match the objects in the video. Furthermore, the subtitle addition unit can simultaneously display multiple subtitles. For example, it can simultaneously display information about the locations of evacuees and evacuation shelters. This allows the automatic subtitling system according to the embodiment to more easily understand the content of aerial footage. For example, adding subtitles to video of a disaster site allows viewers to quickly and accurately understand the situation of victims and rescue methods.In addition, adding subtitles to videos of tourist spots can provide information about tourist spots, and adding subtitles to videos of construction sites can communicate the progress of construction and important points to note to those involved.

[0030] The analysis unit can simultaneously analyze audio information in aerial footage and generate subtitles based on the correlation between audio and video. For example, when analyzing aerial footage, the analysis unit uses a generation AI to simultaneously analyze audio information in the video and generate subtitles based on the correlation between audio and video. For example, it can analyze the sounds of people shouting and giving instructions at a disaster site and display that content as subtitles. The analysis unit can also analyze audio information in the video and identify the source of the sound. For example, it can analyze the sound of a collapsing building or the sound of a car engine and generate subtitles based on that sound source. Furthermore, the analysis unit can integrate audio information and video information and generate subtitles based on the content of the audio. For example, it can analyze radio communications between rescue teams and display that content as subtitles. This allows for more detailed subtitles to be generated by analyzing audio information.

[0031] The analysis unit can track the movements and changes of objects in the video in real time and generate dynamic subtitles based on the movements. For example, the analysis unit can track the movements of objects in the video in real time and generate subtitles based on the movements. For example, the analysis unit can track the paths of evacuees and generate subtitles such as "Evacuees are heading this way." The analysis unit can also analyze changes in objects in real time and generate subtitles based on the changes. For example, the analysis unit can analyze the progression of a building collapsing and generate subtitles such as "The building is collapsing." The analysis unit can also analyze movements in the video and generate dynamic subtitles. For example, the analysis unit can track the movements of rescue teams and generate subtitles such as "Rescue teams have arrived in this area." This allows dynamic subtitles to be generated based on the movements of objects.

[0032] The analysis unit can integrate multiple images taken from different perspectives and perform a more detailed analysis. For example, the analysis unit integrates multiple aerial images taken from different perspectives, and the generation AI performs a detailed analysis. For example, it can integrate images from multiple drones to grasp the overall picture of a disaster area. The analysis unit also analyzes images from multiple perspectives and generates subtitles based on the integrated information. For example, it can integrate images taken from different angles and generate subtitles such as "This area is safe." Furthermore, the analysis unit integrates images from different perspectives in real time, and the generation AI performs a detailed analysis. For example, it can simultaneously analyze images from multiple drones and generate subtitles such as "Evacuees are heading this way." This makes it possible to integrate images from multiple perspectives for detailed analysis.

[0033] The analysis unit can reconstruct the results of video analysis as a 3D model and generate subtitles based on that model. For example, the analysis unit reconstructs a 3D model based on the results of video analysis and generates subtitles based on that model. For example, a 3D model of the disaster area is created and a subtitle such as "This building has collapsed" is generated. The analysis unit can also use the 3D model to determine the position and status of objects in the video and generate subtitles. For example, the location of evacuees can be identified using a 3D model and a subtitle such as "Evacuees are here" can be generated. Furthermore, the analysis unit can reconstruct the results of video analysis as a 3D model and generate dynamic subtitles based on that model. For example, the movement of rescue teams can be tracked using a 3D model and a subtitle such as "Rescue teams have arrived in this area" can be generated. This allows subtitles to be generated based on the 3D model.

[0034] The analysis unit can integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, the analysis unit can integrate meteorological data in addition to analyzing video to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by heavy rain" can be generated for video of rainy weather. The analysis unit can also integrate geographic information to understand the location and status of objects in the video. For example, a subtitle such as "This location is a shelter" can be generated based on map data. The analysis unit can also integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by strong winds" can be generated based on wind speed data. In this way, meteorological data and geographic information can be integrated to generate subtitles based on environmental conditions.

[0035] The analysis unit can refer to similar past video data, compare it with the past data to detect anomalies, and generate subtitles based on the anomalies. For example, when analyzing a video, the analysis unit refers to similar past video data and detects anomalies. For example, by comparing it with past video, it detects a building collapse and generates a subtitle such as "This building has collapsed." The analysis unit can also compare it with past data to detect anomalies and generate subtitles based on the anomalies. For example, it can compare it with past video to analyze road traffic conditions and generate subtitles such as "This road is impassable." Furthermore, the analysis unit can build a system that refers to similar video data and detects anomalies. For example, it can compare it with past video to analyze the number of evacuees and generate a subtitle such as "The number of evacuees is increasing." This makes it possible to detect anomalies by comparing it with past data and generate subtitles based on the anomalies.

[0036] The analysis unit can integrate this data with other sensor data to generate more detailed subtitles. For example, the analysis unit integrates the results of video analysis with temperature sensor data to generate more detailed subtitles. For example, a subtitle such as "This area is hot" can be generated based on the temperature data. The analysis unit also integrates vibration sensor data to understand the state of objects in the video. For example, a subtitle such as "This building is vibrating" can be generated based on the vibration data. The analysis unit can also integrate other sensor data to generate more detailed subtitles. For example, a subtitle such as "This area is dangerous" can be generated based on data from a temperature sensor and a vibration sensor. This allows for more detailed subtitles to be generated by integrating this data with other sensor data.

[0037] The analysis unit can automatically translate the results of the video analysis into different languages ​​and generate multilingual subtitles. The analysis unit, for example, automatically translates the results of the video analysis into different languages ​​and generates multilingual subtitles based on the results of the video analysis. For example, Japanese subtitles are translated into English or Chinese and displayed. The analysis unit also uses an automatic translation function to convert the results of the video analysis into multilingual subtitles. For example, information about disaster sites can be displayed in multiple languages. Furthermore, the analysis unit translates the results of the video analysis in real time and generates multilingual subtitles. For example, information about evacuation shelters can be displayed in multiple languages. This allows multilingual subtitles to be generated by automatic translation into different languages.

[0038] The subtitle generation unit can evaluate the importance of objects in the video and determine the priority of subtitles based on the importance. For example, the subtitle generation unit can prioritize subtitles by displaying collapsed buildings and the locations of evacuees as subtitles. The subtitle generation unit can also generate detailed subtitles for objects with high importance. For example, the subtitle generation unit can prioritize displaying information about important evacuation shelters and generating subtitles such as "This evacuation shelter is full." The subtitle generation unit can also evaluate the importance of objects in the video in real time and dynamically adjust the priority of subtitles based on the evaluation. For example, the subtitle generation unit can prioritize displaying the movements of rescue teams and generating subtitles such as "Rescue teams have arrived in this area." This allows the priority of subtitles to be determined based on the importance of objects.

[0039] The subtitle generation unit can analyze audio and environmental sounds in the video and generate subtitles based on the audio information. For example, the subtitle generation unit analyzes audio and environmental sounds in the video and generates subtitles based on the audio information. For example, it analyzes the cries of evacuees and instructions from rescue teams and generates subtitles such as "Evacuees are calling for help." The subtitle generation unit also generates subtitles that explain the situation in the video based on audio information. For example, it can analyze the sound of a building collapsing and generate subtitles such as "The building is collapsing." Furthermore, the subtitle generation unit analyzes environmental sounds and generates subtitles based on the audio information. For example, it analyzes the sound of wind and rain and generates subtitles such as "This area is affected by strong winds." In this way, subtitles can be generated based on audio and environmental sounds.

[0040] The subtitle generation unit can generate subtitles that change dynamically based on the movement and position information of objects in the video. The subtitle generation unit generates subtitles that change dynamically based on, for example, the movement and position information of objects in the video. For example, the subtitle generation unit tracks the movement paths of evacuees and generates subtitles such as "Evacuees are heading this way." The subtitle generation unit also builds a system that generates subtitles that change dynamically based on object position information. For example, it can track the location of rescue teams in real time and generate subtitles such as "Rescue teams have arrived in this area." Furthermore, the subtitle generation unit analyzes movement in the video and generates subtitles that change dynamically based on that movement. For example, it analyzes the progress of a building collapsing and generates subtitles such as "The building is collapsing." In this way, it is possible to generate subtitles that change dynamically based on object movement and position information.

[0041] The subtitle generation unit can automatically recognize text information in video and generate subtitles based on that text information. The subtitle generation unit can automatically recognize text information in video and generate subtitles based on that text information. For example, the subtitle generation unit can analyze information on signs and road signs and generate subtitles such as "This area is a shelter." The subtitle generation unit can also use text recognition technology to analyze text information in video and generate subtitles based on that information. For example, the subtitle generation unit can analyze information on road signs and generate subtitles such as "This road is passable." Furthermore, the subtitle generation unit can recognize text information in video in real time and build a system that generates subtitles based on that information. For example, the subtitle generation unit can analyze the name and address of a building and generate subtitles such as "This building is a shelter." This allows subtitles to be generated based on text information in video.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can track the movements and changes of objects in the video in real time and generate dynamic subtitles based on that movement. For example, it can track the path of evacuees and generate subtitles such as "Evacuees are heading this way." The analysis unit can also analyze changes in objects in real time and generate subtitles based on those changes. For example, it can analyze the progression of a building collapsing and generate subtitles such as "The building is collapsing." The analysis unit can also analyze movements in the video and generate dynamic subtitles. For example, it can track the movements of rescue teams and generate subtitles such as "Rescue teams have arrived in this area." This makes it possible to generate dynamic subtitles based on the movement of objects.

[0044] The analysis unit can simultaneously analyze audio information in the aerial footage and generate subtitles based on the correlation between audio and video. For example, it can analyze the sounds of people shouting and giving instructions at a disaster site and display the content as subtitles. The analysis unit can also analyze audio information in the video and identify the source of the sound. For example, it can analyze the sound of a collapsing building or the sound of a car engine and generate subtitles based on the sound source. Furthermore, the analysis unit can integrate audio information and video information and generate subtitles based on the content of the audio. For example, it can analyze radio communications between rescue teams and display the content as subtitles. This allows for more detailed subtitles to be generated by analyzing audio information.

[0045] The analysis unit can integrate multiple images taken from different perspectives to perform more detailed analysis. For example, it can integrate images from multiple drones to grasp the overall picture of a disaster area. The analysis unit can also analyze images from multiple perspectives and generate subtitles based on the integrated information. For example, it can integrate images taken from different angles to generate subtitles such as "This area is safe." Furthermore, the analysis unit integrates images from different perspectives in real time, and the generation AI performs a detailed analysis. For example, it can simultaneously analyze images from multiple drones and generate subtitles such as "Evacuees are heading this way." This makes it possible to integrate images from multiple perspectives for more detailed analysis.

[0046] The analysis unit can reconstruct the results of video analysis as a 3D model and generate subtitles based on that model. For example, it can create a 3D model of the disaster area and generate subtitles such as "This building has collapsed." The analysis unit can also use the 3D model to determine the position and status of objects in the video and generate subtitles. For example, it can identify the location of evacuees using a 3D model and generate subtitles such as "Evacuees are here." The analysis unit can also reconstruct the results of video analysis as a 3D model and generate dynamic subtitles based on that model. For example, it can track the movements of rescue teams using a 3D model and generate subtitles such as "Rescue teams have arrived in this area." This allows subtitles to be generated based on the 3D model.

[0047] The analysis unit can integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, in addition to analyzing video, meteorological data can be integrated to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by heavy rain" can be generated for video footage of rainy weather. The analysis unit also integrates geographic information to understand the location and status of objects in the video. For example, a subtitle such as "This location is a shelter" can be generated based on map data. The analysis unit can also integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by strong winds" can be generated based on wind speed data. This makes it possible to generate subtitles based on environmental conditions by integrating meteorological data and geographic information.

[0048] The analysis unit can refer to similar past video data, compare it with the past data to detect anomalies, and generate subtitles based on those anomalies. For example, by comparing it with past video, it can detect a building collapse and generate subtitles such as "This building is collapsing." The analysis unit can also compare it with past data to detect anomalies and generate subtitles based on those anomalies. For example, it can compare it with past video to analyze road traffic conditions and generate subtitles such as "This road is impassable." Furthermore, the analysis unit can build a system that refers to similar video data and detects anomalies. For example, it can compare it with past video to analyze the number of evacuees and generate subtitles such as "The number of evacuees is increasing." This makes it possible to detect anomalies by comparing it with past data and generate subtitles based on those anomalies.

[0049] The analysis unit can integrate data from other sensors to generate more detailed subtitles. For example, the results of video analysis can be integrated with temperature sensor data to generate more detailed subtitles. For example, a subtitle such as "This area is hot" can be generated based on the temperature data. The analysis unit can also integrate vibration sensor data to understand the state of objects in the video. For example, a subtitle such as "This building is vibrating" can be generated based on the vibration data. The analysis unit can also integrate data from other sensors to generate more detailed subtitles. For example, a subtitle such as "This area is dangerous" can be generated based on data from temperature and vibration sensors. This allows for the generation of more detailed subtitles by integrating data from other sensors.

[0050] The analysis unit can automatically translate the results of the video analysis into different languages ​​and generate multilingual subtitles. For example, multilingual subtitles can be generated by automatically translating into different languages ​​based on the results of the video analysis. For example, Japanese subtitles can be translated into English or Chinese and displayed. The analysis unit can also use an automatic translation function to convert the results of the video analysis into multilingual subtitles. For example, information about disaster sites can be displayed in multiple languages. Furthermore, the analysis unit can translate the results of the video analysis in real time and generate multilingual subtitles. For example, information about evacuation shelters can be displayed in multiple languages. This allows multilingual subtitles to be generated by automatic translation into different languages.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The aerial image acquisition unit acquires aerial images taken by a drone. For example, high-resolution images are acquired using the drone's camera. The aerial image acquisition unit can also acquire images in real time. For example, live streaming technology is used to acquire images from the drone in real time. Furthermore, the aerial image acquisition unit can also acquire images taken in the past from a database. For example, it acquires images stored in cloud storage. Step 2: The analysis unit analyzes the aerial footage acquired by the aerial footage acquisition unit. For example, the generation AI recognizes objects such as buildings, roads, and people in the footage. The generation AI can also grasp the position and status of objects in the footage. For example, it uses image recognition technology to analyze the state of collapsed buildings and the locations of evacuees. Furthermore, the analysis unit can track movements and changes in the footage in real time. For example, it uses a motion detection algorithm to track the movement paths of evacuees. Step 3: The subtitle generation unit generates subtitles based on the results of the analysis by the analysis unit. For example, the generation AI generates subtitles such as "There is a collapsed building here" for footage of a disaster site. The generation AI can also generate subtitles such as "Evacuees are gathering in this area" based on the location of evacuees. Furthermore, the subtitle generation unit can generate dynamic subtitles based on the movement of objects in the video. For example, it generates subtitles such as "Evacuees are heading this way" in accordance with the movement of evacuees. Step 4: The subtitle adding unit adds the subtitles generated by the subtitle generating unit to the aerial footage. For example, it displays subtitles at specific locations or objects within the footage. The subtitle adding unit can also adjust the display position and format of the subtitles. For example, it can dynamically change the display position of the subtitles to match the objects within the footage. Furthermore, the subtitle adding unit can also display multiple subtitles simultaneously. For example, it can simultaneously display the locations of evacuees and information about evacuation shelters.

[0053] (Example 2) The automatic captioning system according to an embodiment of the present invention automatically analyzes aerial footage shot by a drone, and uses a generation AI to generate appropriate captions and add them to the footage. This allows the automatic captioning system to make the content of the aerial footage easier for viewers to understand.

[0054] An automatic captioning system according to an embodiment includes an aerial video acquisition unit, an analysis unit, a subtitle generation unit, and a subtitle addition unit. The aerial video acquisition unit acquires aerial video captured by a drone. For example, high-resolution video is acquired using the drone's camera. The aerial video acquisition unit can also acquire video in real time. For example, it can acquire video from a drone in real time using live streaming technology. The aerial video acquisition unit can also acquire previously captured video from a database. For example, it can acquire video stored in cloud storage. The analysis unit analyzes the aerial video acquired by the aerial video acquisition unit. For example, the generation AI recognizes objects such as buildings, roads, and people in the video. The generation AI can also grasp the position and status of objects in the video. For example, it uses video recognition technology to analyze the collapsed state of buildings and the locations of evacuees. The analysis unit can also track movements and changes in the video in real time. For example, it uses a motion detection algorithm to track the movement paths of evacuees. The subtitle generation unit generates subtitles based on the results of the analysis by the analysis unit. For example, the generation AI generates subtitles such as "There is a collapsed building here" for video of a disaster site. The generation AI can also generate subtitles such as "Evacuees are gathering in this area" based on the locations of evacuees. Furthermore, the subtitle generation unit can generate dynamic subtitles based on the movement of objects in the video. For example, it generates subtitles such as "Evacuees are heading this way" to match the movement of evacuees. The subtitle addition unit adds the subtitles generated by the subtitle generation unit to aerial footage. For example, it displays subtitles for specific locations or objects in the video. The subtitle addition unit can also adjust the display position and format of the subtitles. For example, it can dynamically change the display position of the subtitles to match the objects in the video. Furthermore, the subtitle addition unit can simultaneously display multiple subtitles. For example, it can simultaneously display information about the locations of evacuees and evacuation shelters. This allows the automatic subtitling system according to the embodiment to more easily understand the content of aerial footage. For example, adding subtitles to video of a disaster site allows viewers to quickly and accurately understand the situation of victims and rescue methods.In addition, adding subtitles to videos of tourist spots can provide information about tourist spots, and adding subtitles to videos of construction sites can communicate the progress of construction and important points to note to those involved.

[0055] The analysis unit can simultaneously analyze audio information in aerial footage and generate subtitles based on the correlation between audio and video. For example, when analyzing aerial footage, the analysis unit uses a generation AI to simultaneously analyze audio information in the video and generate subtitles based on the correlation between audio and video. For example, it can analyze the sounds of people shouting and giving instructions at a disaster site and display that content as subtitles. The analysis unit can also analyze audio information in the video and identify the source of the sound. For example, it can analyze the sound of a collapsing building or the sound of a car engine and generate subtitles based on that sound source. Furthermore, the analysis unit can integrate audio information and video information and generate subtitles based on the content of the audio. For example, it can analyze radio communications between rescue teams and display that content as subtitles. This allows for more detailed subtitles to be generated by analyzing audio information.

[0056] The analysis unit can track the movements and changes of objects in the video in real time and generate dynamic subtitles based on the movements. For example, the analysis unit can track the movements of objects in the video in real time and generate subtitles based on the movements. For example, the analysis unit can track the paths of evacuees and generate subtitles such as "Evacuees are heading this way." The analysis unit can also analyze changes in objects in real time and generate subtitles based on the changes. For example, the analysis unit can analyze the progression of a building collapsing and generate subtitles such as "The building is collapsing." The analysis unit can also analyze movements in the video and generate dynamic subtitles. For example, the analysis unit can track the movements of rescue teams and generate subtitles such as "Rescue teams have arrived in this area." This allows dynamic subtitles to be generated based on the movements of objects.

[0057] The analysis unit can use the emotion estimation function to estimate emotions from the facial expressions and movements of people in the video and generate subtitles based on those emotions. The analysis unit, for example, analyzes the facial expressions of people in the video and generates subtitles based on those emotions. For example, it analyzes the facial expressions of evacuees and generates subtitles such as "The evacuees look anxious." The analysis unit can also analyze the movements of people in the video and generate subtitles based on those emotions. For example, it can analyze the movements of rescue teams and generate subtitles such as "The rescue teams are nervous." The analysis unit can also use the emotion estimation function to estimate emotions of people in the video and generate subtitles based on those emotions. For example, it can analyze the facial expressions and movements of victims and generate subtitles such as "The victims look relieved." In this way, subtitles can be generated based on people's emotions.

[0058] The analysis unit can integrate multiple images taken from different perspectives and perform a more detailed analysis. For example, the analysis unit integrates multiple aerial images taken from different perspectives, and the generation AI performs a detailed analysis. For example, it can integrate images from multiple drones to grasp the overall picture of a disaster area. The analysis unit also analyzes images from multiple perspectives and generates subtitles based on the integrated information. For example, it can integrate images taken from different angles and generate subtitles such as "This area is safe." Furthermore, the analysis unit integrates images from different perspectives in real time, and the generation AI performs a detailed analysis. For example, it can simultaneously analyze images from multiple drones and generate subtitles such as "Evacuees are heading this way." This makes it possible to integrate images from multiple perspectives for detailed analysis.

[0059] The analysis unit can reconstruct the results of video analysis as a 3D model and generate subtitles based on that model. For example, the analysis unit reconstructs a 3D model based on the results of video analysis and generates subtitles based on that model. For example, a 3D model of the disaster area is created and a subtitle such as "This building has collapsed" is generated. The analysis unit can also use the 3D model to determine the position and status of objects in the video and generate subtitles. For example, the location of evacuees can be identified using a 3D model and a subtitle such as "Evacuees are here" can be generated. Furthermore, the analysis unit can reconstruct the results of video analysis as a 3D model and generate dynamic subtitles based on that model. For example, the movement of rescue teams can be tracked using a 3D model and a subtitle such as "Rescue teams have arrived in this area" can be generated. This allows subtitles to be generated based on the 3D model.

[0060] The analysis unit can use the emotion estimation function to monitor the viewer's emotional response in real time and dynamically adjust the content of the subtitles based on that response. For example, the analysis unit can use the emotion estimation function to monitor the viewer's emotional response in real time and dynamically adjust the content of the subtitles based on that response. For example, if the viewer feels anxious, subtitles with reassuring content are displayed. The analysis unit can also analyze the viewer's emotional response and adjust the content of the subtitles based on the results. For example, it can generate subtitles that provide detailed information about parts that interest the viewer. Furthermore, the analysis unit can use the emotion estimation function to collect the viewer's emotional response in real time and dynamically adjust the content of the subtitles based on that data. For example, if the viewer is surprised, subtitles that explain the reason are displayed. This makes it possible to dynamically adjust the content of the subtitles based on the viewer's emotional response.

[0061] The analysis unit can integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, the analysis unit can integrate meteorological data in addition to analyzing video to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by heavy rain" can be generated for video of rainy weather. The analysis unit can also integrate geographic information to understand the location and status of objects in the video. For example, a subtitle such as "This location is a shelter" can be generated based on map data. The analysis unit can also integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by strong winds" can be generated based on wind speed data. In this way, meteorological data and geographic information can be integrated to generate subtitles based on environmental conditions.

[0062] The analysis unit can refer to similar past video data, compare it with the past data to detect anomalies, and generate subtitles based on the anomalies. For example, when analyzing a video, the analysis unit refers to similar past video data and detects anomalies. For example, by comparing it with past video, it detects a building collapse and generates a subtitle such as "This building has collapsed." The analysis unit can also compare it with past data to detect anomalies and generate subtitles based on the anomalies. For example, it can compare it with past video to analyze road traffic conditions and generate subtitles such as "This road is impassable." Furthermore, the analysis unit can build a system that refers to similar video data and detects anomalies. For example, it can compare it with past video to analyze the number of evacuees and generate a subtitle such as "The number of evacuees is increasing." This makes it possible to detect anomalies by comparing it with past data and generate subtitles based on the anomalies.

[0063] The analysis unit can integrate this data with other sensor data to generate more detailed subtitles. For example, the analysis unit integrates the results of video analysis with temperature sensor data to generate more detailed subtitles. For example, a subtitle such as "This area is hot" can be generated based on the temperature data. The analysis unit also integrates vibration sensor data to understand the state of objects in the video. For example, a subtitle such as "This building is vibrating" can be generated based on the vibration data. The analysis unit can also integrate other sensor data to generate more detailed subtitles. For example, a subtitle such as "This area is dangerous" can be generated based on data from a temperature sensor and a vibration sensor. This allows for more detailed subtitles to be generated by integrating this data with other sensor data.

[0064] The analysis unit can automatically translate the results of the video analysis into different languages ​​and generate multilingual subtitles. The analysis unit, for example, automatically translates the results of the video analysis into different languages ​​and generates multilingual subtitles based on the results of the video analysis. For example, Japanese subtitles are translated into English or Chinese and displayed. The analysis unit also uses an automatic translation function to convert the results of the video analysis into multilingual subtitles. For example, information about disaster sites can be displayed in multiple languages. Furthermore, the analysis unit translates the results of the video analysis in real time and generates multilingual subtitles. For example, information about evacuation shelters can be displayed in multiple languages. This allows multilingual subtitles to be generated by automatic translation into different languages.

[0065] The analysis unit can analyze the viewer's emotional response and adjust the timing and style of subtitle display based on that response. The analysis unit, for example, uses an emotion estimation function to analyze the viewer's emotional response and adjust the timing of subtitle display based on that response. For example, if the viewer is surprised, the display of subtitles can be delayed. The analysis unit can also analyze the viewer's emotional response and adjust the style of subtitles based on the results. For example, if the viewer feels anxious, subtitles with reassuring content can be displayed. Furthermore, the analysis unit can collect the viewer's emotional response in real time using the emotion estimation function and dynamically adjust the timing and style of subtitle display based on that data. For example, subtitles that provide detailed information about parts that the viewer is interested in can be generated. This makes it possible to adjust the timing and style of subtitle display based on the viewer's emotional response.

[0066] The subtitle generation unit can evaluate the importance of objects in the video and determine the priority of subtitles based on the importance. For example, the subtitle generation unit can prioritize subtitles by displaying collapsed buildings and the locations of evacuees as subtitles. The subtitle generation unit can also generate detailed subtitles for objects with high importance. For example, the subtitle generation unit can prioritize displaying information about important evacuation shelters and generating subtitles such as "This evacuation shelter is full." The subtitle generation unit can also evaluate the importance of objects in the video in real time and dynamically adjust the priority of subtitles based on the evaluation. For example, the subtitle generation unit can prioritize displaying the movements of rescue teams and generating subtitles such as "Rescue teams have arrived in this area." This allows the priority of subtitles to be determined based on the importance of objects.

[0067] The subtitle generation unit can analyze audio and environmental sounds in the video and generate subtitles based on the audio information. For example, the subtitle generation unit analyzes audio and environmental sounds in the video and generates subtitles based on the audio information. For example, it analyzes the cries of evacuees and instructions from rescue teams and generates subtitles such as "Evacuees are calling for help." The subtitle generation unit also generates subtitles that explain the situation in the video based on audio information. For example, it can analyze the sound of a building collapsing and generate subtitles such as "The building is collapsing." Furthermore, the subtitle generation unit analyzes environmental sounds and generates subtitles based on the audio information. For example, it analyzes the sound of wind and rain and generates subtitles such as "This area is affected by strong winds." In this way, subtitles can be generated based on audio and environmental sounds.

[0068] The subtitle generation unit can predict the viewer's emotional response and generate subtitles that are easy to empathize with emotionally based on the prediction. The subtitle generation unit, for example, uses an emotion estimation function to predict the viewer's emotional response and generates subtitles that are easy to empathize with emotionally based on the prediction. For example, subtitles with content that reassures the viewer in a scene where the viewer feels anxious are displayed. The subtitle generation unit also builds a system that predicts the viewer's emotional response and generates subtitles that are easy to empathize with emotionally based on the prediction. For example, it can also prioritize the display of information that interests the viewer. Furthermore, the subtitle generation unit predicts the viewer's emotional response based on the emotion estimation data and generates subtitles that are easy to empathize with emotionally based on the prediction. For example, in a scene where the viewer is surprised, subtitles that explain the reason are displayed. In this way, the viewer's emotional response can be predicted and subtitles that are easy to empathize with emotionally can be generated.

[0069] The subtitle generation unit can generate subtitles that change dynamically based on the movement and position information of objects in the video. The subtitle generation unit generates subtitles that change dynamically based on, for example, the movement and position information of objects in the video. For example, the subtitle generation unit tracks the movement paths of evacuees and generates subtitles such as "Evacuees are heading this way." The subtitle generation unit also builds a system that generates subtitles that change dynamically based on object position information. For example, it can track the location of rescue teams in real time and generate subtitles such as "Rescue teams have arrived in this area." Furthermore, the subtitle generation unit analyzes movement in the video and generates subtitles that change dynamically based on that movement. For example, it analyzes the progress of a building collapsing and generates subtitles such as "The building is collapsing." In this way, it is possible to generate subtitles that change dynamically based on object movement and position information.

[0070] The subtitle generation unit can automatically recognize text information in video and generate subtitles based on that text information. The subtitle generation unit can automatically recognize text information in video and generate subtitles based on that text information. For example, the subtitle generation unit can analyze information on signs and road signs and generate subtitles such as "This area is a shelter." The subtitle generation unit can also use text recognition technology to analyze text information in video and generate subtitles based on that information. For example, the subtitle generation unit can analyze information on road signs and generate subtitles such as "This road is passable." Furthermore, the subtitle generation unit can recognize text information in video in real time and build a system that generates subtitles based on that information. For example, the subtitle generation unit can analyze the name and address of a building and generate subtitles such as "This building is a shelter." This allows subtitles to be generated based on text information in video.

[0071] The subtitle generation unit can monitor the viewer's emotional response in real time and dynamically adjust the content and display method of the subtitles based on the response. The subtitle generation unit, for example, uses an emotion estimation function to monitor the viewer's emotional response in real time and dynamically adjust the content of the subtitles based on the response. For example, if the viewer feels anxious, it displays subtitles with reassuring content. The subtitle generation unit also analyzes the viewer's emotional response and adjusts the display method of the subtitles based on the results. For example, it can generate subtitles that provide detailed information about parts that interest the viewer. Furthermore, the subtitle generation unit uses the emotion estimation function to collect the viewer's emotional response in real time and dynamically adjust the content and display method of the subtitles based on the data. For example, if the viewer is surprised, it displays subtitles that explain the reason. This makes it possible to dynamically adjust the content and display method of the subtitles based on the viewer's emotional response.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The analysis unit can track the movements and changes of objects in the video in real time and generate dynamic subtitles based on that movement. For example, it can track the path of evacuees and generate subtitles such as "Evacuees are heading this way." The analysis unit can also analyze changes in objects in real time and generate subtitles based on those changes. For example, it can analyze the progression of a building collapsing and generate subtitles such as "The building is collapsing." The analysis unit can also analyze movements in the video and generate dynamic subtitles. For example, it can track the movements of rescue teams and generate subtitles such as "Rescue teams have arrived in this area." This makes it possible to generate dynamic subtitles based on the movement of objects.

[0074] The analysis unit can simultaneously analyze audio information in the aerial footage and generate subtitles based on the correlation between audio and video. For example, it can analyze the sounds of people shouting and giving instructions at a disaster site and display the content as subtitles. The analysis unit can also analyze audio information in the video and identify the source of the sound. For example, it can analyze the sound of a collapsing building or the sound of a car engine and generate subtitles based on the sound source. Furthermore, the analysis unit can integrate audio information and video information and generate subtitles based on the content of the audio. For example, it can analyze radio communications between rescue teams and display the content as subtitles. This allows for more detailed subtitles to be generated by analyzing audio information.

[0075] The analysis unit can use the emotion estimation function to estimate emotions from the facial expressions and movements of people in the video and generate subtitles based on those emotions. For example, it can analyze the facial expressions of evacuees and generate subtitles such as "The evacuees look anxious." The analysis unit can also analyze the movements of people in the video and generate subtitles based on those emotions. For example, it can analyze the movements of rescue teams and generate subtitles such as "The rescue team looks nervous." The analysis unit can also use the emotion estimation function to estimate emotions of people in the video and generate subtitles based on those emotions. For example, it can analyze the facial expressions and movements of victims and generate subtitles such as "The victims look relieved." This makes it possible to generate subtitles based on people's emotions.

[0076] The analysis unit can integrate multiple images taken from different perspectives to perform more detailed analysis. For example, it can integrate images from multiple drones to grasp the overall picture of a disaster area. The analysis unit can also analyze images from multiple perspectives and generate subtitles based on the integrated information. For example, it can integrate images taken from different angles to generate subtitles such as "This area is safe." Furthermore, the analysis unit integrates images from different perspectives in real time, and the generation AI performs a detailed analysis. For example, it can simultaneously analyze images from multiple drones and generate subtitles such as "Evacuees are heading this way." This makes it possible to integrate images from multiple perspectives for more detailed analysis.

[0077] The analysis unit can reconstruct the results of video analysis as a 3D model and generate subtitles based on that model. For example, it can create a 3D model of the disaster area and generate subtitles such as "This building has collapsed." The analysis unit can also use the 3D model to determine the position and status of objects in the video and generate subtitles. For example, it can identify the location of evacuees using a 3D model and generate subtitles such as "Evacuees are here." The analysis unit can also reconstruct the results of video analysis as a 3D model and generate dynamic subtitles based on that model. For example, it can track the movements of rescue teams using a 3D model and generate subtitles such as "Rescue teams have arrived in this area." This allows subtitles to be generated based on the 3D model.

[0078] The analysis unit can use the emotion estimation function to monitor the viewer's emotional response in real time and dynamically adjust the content of the subtitles based on that response. For example, if the viewer feels anxious, subtitles with reassuring content can be displayed. The analysis unit can also analyze the viewer's emotional response and adjust the content of the subtitles based on the results. For example, it can generate subtitles that provide detailed information about parts that interest the viewer. Furthermore, the analysis unit can use the emotion estimation function to collect the viewer's emotional response in real time and dynamically adjust the content of the subtitles based on that data. For example, if the viewer is surprised, subtitles that explain the reason can be displayed. This makes it possible to dynamically adjust the content of the subtitles based on the viewer's emotional response.

[0079] The analysis unit can integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, in addition to analyzing video, meteorological data can be integrated to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by heavy rain" can be generated for video footage of rainy weather. The analysis unit also integrates geographic information to understand the location and status of objects in the video. For example, a subtitle such as "This location is a shelter" can be generated based on map data. The analysis unit can also integrate meteorological data and geographic information to generate subtitles based on environmental conditions. For example, a subtitle such as "This area is affected by strong winds" can be generated based on wind speed data. This makes it possible to generate subtitles based on environmental conditions by integrating meteorological data and geographic information.

[0080] The analysis unit can refer to similar past video data, compare it with the past data to detect anomalies, and generate subtitles based on those anomalies. For example, by comparing it with past video, it can detect a building collapse and generate subtitles such as "This building is collapsing." The analysis unit can also compare it with past data to detect anomalies and generate subtitles based on those anomalies. For example, it can compare it with past video to analyze road traffic conditions and generate subtitles such as "This road is impassable." Furthermore, the analysis unit can build a system that refers to similar video data and detects anomalies. For example, it can compare it with past video to analyze the number of evacuees and generate subtitles such as "The number of evacuees is increasing." This makes it possible to detect anomalies by comparing it with past data and generate subtitles based on those anomalies.

[0081] The analysis unit can integrate data from other sensors to generate more detailed subtitles. For example, the results of video analysis can be integrated with temperature sensor data to generate more detailed subtitles. For example, a subtitle such as "This area is hot" can be generated based on the temperature data. The analysis unit can also integrate vibration sensor data to understand the state of objects in the video. For example, a subtitle such as "This building is vibrating" can be generated based on the vibration data. The analysis unit can also integrate data from other sensors to generate more detailed subtitles. For example, a subtitle such as "This area is dangerous" can be generated based on data from temperature and vibration sensors. This allows for the generation of more detailed subtitles by integrating data from other sensors.

[0082] The analysis unit can automatically translate the results of the video analysis into different languages ​​and generate multilingual subtitles. For example, multilingual subtitles can be generated by automatically translating into different languages ​​based on the results of the video analysis. For example, Japanese subtitles can be translated into English or Chinese and displayed. The analysis unit can also use an automatic translation function to convert the results of the video analysis into multilingual subtitles. For example, information about disaster sites can be displayed in multiple languages. Furthermore, the analysis unit can translate the results of the video analysis in real time and generate multilingual subtitles. For example, information about evacuation shelters can be displayed in multiple languages. This allows multilingual subtitles to be generated by automatic translation into different languages.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The aerial image acquisition unit acquires aerial images taken by a drone. For example, high-resolution images are acquired using the drone's camera. The aerial image acquisition unit can also acquire images in real time. For example, live streaming technology is used to acquire images from the drone in real time. Furthermore, the aerial image acquisition unit can also acquire images taken in the past from a database. For example, it acquires images stored in cloud storage. Step 2: The analysis unit analyzes the aerial footage acquired by the aerial footage acquisition unit. For example, the generation AI recognizes objects such as buildings, roads, and people in the footage. The generation AI can also grasp the position and status of objects in the footage. For example, it uses image recognition technology to analyze the state of collapsed buildings and the locations of evacuees. Furthermore, the analysis unit can track movements and changes in the footage in real time. For example, it uses a motion detection algorithm to track the movement paths of evacuees. Step 3: The subtitle generation unit generates subtitles based on the results of the analysis by the analysis unit. For example, the generation AI generates subtitles such as "There is a collapsed building here" for footage of a disaster site. The generation AI can also generate subtitles such as "Evacuees are gathering in this area" based on the location of evacuees. Furthermore, the subtitle generation unit can generate dynamic subtitles based on the movement of objects in the video. For example, it generates subtitles such as "Evacuees are heading this way" in accordance with the movement of evacuees. Step 4: The subtitle adding unit adds the subtitles generated by the subtitle generating unit to the aerial footage. For example, it displays subtitles at specific locations or objects within the footage. The subtitle adding unit can also adjust the display position and format of the subtitles. For example, it can dynamically change the display position of the subtitles to match the objects within the footage. Furthermore, the subtitle adding unit can also display multiple subtitles simultaneously. For example, it can simultaneously display the locations of evacuees and information about evacuation shelters.

[0085] 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.

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

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

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

[0091] The 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.

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

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

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

[0095] Fig. 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.

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

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

[0098] 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.

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

[0100] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0102] The data processing system 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

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

[0106] The 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.

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

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 aerial image acquisition unit that acquires aerial images; an analysis unit that analyzes the aerial image acquired by the aerial image acquisition unit; a caption generation unit that generates captions based on the results of the analysis by the analysis unit; a subtitle adding unit that adds the subtitles generated by the subtitle generating unit to the aerial image. A system characterized by:

2. The analysis unit The audio information in the aerial video is simultaneously analyzed, and the subtitles are generated based on the correlation between the audio and the video.

2. The system of claim 1.

3. The analysis unit Tracks the movement and changes of objects in the video in real time and generates dynamic subtitles based on that movement 2. The system of claim 1.

4. The analysis unit Estimate emotions from the facial expressions and movements of people in the video and generate subtitles based on those emotions.

2. The system of claim 1.

5. The analysis unit Integrating multiple images taken from different perspectives for more detailed analysis 2. The system of claim 1.

Images