system

Abstract

The system according to this embodiment aims to efficiently edit videos of school events individually and customize them according to the user's wishes. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a reception unit, and a customization unit. The collection unit collects videos of school events such as sports days. The analysis unit analyzes the videos collected by the collection unit. The generation unit generates individual videos for each child based on the data analyzed by the analysis unit. The reception unit receives user requests. The customization unit customizes the videos based on the requests received by the reception unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the work of individually editing videos of school events requires time and effort and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently edit videos of school events individually and customize them according to the user's wishes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a reception unit, and a customization unit. The collection unit collects videos of school events such as sports days. The analysis unit analyzes the videos collected by the collection unit. The generation unit generates individual videos for each child based on the data analyzed by the analysis unit. The reception unit receives user requests. The customization unit customizes the videos based on the requests received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently edit videos of school events individually and customize them according to the user's wishes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The video generation system according to an embodiment of the present invention is a system that automatically generates videos of school events such as sports days. This video generation system films videos of school events such as sports days with a fixed camera, and an AI analyzes the footage to recognize the movements of each child and generates individual videos. Furthermore, users can customize the videos using an app, and by simply telling the AI ​​their preferences, they can emphasize specific scenes or prioritize the display of footage of specific children. For example, the video generation system films a sports day with a fixed camera. Next, the video generation system uses an AI to analyze the filmed video, recognize the movements of each child, and generate individual videos. Furthermore, the video generation system allows users to customize the videos using an app. For example, by simply telling the AI ​​their preferences, users can emphasize specific scenes or prioritize the display of footage of specific children. This system makes it easy to create and customize videos of school events such as sports days, which is extremely convenient for parents and other stakeholders.

[0029] The video generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a reception unit, and a customization unit. The collection unit collects videos of school events such as sports days. The collection unit can collect videos using, for example, a fixed camera. The collection unit can also collect videos from different angles using multiple cameras. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions. For example, if the user is excited, the AI ​​in the collection unit can detect emotions in real time and adjust the timing of video collection to avoid missing important moments. Also, if the user is relaxed, the AI ​​in the collection unit can detect emotions and start collecting videos before important events occur. Furthermore, if the user is nervous, the AI ​​in the collection unit can detect emotions and temporarily stop video collection to alleviate the tension. The analysis unit analyzes the videos collected by the collection unit. The analysis unit can, for example, recognize the movements of each child in the video and generate individual videos. The analysis unit recognizes movements based, for example, the algorithm used and the accuracy of the analysis. Furthermore, the analysis unit can optimize the analysis results by considering background and environmental information when analyzing videos. For example, the analysis unit can optimize the analysis results by considering the background color and brightness. The generation unit generates individual videos for each child based on the data analyzed by the analysis unit. For example, the generation unit can track each child's running in a footrace scene and create a video for each child. The generation unit can also generate videos to emphasize specific actions or facial expressions. The reception unit receives user requests. For example, the reception unit allows users to input their requests using the app. The reception unit can also estimate the user's emotions and adjust the method of receiving requests based on the estimated emotions. For example, if the user is excited, the reception unit's AI can detect their emotions and provide a simple and quick method of receiving requests. The customization unit customizes the videos based on the requests received by the reception unit. For example, the customization unit can perform customizations such as emphasizing specific scenes or prioritizing the display of videos of specific children.The customization unit can also estimate the user's emotions and adjust the customization content based on those emotions. For example, if the user is excited, the customization unit's AI can detect the emotion and provide a visually stimulating customization. This allows the video generation system according to the embodiment to easily create and customize videos of school events such as sports days.

[0030] The collection unit collects videos of school events such as sports days. The collection unit can, for example, collect videos using a fixed camera. It can also collect videos from different angles using multiple cameras. Specifically, a fixed camera is positioned to capture an overall view of the sports field, collecting wide-angle footage. Meanwhile, multiple cameras capture key points in each competition or the spectator stands from different angles. This allows the collection unit to cover both the overall picture and detailed scenes of the event. Furthermore, the collection unit can estimate the user's emotions and adjust the video collection timing based on the estimated emotions. For example, if the user is excited, the AI ​​can detect their emotions in real time and adjust the video collection timing to ensure important moments are not missed. The AI ​​analyzes biometric data such as the user's facial expressions, tone of voice, and heart rate to detect their state of excitement. This ensures the collection unit captures moments that are of particular interest to the user. Conversely, if the user is relaxed, the AI ​​can detect their emotions and begin video collection before important events occur. For example, to ensure that a relaxed user doesn't miss a particular event, the AI ​​can start collecting footage in advance, reliably recording important scenes. Furthermore, if the user is feeling tense, the AI ​​can detect their emotions and temporarily stop collecting video to alleviate the tension. This allows the user to avoid scenes that cause tension and enjoy the video in a relaxed state. By combining these functions, the collection unit achieves flexible video collection that responds to the user's emotions, supporting the creation of more satisfying videos.

[0031] The analysis unit analyzes the videos collected by the collection unit. For example, the analysis unit can recognize the movements of each child in the video and generate individual videos. Specifically, the analysis unit utilizes deep learning-based image recognition technology to detect the facial and body features of each child. This makes it possible to accurately track the movements of each child and generate individual videos. The analysis unit recognizes movement based on the algorithm used and the accuracy of the analysis. For example, it can use a convolutional neural network (CNN) to extract features from each frame in the video and a recurrent neural network (RNN) to analyze the temporal movement patterns. The analysis unit can also optimize the analysis results by considering background and environmental information when analyzing the video. For example, the analysis unit optimizes the analysis results by considering the color and brightness of the background. Specifically, it performs appropriate filtering and correction depending on whether the background is bright or dark to improve the accuracy of the analysis. Furthermore, the analysis unit also analyzes audio data and can identify specific scenes and events using speech recognition technology. For example, it can detect the starting signal for a foot race or the cheers of the crowd and identify important scenes in the video based on these audio events. This allows the analysis unit to analyze the collected data from multiple angles and support the generation of more accurate individual videos.

[0032] The generation unit generates individual videos for each child based on data analyzed by the analysis unit. For example, the generation unit can track each child's running in a footrace scene and create a video for each child. Specifically, the generation unit tracks each child's movements based on tracking data provided by the analysis unit and generates individual video clips. The generation unit can also generate videos to emphasize specific actions or expressions. For example, it can apply effects such as slow motion or zoom-in to emphasize particularly moving scenes, such as the finish line of a footrace or the moment of the awards ceremony. Furthermore, the generation unit can enhance the emotional impact of the video by adding music and narration. For example, it can create a greater sense of realism by selecting an emotional song as background music and introducing each child's name and the results of the competition with narration. The generation unit can also customize the style and theme of the video according to the user's preferences. For example, it can adjust the color tone and effects of the video based on the theme desired by the user to generate a video with a specific atmosphere. In this way, the generation unit can generate a variety of videos that meet the user's needs and record individual memories more vividly.

[0033] The reception desk receives user requests. For example, users can input their requests using an app. Specifically, users can use a smartphone or tablet to input their desired video content and style through a dedicated application. The reception desk can also estimate the user's emotions and adjust the request processing method based on that estimation. For example, if a user is excited, the AI ​​can detect their emotions and provide a simple and quick request processing method. The AI ​​analyzes the user's facial expressions, tone of voice, and input speed to detect their level of excitement. This allows the reception desk to provide a concise interface and auto-completion features to enable users to input their requests quickly. Furthermore, if a user is relaxed, the reception desk offers detailed customization options, allowing them to input their requests at their leisure. For example, relaxed users are offered detailed customization options such as color scheme, effects, and music selection to help them create a video they are satisfied with. Additionally, if a user is tense, the AI ​​can detect their emotions and provide guidance and support to alleviate their tension. This creates an environment where users can input their requests with confidence. Through these functions, the reception department provides flexible reception services that respond to the user's emotions, resulting in a more satisfying service.

[0034] The customization department customizes videos based on requests received by the reception department. For example, the customization department can highlight specific scenes or prioritize the display of footage of specific children. Specifically, the customization department edits the video to prioritize scenes or footage of children requested by the user, and applies effects and transitions to highlight specific moments. The customization department can also estimate the user's emotions and adjust the customization based on those emotions. For example, if the user is excited, the customization department's AI detects their emotion and provides a visually stimulating customization. Specifically, for excited users, it uses vibrant colors and dynamic effects to enhance the video's visual impact. Furthermore, if the user is relaxed, the customization department uses calm colors and gentle effects to provide a relaxed atmosphere. Additionally, if the user is tense, the AI ​​detects their emotion and provides a gentle customization to alleviate tension. This allows users to enjoy the video with peace of mind. Through these functions, the customization department can achieve flexible customization tailored to the user's emotions, providing more satisfying videos.

[0035] The data collection unit can collect video from different angles using multiple cameras. For example, the data collection unit can use multiple cameras to simultaneously collect footage of different events at a sports day, and the AI ​​will integrate the footage in real time. The data collection unit can also collect footage from different angles, and the AI ​​can automatically select and integrate the optimal viewpoint. Furthermore, the data collection unit can analyze the footage from each camera in real time and integrate the footage to highlight important scenes. This allows for the collection of footage from different angles by using multiple cameras. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0036] The analysis unit can recognize the movements of each child in a video and generate individual videos. The analysis unit recognizes movements based on, for example, the algorithm used and the accuracy of the analysis. The analysis unit can also use, for example, AI to analyze the movements of each child in real time and generate individual videos. Furthermore, the analysis unit can optimize the analysis results by considering background and environmental information when analyzing the video. For example, the analysis unit optimizes the analysis results by considering the background color and brightness. This allows for the generation of individual videos by recognizing the movements of each child. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the recognition of each child's movements and the generation of individual videos.

[0037] The generation unit can track each child's running in a footrace scene and create a separate video for each child. The generation unit can also use AI, for example, to track each child's running in real time and generate individual videos. Furthermore, the generation unit can generate videos to emphasize specific actions or facial expressions. For example, the generation unit can use AI to track each child's running and generate videos to emphasize specific actions. This allows for the creation of individual videos for each child by tracking their running in a footrace scene. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input video data of the footrace scene into a generation AI and have the generation AI track each child's running and generate videos.

[0038] The customization unit can perform customizations such as highlighting specific scenes or prioritizing the display of videos of specific children. For example, the customization unit can use AI to highlight specific scenes or prioritize the display of videos of specific children based on the user's preferences. Furthermore, the customization unit can estimate the user's emotions and adjust the customization content based on those emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a visually stimulating customization. This allows for customization that meets the user's preferences by highlighting specific scenes or videos of specific children. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input user preference data into a generating AI and have the generating AI perform the highlighting of specific scenes or prioritize the display of videos of specific children.

[0039] The data collection unit can collect video from different angles using multiple cameras and integrate the footage from each camera in real time. For example, the data collection unit can use multiple cameras to simultaneously collect footage of different events at a sports day, and the AI ​​will integrate the footage in real time. The data collection unit can also collect footage from different angles, and the AI ​​can automatically select and integrate the optimal viewpoint. Furthermore, the data collection unit can analyze the footage from each camera in real time and integrate the footage to highlight important scenes. This allows for the provision of footage from the optimal viewpoint by integrating the footage from multiple cameras in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0040] The collection unit can automatically detect specific events or actions during collection and determine the range of video to be collected. For example, the AI ​​can detect the start signal of a sports day and begin collecting video from that moment. The collection unit can also have the AI ​​detect the movements of a specific child and determine the video collection range based on those movements. Furthermore, the collection unit can have the AI ​​detect audience reactions and prioritize collecting exciting scenes. This ensures that important scenes are not missed by automatically detecting specific events or actions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input event or action detection data into a generating AI and have the generating AI determine the collection range.

[0041] The collection unit can prioritize collecting videos that are highly relevant, taking into account the user's geographical location information during collection. For example, if the user is in a specific location, the collection unit will prioritize collecting videos related to that location. Furthermore, if the user is on the move, the collection unit can prioritize collecting videos of events near their current location. Additionally, if the user is in a specific area, the collection unit can prioritize collecting videos related to that area. This allows for the priority collection of highly relevant videos by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant videos.

[0042] The data collection unit can analyze a user's social media activity during collection and collect relevant videos. For example, if a user posts about a specific event on social media, the data collection unit can collect videos related to that event. It can also collect videos related to a specific hashtag if the user uses that hashtag on social media. Furthermore, if a user checks in to a specific location on social media, the data collection unit can collect videos related to that location. This allows for the efficient collection of relevant videos by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant videos.

[0043] The analysis unit can recognize the movements of each child in the video and perform analysis to emphasize specific actions or facial expressions. For example, the analysis unit can use AI to analyze each child's movements in real time and emphasize specific actions or facial expressions. For example, the analysis unit can use AI to recognize each child's running and analyze it to emphasize specific actions. The analysis unit can also use AI to recognize each child's smile and analyze it to emphasize specific facial expressions. Furthermore, the analysis unit can use AI to recognize each child's jumping and analyze it to emphasize specific actions. This allows for the creation of more engaging videos by emphasizing each child's actions and facial expressions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data into a generating AI and have the generating AI perform the emphasis of specific actions or facial expressions.

[0044] The analysis unit can optimize the analysis results by considering background and environmental information when analyzing a video. For example, the analysis unit can use AI to optimize the analysis results by considering the background color and brightness. The analysis unit can also use AI to optimize the analysis results by considering ambient sounds. Furthermore, the analysis unit can use AI to optimize the analysis results by considering weather information. This improves the accuracy of the analysis results by considering background and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input background and environmental information data into a generating AI and have the generating AI perform the optimization of the analysis results.

[0045] The analysis unit can optimize its analysis algorithm by referring to the user's past video viewing history when analyzing a video. For example, the analysis unit can analyze the trends of videos the user has watched in the past and optimize the analysis algorithm. The analysis unit can also optimize the analysis algorithm based on the user's evaluation of videos they have watched in the past. Furthermore, the analysis unit can optimize the analysis algorithm by considering the viewing time of videos the user has watched in the past. This improves the accuracy of the analysis algorithm by referring to the user's past video viewing history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past viewing history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0046] The analysis unit can improve the accuracy of its analysis by referencing relevant external data when analyzing video. For example, the analysis unit can improve the accuracy of its analysis by having the AI ​​refer to weather data. Furthermore, the analysis unit can improve the accuracy of its analysis by having the AI ​​refer to traffic data. In addition, the analysis unit can improve the accuracy of its analysis by having the AI ​​refer to event data. Thus, the accuracy of the analysis is improved by referencing relevant external data. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input external data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The generation unit can track each child's running in a footrace scene and create individual videos for each child, emphasizing specific actions or facial expressions. The generation unit can, for example, use AI to track each child's running in real time and emphasize specific actions or facial expressions. For instance, the AI ​​can track each child's running and generate videos to emphasize specific actions. The generation unit can also use AI to track each child's smile and generate videos to emphasize specific expressions. Furthermore, the AI ​​can track each child's jumping and generate videos to emphasize specific actions. This allows for the creation of more engaging videos by emphasizing each child's actions and facial expressions. Some or all of the above processing in the generation unit may be performed using AI, or without AI. For example, the generation unit can input video data of a footrace scene into a generation AI and have the generation AI perform the emphasis of specific actions or facial expressions.

[0048] The generation unit can adjust the video length and frame rate during generation to provide an optimal viewing experience. For example, the generation unit can use AI to adjust the video length to provide an optimal viewing experience that keeps viewers engaged. The generation unit can also use AI to adjust the frame rate to provide smooth video. Furthermore, the generation unit can use AI to adjust both the video length and frame rate simultaneously to provide an optimal viewing experience. This allows for the provision of an optimal viewing experience by adjusting the video length and frame rate. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input video data into a generation AI and have the generation AI perform adjustments to the video length and frame rate.

[0049] The generation unit can optimize its generation algorithm by referring to the user's past video viewing history during generation. For example, the generation unit can analyze the trends of videos the user has watched in the past and optimize the generation algorithm. It can also optimize the generation algorithm based on the user's ratings of videos they have watched in the past. Furthermore, the generation unit can optimize the generation algorithm by considering the viewing time of videos the user has watched in the past. This improves the accuracy of the generation algorithm by referring to the user's past video viewing history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past viewing history data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0050] The generation unit can improve the accuracy of generation by referencing relevant external data during generation. For example, the generation unit can improve the accuracy of generation by having the AI ​​reference weather data. The generation unit can also improve the accuracy of generation by having the AI ​​reference traffic data. Furthermore, the generation unit can improve the accuracy of generation by having the AI ​​reference event data. In this way, the accuracy of generation is improved by referencing relevant external data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input external data into the generation AI and have the generation AI perform the generation accuracy improvement.

[0051] The reception desk can select the most suitable reception method by referring to the user's past request history when a request is made. For example, the reception desk can suggest the most suitable reception method based on the content of the user's past requests. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest a reception method to be used during a specific time period based on the user's past request history. In this way, the reception desk can suggest the most suitable reception method by referring to the user's past request history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past request history data into a generating AI and have the generating AI select the most suitable reception method.

[0052] The reception desk can filter requests based on the user's current situation and areas of interest. For example, the reception desk can prioritize requests related to events the user is currently interested in. It can also filter requests based on the user's current situation (e.g., being in the middle of a sports day). Furthermore, the reception desk can suggest the most suitable requests based on the user's areas of interest. This allows for priority reception of relevant requests by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0053] The reception desk can prioritize requests that are highly relevant to the user, taking into account the user's geographical location when receiving requests. For example, if the user is in a specific location, the reception desk will prioritize requests related to that location. Furthermore, if the user is on the move, the reception desk can prioritize requests close to the user's current location. Additionally, if the user is in a specific area, the reception desk can prioritize requests related to that area. This allows for the prioritization of highly relevant requests by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI process requests for highly relevant ones.

[0054] The reception desk can analyze a user's social media activity when a request is received and receive relevant requests. For example, if a user posts about a specific event on social media, the reception desk can receive requests related to that event. It can also receive requests related to a specific hashtag if the user uses one on social media. Furthermore, if a user checks in to a specific location on social media, the reception desk can receive requests related to that location. This allows for efficient reception of relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the reception of relevant requests.

[0055] The customization unit can refer to the user's past customization history when performing customizations such as highlighting specific scenes or prioritizing the display of videos of specific children. For example, the customization unit can prioritize the display of similar scenes based on scenes the user has previously highlighted. It can also prioritize the display of similar videos of children based on videos the user has previously prioritized. Furthermore, the customization unit can analyze the user's past customization history and propose the optimal customization. This allows it to propose the optimal customization by referring to the user's past customization history. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the user's past customization history data into a generating AI and have the generating AI execute a proposal for the optimal customization.

[0056] The customization unit can adjust the video length and frame rate during customization to provide an optimal viewing experience. For example, the customization unit can use AI to adjust the video length to provide an optimal viewing experience that keeps viewers engaged. The customization unit can also use AI to adjust the frame rate to provide smooth video. Furthermore, the customization unit can use AI to adjust both the video length and frame rate simultaneously to provide an optimal viewing experience. This allows for the provision of an optimal viewing experience by adjusting the video length and frame rate. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input video data into a generating AI and have the generating AI perform adjustments to the video length and frame rate.

[0057] The customization unit can optimize the customization algorithm by referring to the user's past video viewing history during the customization process. For example, the customization unit can analyze the trends of videos the user has watched in the past and optimize the customization algorithm. It can also optimize the customization algorithm based on the user's ratings of videos they have watched in the past. Furthermore, the customization unit can optimize the customization algorithm by considering the viewing time of videos the user has watched in the past. This improves the accuracy of the customization algorithm by referring to the user's past video viewing history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past viewing history data into a generating AI and have the generating AI perform the optimization of the customization algorithm.

[0058] The customization unit can improve the accuracy of customization by referencing relevant external data during the customization process. For example, the customization unit can improve the accuracy of customization by having the AI ​​reference weather data. Furthermore, the customization unit can improve the accuracy of customization by having the AI ​​reference traffic data. In addition, the customization unit can improve the accuracy of customization by having the AI ​​reference event data. Thus, the accuracy of customization is improved by referencing relevant external data. Some or all of the above-described processes in the customization unit may be performed using, for example, AI, or without AI. For example, the customization unit can input external data into a generating AI and have the generating AI perform the customization accuracy improvement.

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

[0060] The video generation system can also be equipped with a speech recognition unit. The speech recognition unit can analyze the audio in the collected videos and detect specific audio events (e.g., cheers and applause). For example, the speech recognition unit can detect the start signal and finish of a sports day event by sound and generate a video that highlights those moments. The speech recognition unit can also recognize the voice of a specific child and prioritize collecting scenes in which that child is speaking. Furthermore, the speech recognition unit can analyze audience reactions and customize the video to highlight exciting scenes. This allows for the creation of more immersive videos by utilizing audio information.

[0061] The video generation system can also be equipped with a face recognition unit. This unit can analyze faces in collected videos and recognize the faces of specific children. For example, it can track the faces of a specific child in a sports day scene and generate a video that emphasizes that child's expressions. Furthermore, the face recognition unit can simultaneously recognize the faces of multiple children and emphasize group activity scenes. In addition, the face recognition unit can analyze the faces of spectators and customize videos based on their reactions. This allows for the creation of more personalized videos by utilizing facial information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects videos of school events such as sports days. The collection unit can collect videos from different angles using, for example, a fixed camera or multiple cameras. The collection unit can also estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion in real time and adjust the timing of video collection to ensure that important moments are not missed. If the user is relaxed, video collection can start before important events occur, and if the user is nervous, video collection can be temporarily stopped to help alleviate the tension. Step 2: The analysis unit analyzes the video collected by the collection unit. The analysis unit can, for example, recognize the movements of each child in the video and generate individual videos. The analysis unit can also optimize the analysis results by recognizing movements based on the algorithm used and the accuracy of the analysis, and by taking background and environmental information into consideration. For example, it can optimize the analysis results by taking background color and brightness into consideration. Step 3: The generation unit generates individual videos for each child based on the data analyzed by the analysis unit. For example, the generation unit can track each child's running in a footrace scene and create a video for each child. The generation unit can also generate videos to emphasize specific actions or facial expressions. Step 4: The reception desk receives the user's requests. For example, the reception desk can receive requests from users using an app. The reception desk can also estimate the user's emotions and adjust the request reception method based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a simple and quick reception method. Step 5: The customization department customizes the video based on the requests received by the reception department. The customization department can, for example, highlight specific scenes or prioritize the display of footage of a particular child. The customization department can also estimate the user's emotions and adjust the customization based on those emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a visually stimulating customization.

[0064] (Example of form 2) The video generation system according to an embodiment of the present invention is a system that automatically generates videos of school events such as sports days. This video generation system films videos of school events such as sports days with a fixed camera, and an AI analyzes the footage to recognize the movements of each child and generates individual videos. Furthermore, users can customize the videos using an app, and by simply telling the AI ​​their preferences, they can emphasize specific scenes or prioritize the display of footage of specific children. For example, the video generation system films a sports day with a fixed camera. Next, the video generation system uses an AI to analyze the filmed video, recognize the movements of each child, and generate individual videos. Furthermore, the video generation system allows users to customize the videos using an app. For example, by simply telling the AI ​​their preferences, users can emphasize specific scenes or prioritize the display of footage of specific children. This system makes it easy to create and customize videos of school events such as sports days, which is extremely convenient for parents and other stakeholders.

[0065] The video generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a reception unit, and a customization unit. The collection unit collects videos of school events such as sports days. The collection unit can collect videos using, for example, a fixed camera. The collection unit can also collect videos from different angles using multiple cameras. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions. For example, if the user is excited, the AI ​​in the collection unit can detect emotions in real time and adjust the timing of video collection to avoid missing important moments. Also, if the user is relaxed, the AI ​​in the collection unit can detect emotions and start collecting videos before important events occur. Furthermore, if the user is nervous, the AI ​​in the collection unit can detect emotions and temporarily stop video collection to alleviate the tension. The analysis unit analyzes the videos collected by the collection unit. The analysis unit can, for example, recognize the movements of each child in the video and generate individual videos. The analysis unit recognizes movements based, for example, the algorithm used and the accuracy of the analysis. Furthermore, the analysis unit can optimize the analysis results by considering background and environmental information when analyzing videos. For example, the analysis unit can optimize the analysis results by considering the background color and brightness. The generation unit generates individual videos for each child based on the data analyzed by the analysis unit. For example, the generation unit can track each child's running in a footrace scene and create a video for each child. The generation unit can also generate videos to emphasize specific actions or facial expressions. The reception unit receives user requests. For example, the reception unit allows users to input their requests using the app. The reception unit can also estimate the user's emotions and adjust the method of receiving requests based on the estimated emotions. For example, if the user is excited, the reception unit's AI can detect their emotions and provide a simple and quick method of receiving requests. The customization unit customizes the videos based on the requests received by the reception unit. For example, the customization unit can perform customizations such as emphasizing specific scenes or prioritizing the display of videos of specific children.The customization unit can also estimate the user's emotions and adjust the customization content based on those emotions. For example, if the user is excited, the customization unit's AI can detect the emotion and provide a visually stimulating customization. This allows the video generation system according to the embodiment to easily create and customize videos of school events such as sports days.

[0066] The collection unit collects videos of school events such as sports days. The collection unit can, for example, collect videos using a fixed camera. It can also collect videos from different angles using multiple cameras. Specifically, a fixed camera is positioned to capture an overall view of the sports field, collecting wide-angle footage. Meanwhile, multiple cameras capture key points in each competition or the spectator stands from different angles. This allows the collection unit to cover both the overall picture and detailed scenes of the event. Furthermore, the collection unit can estimate the user's emotions and adjust the video collection timing based on the estimated emotions. For example, if the user is excited, the AI ​​can detect their emotions in real time and adjust the video collection timing to ensure important moments are not missed. The AI ​​analyzes biometric data such as the user's facial expressions, tone of voice, and heart rate to detect their state of excitement. This ensures the collection unit captures moments that are of particular interest to the user. Conversely, if the user is relaxed, the AI ​​can detect their emotions and begin video collection before important events occur. For example, to ensure that a relaxed user doesn't miss a particular event, the AI ​​can start collecting footage in advance, reliably recording important scenes. Furthermore, if the user is feeling tense, the AI ​​can detect their emotions and temporarily stop collecting video to alleviate the tension. This allows the user to avoid scenes that cause tension and enjoy the video in a relaxed state. By combining these functions, the collection unit achieves flexible video collection that responds to the user's emotions, supporting the creation of more satisfying videos.

[0067] The analysis unit analyzes the videos collected by the collection unit. For example, the analysis unit can recognize the movements of each child in the video and generate individual videos. Specifically, the analysis unit utilizes deep learning-based image recognition technology to detect the facial and body features of each child. This makes it possible to accurately track the movements of each child and generate individual videos. The analysis unit recognizes movement based on the algorithm used and the accuracy of the analysis. For example, it can use a convolutional neural network (CNN) to extract features from each frame in the video and a recurrent neural network (RNN) to analyze the temporal movement patterns. The analysis unit can also optimize the analysis results by considering background and environmental information when analyzing the video. For example, the analysis unit optimizes the analysis results by considering the color and brightness of the background. Specifically, it performs appropriate filtering and correction depending on whether the background is bright or dark to improve the accuracy of the analysis. Furthermore, the analysis unit also analyzes audio data and can identify specific scenes and events using speech recognition technology. For example, it can detect the starting signal for a foot race or the cheers of the crowd and identify important scenes in the video based on these audio events. This allows the analysis unit to analyze the collected data from multiple angles and support the generation of more accurate individual videos.

[0068] The generation unit generates individual videos for each child based on data analyzed by the analysis unit. For example, the generation unit can track each child's running in a footrace scene and create a video for each child. Specifically, the generation unit tracks each child's movements based on tracking data provided by the analysis unit and generates individual video clips. The generation unit can also generate videos to emphasize specific actions or expressions. For example, it can apply effects such as slow motion or zoom-in to emphasize particularly moving scenes, such as the finish line of a footrace or the moment of the awards ceremony. Furthermore, the generation unit can enhance the emotional impact of the video by adding music and narration. For example, it can create a greater sense of realism by selecting an emotional song as background music and introducing each child's name and the results of the competition with narration. The generation unit can also customize the style and theme of the video according to the user's preferences. For example, it can adjust the color tone and effects of the video based on the theme desired by the user to generate a video with a specific atmosphere. In this way, the generation unit can generate a variety of videos that meet the user's needs and record individual memories more vividly.

[0069] The reception desk receives user requests. For example, users can input their requests using an app. Specifically, users can use a smartphone or tablet to input their desired video content and style through a dedicated application. The reception desk can also estimate the user's emotions and adjust the request processing method based on that estimation. For example, if a user is excited, the AI ​​can detect their emotions and provide a simple and quick request processing method. The AI ​​analyzes the user's facial expressions, tone of voice, and input speed to detect their level of excitement. This allows the reception desk to provide a concise interface and auto-completion features to enable users to input their requests quickly. Furthermore, if a user is relaxed, the reception desk offers detailed customization options, allowing them to input their requests at their leisure. For example, relaxed users are offered detailed customization options such as color scheme, effects, and music selection to help them create a video they are satisfied with. Additionally, if a user is tense, the AI ​​can detect their emotions and provide guidance and support to alleviate their tension. This creates an environment where users can input their requests with confidence. Through these functions, the reception department provides flexible reception services that respond to the user's emotions, resulting in a more satisfying service.

[0070] The customization department customizes videos based on requests received by the reception department. For example, the customization department can highlight specific scenes or prioritize the display of footage of specific children. Specifically, the customization department edits the video to prioritize scenes or footage of children requested by the user, and applies effects and transitions to highlight specific moments. The customization department can also estimate the user's emotions and adjust the customization based on those emotions. For example, if the user is excited, the customization department's AI detects their emotion and provides a visually stimulating customization. Specifically, for excited users, it uses vibrant colors and dynamic effects to enhance the video's visual impact. Furthermore, if the user is relaxed, the customization department uses calm colors and gentle effects to provide a relaxed atmosphere. Additionally, if the user is tense, the AI ​​detects their emotion and provides a gentle customization to alleviate tension. This allows users to enjoy the video with peace of mind. Through these functions, the customization department can achieve flexible customization tailored to the user's emotions, providing more satisfying videos.

[0071] The data collection unit can collect video from different angles using multiple cameras. For example, the data collection unit can use multiple cameras to simultaneously collect footage of different events at a sports day, and the AI ​​will integrate the footage in real time. The data collection unit can also collect footage from different angles, and the AI ​​can automatically select and integrate the optimal viewpoint. Furthermore, the data collection unit can analyze the footage from each camera in real time and integrate the footage to highlight important scenes. This allows for the collection of footage from different angles by using multiple cameras. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0072] The analysis unit can recognize the movements of each child in a video and generate individual videos. The analysis unit recognizes movements based on, for example, the algorithm used and the accuracy of the analysis. The analysis unit can also use, for example, AI to analyze the movements of each child in real time and generate individual videos. Furthermore, the analysis unit can optimize the analysis results by considering background and environmental information when analyzing the video. For example, the analysis unit optimizes the analysis results by considering the background color and brightness. This allows for the generation of individual videos by recognizing the movements of each child. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input video data into a generation AI and have the generation AI perform the recognition of each child's movements and the generation of individual videos.

[0073] The generation unit can track each child's running in a footrace scene and create a separate video for each child. The generation unit can also use AI, for example, to track each child's running in real time and generate individual videos. Furthermore, the generation unit can generate videos to emphasize specific actions or facial expressions. For example, the generation unit can use AI to track each child's running and generate videos to emphasize specific actions. This allows for the creation of individual videos for each child by tracking their running in a footrace scene. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input video data of the footrace scene into a generation AI and have the generation AI track each child's running and generate videos.

[0074] The customization unit can perform customizations such as highlighting specific scenes or prioritizing the display of videos of specific children. For example, the customization unit can use AI to highlight specific scenes or prioritize the display of videos of specific children based on the user's preferences. Furthermore, the customization unit can estimate the user's emotions and adjust the customization content based on those emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a visually stimulating customization. This allows for customization that meets the user's preferences by highlighting specific scenes or videos of specific children. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input user preference data into a generating AI and have the generating AI perform the highlighting of specific scenes or prioritize the display of videos of specific children.

[0075] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion in real time and adjust the timing of video collection to ensure that important moments are not missed. The collection unit can also detect the emotion if the user is relaxed and start collecting video before important events occur. Furthermore, if the user is tense, the AI ​​can detect the emotion and temporarily stop video collection to alleviate the tension. This ensures that important moments are not missed by adjusting the timing of video collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of collection timing.

[0076] The data collection unit can collect video from different angles using multiple cameras and integrate the footage from each camera in real time. For example, the data collection unit can use multiple cameras to simultaneously collect footage of different events at a sports day, and the AI ​​will integrate the footage in real time. The data collection unit can also collect footage from different angles, and the AI ​​can automatically select and integrate the optimal viewpoint. Furthermore, the data collection unit can analyze the footage from each camera in real time and integrate the footage to highlight important scenes. This allows for the provision of footage from the optimal viewpoint by integrating the footage from multiple cameras in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired from multiple cameras into a generating AI and have the generating AI perform the video integration.

[0077] The collection unit can automatically detect specific events or actions during collection and determine the range of video to be collected. For example, the AI ​​can detect the start signal of a sports day and begin collecting video from that moment. The collection unit can also have the AI ​​detect the movements of a specific child and determine the video collection range based on those movements. Furthermore, the collection unit can have the AI ​​detect audience reactions and prioritize collecting exciting scenes. This ensures that important scenes are not missed by automatically detecting specific events or actions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input event or action detection data into a generating AI and have the generating AI determine the collection range.

[0078] The collection unit can estimate the user's emotions and determine the priority of videos to collect based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion and prioritize collecting scenes that cause excitement. Similarly, if the user is relaxed, the AI ​​can detect the emotion and prioritize collecting scenes that promote relaxation. Furthermore, if the user is tense, the AI ​​can detect the emotion and prioritize collecting scenes that alleviate tension. This allows for the priority collection of important scenes by determining the priority of videos based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine collection priorities.

[0079] The collection unit can prioritize collecting videos that are highly relevant, taking into account the user's geographical location information during collection. For example, if the user is in a specific location, the collection unit will prioritize collecting videos related to that location. Furthermore, if the user is on the move, the collection unit can prioritize collecting videos of events near their current location. Additionally, if the user is in a specific area, the collection unit can prioritize collecting videos related to that area. This allows for the priority collection of highly relevant videos by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant videos.

[0080] The data collection unit can analyze a user's social media activity during collection and collect relevant videos. For example, if a user posts about a specific event on social media, the data collection unit can collect videos related to that event. It can also collect videos related to a specific hashtag if the user uses that hashtag on social media. Furthermore, if a user checks in to a specific location on social media, the data collection unit can collect videos related to that location. This allows for the efficient collection of relevant videos by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant videos.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion and improve the accuracy of the analysis to highlight important scenes. The analysis unit can also detect the emotion if the user is relaxed and adjust the accuracy to analyze the overall scene. Furthermore, if the user is tense, the AI ​​can detect the emotion and adjust the accuracy to analyze scenes that alleviate the tension. This allows for the highlighting of important scenes by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of analysis accuracy.

[0082] The analysis unit can recognize the movements of each child in the video and perform analysis to emphasize specific actions or facial expressions. For example, the analysis unit can use AI to analyze each child's movements in real time and emphasize specific actions or facial expressions. For example, the analysis unit can use AI to recognize each child's running and analyze it to emphasize specific actions. The analysis unit can also use AI to recognize each child's smile and analyze it to emphasize specific facial expressions. Furthermore, the analysis unit can use AI to recognize each child's jumping and analyze it to emphasize specific actions. This allows for the creation of more engaging videos by emphasizing each child's actions and facial expressions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data into a generating AI and have the generating AI perform the emphasis of specific actions or facial expressions.

[0083] The analysis unit can optimize the analysis results by considering background and environmental information when analyzing a video. For example, the analysis unit can use AI to optimize the analysis results by considering the background color and brightness. The analysis unit can also use AI to optimize the analysis results by considering ambient sounds. Furthermore, the analysis unit can use AI to optimize the analysis results by considering weather information. This improves the accuracy of the analysis results by considering background and environmental information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input background and environmental information data into a generating AI and have the generating AI perform the optimization of the analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a visually stimulating display method. The analysis unit can also detect the emotion if the user is relaxed and provide a calm display method. Furthermore, if the user is tense, the AI ​​can detect the emotion and provide a simple and highly visible display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, a visually stimulating display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0085] The analysis unit can optimize its analysis algorithm by referring to the user's past video viewing history when analyzing a video. For example, the analysis unit can analyze the trends of videos the user has watched in the past and optimize the analysis algorithm. The analysis unit can also optimize the analysis algorithm based on the user's evaluation of videos they have watched in the past. Furthermore, the analysis unit can optimize the analysis algorithm by considering the viewing time of videos the user has watched in the past. This improves the accuracy of the analysis algorithm by referring to the user's past video viewing history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past viewing history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0086] The analysis unit can improve the accuracy of its analysis by referencing relevant external data when analyzing video. For example, the analysis unit can improve the accuracy of its analysis by having the AI ​​refer to weather data. Furthermore, the analysis unit can improve the accuracy of its analysis by having the AI ​​refer to traffic data. In addition, the analysis unit can improve the accuracy of its analysis by having the AI ​​refer to event data. Thus, the accuracy of the analysis is improved by referencing relevant external data. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input external data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0087] The generation unit can estimate the user's emotions and adjust the content of the generated video based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion and generate a visually stimulating video. The generation unit can also detect the emotion if the user is relaxed and generate a calming video. Furthermore, if the user is tense, the AI ​​can detect the emotion and generate a simple, easy-to-understand video. This allows for the generation of visually stimulating videos by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and video content adjustment.

[0088] The generation unit can track each child's running in a footrace scene and create individual videos for each child, emphasizing specific actions or facial expressions. The generation unit can, for example, use AI to track each child's running in real time and emphasize specific actions or facial expressions. For instance, the AI ​​can track each child's running and generate videos to emphasize specific actions. The generation unit can also use AI to track each child's smile and generate videos to emphasize specific expressions. Furthermore, the AI ​​can track each child's jumping and generate videos to emphasize specific actions. This allows for the creation of more engaging videos by emphasizing each child's actions and facial expressions. Some or all of the above processing in the generation unit may be performed using AI, or without AI. For example, the generation unit can input video data of a footrace scene into a generation AI and have the generation AI perform the emphasis of specific actions or facial expressions.

[0089] The generation unit can adjust the video length and frame rate during generation to provide an optimal viewing experience. For example, the generation unit can use AI to adjust the video length to provide an optimal viewing experience that keeps viewers engaged. The generation unit can also use AI to adjust the frame rate to provide smooth video. Furthermore, the generation unit can use AI to adjust both the video length and frame rate simultaneously to provide an optimal viewing experience. This allows for the provision of an optimal viewing experience by adjusting the video length and frame rate. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input video data into a generation AI and have the generation AI perform adjustments to the video length and frame rate.

[0090] The generation unit can estimate the user's emotions and adjust the order of the generated videos based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion and place visually stimulating scenes first. Similarly, if the user is relaxed, the AI ​​can detect the emotion and place calming scenes first. Furthermore, if the user is tense, the AI ​​can detect the emotion and place simple, highly visible scenes first. This allows for the placement of visually stimulating scenes first by adjusting the video order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and video order adjustment.

[0091] The generation unit can optimize its generation algorithm by referring to the user's past video viewing history during generation. For example, the generation unit can analyze the trends of videos the user has watched in the past and optimize the generation algorithm. It can also optimize the generation algorithm based on the user's ratings of videos they have watched in the past. Furthermore, the generation unit can optimize the generation algorithm by considering the viewing time of videos the user has watched in the past. This improves the accuracy of the generation algorithm by referring to the user's past video viewing history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past viewing history data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0092] The generation unit can improve the accuracy of generation by referencing relevant external data during generation. For example, the generation unit can improve the accuracy of generation by having the AI ​​reference weather data. The generation unit can also improve the accuracy of generation by having the AI ​​reference traffic data. Furthermore, the generation unit can improve the accuracy of generation by having the AI ​​reference event data. In this way, the accuracy of generation is improved by referencing relevant external data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input external data into the generation AI and have the generation AI perform the generation accuracy improvement.

[0093] The reception desk can estimate the user's emotions and adjust the preferred reception method based on the estimated emotions. For example, if the user is excited, the reception desk can use AI to detect the emotion and provide a simple and quick reception method. Alternatively, if the user is relaxed, the reception desk can use AI to detect the emotion and provide a reception method with more detailed options. Furthermore, if the user is nervous, the reception desk can use AI to detect the emotion and provide a calming interface. This allows for a simple and quick reception method by adjusting the preferred reception method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation and reception method adjustment.

[0094] The reception desk can select the most suitable reception method by referring to the user's past request history when a request is made. For example, the reception desk can suggest the most suitable reception method based on the content of the user's past requests. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest a reception method to be used during a specific time period based on the user's past request history. In this way, the reception desk can suggest the most suitable reception method by referring to the user's past request history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past request history data into a generating AI and have the generating AI select the most suitable reception method.

[0095] The reception desk can filter requests based on the user's current situation and areas of interest. For example, the reception desk can prioritize requests related to events the user is currently interested in. It can also filter requests based on the user's current situation (e.g., being in the middle of a sports day). Furthermore, the reception desk can suggest the most suitable requests based on the user's areas of interest. This allows for priority reception of relevant requests by filtering based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's current situation and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0096] The reception desk can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is excited, the reception desk's AI can detect the emotion and prioritize requests that cause excitement. Similarly, if the user is relaxed, the reception desk's AI can detect the emotion and prioritize requests that promote relaxation. Furthermore, if the user is tense, the reception desk's AI can detect the emotion and prioritize requests that alleviate tension. This allows for prioritizing important requests based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation and priority determination of requests.

[0097] The reception desk can prioritize requests that are highly relevant to the user, taking into account the user's geographical location when receiving requests. For example, if the user is in a specific location, the reception desk will prioritize requests related to that location. Furthermore, if the user is on the move, the reception desk can prioritize requests close to the user's current location. Additionally, if the user is in a specific area, the reception desk can prioritize requests related to that area. This allows for the prioritization of highly relevant requests by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI process requests for highly relevant ones.

[0098] The reception desk can analyze a user's social media activity when a request is received and receive relevant requests. For example, if a user posts about a specific event on social media, the reception desk can receive requests related to that event. It can also receive requests related to a specific hashtag if the user uses one on social media. Furthermore, if a user checks in to a specific location on social media, the reception desk can receive requests related to that location. This allows for efficient reception of relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the reception of relevant requests.

[0099] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated emotions. For example, if the user is excited, the customization unit's AI can detect the emotion and provide a visually stimulating customization. Similarly, if the user is relaxed, the customization unit's AI can detect the emotion and provide a calming customization. Furthermore, if the user is tense, the customization unit's AI can detect the emotion and provide a simple and highly visible customization. This allows for the provision of visually stimulating customization by adjusting the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and customization content adjustment.

[0100] The customization unit can refer to the user's past customization history when performing customizations such as highlighting specific scenes or prioritizing the display of videos of specific children. For example, the customization unit can prioritize the display of similar scenes based on scenes the user has previously highlighted. It can also prioritize the display of similar videos of children based on videos the user has previously prioritized. Furthermore, the customization unit can analyze the user's past customization history and propose the optimal customization. This allows it to propose the optimal customization by referring to the user's past customization history. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the user's past customization history data into a generating AI and have the generating AI execute a proposal for the optimal customization.

[0101] The customization unit can adjust the video length and frame rate during customization to provide an optimal viewing experience. For example, the customization unit can use AI to adjust the video length to provide an optimal viewing experience that keeps viewers engaged. The customization unit can also use AI to adjust the frame rate to provide smooth video. Furthermore, the customization unit can use AI to adjust both the video length and frame rate simultaneously to provide an optimal viewing experience. This allows for the provision of an optimal viewing experience by adjusting the video length and frame rate. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input video data into a generating AI and have the generating AI perform adjustments to the video length and frame rate.

[0102] The customization unit can estimate the user's emotions and adjust the customization order based on the estimated emotions. For example, if the user is excited, the customization unit's AI can detect the emotion and place visually stimulating scenes first. Similarly, if the user is relaxed, the customization unit's AI can detect the emotion and place calming scenes first. Furthermore, if the user is tense, the customization unit's AI can detect the emotion and place simple, highly visible scenes first. This allows for the placement of visually stimulating scenes first by adjusting the customization order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the customization order.

[0103] The customization unit can optimize the customization algorithm by referring to the user's past video viewing history during the customization process. For example, the customization unit can analyze the trends of videos the user has watched in the past and optimize the customization algorithm. It can also optimize the customization algorithm based on the user's ratings of videos they have watched in the past. Furthermore, the customization unit can optimize the customization algorithm by considering the viewing time of videos the user has watched in the past. This improves the accuracy of the customization algorithm by referring to the user's past video viewing history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past viewing history data into a generating AI and have the generating AI perform the optimization of the customization algorithm.

[0104] The customization unit can improve the accuracy of customization by referencing relevant external data during the customization process. For example, the customization unit can improve the accuracy of customization by having the AI ​​reference weather data. Furthermore, the customization unit can improve the accuracy of customization by having the AI ​​reference traffic data. In addition, the customization unit can improve the accuracy of customization by having the AI ​​reference event data. Thus, the accuracy of customization is improved by referencing relevant external data. Some or all of the above-described processes in the customization unit may be performed using, for example, AI, or without AI. For example, the customization unit can input external data into a generating AI and have the generating AI perform the customization accuracy improvement.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The video generation system can also be equipped with a speech recognition unit. The speech recognition unit can analyze the audio in the collected videos and detect specific audio events (e.g., cheers and applause). For example, the speech recognition unit can detect the start signal and finish of a sports day event by sound and generate a video that highlights those moments. The speech recognition unit can also recognize the voice of a specific child and prioritize collecting scenes in which that child is speaking. Furthermore, the speech recognition unit can analyze audience reactions and customize the video to highlight exciting scenes. This allows for the creation of more immersive videos by utilizing audio information.

[0107] The video generation system can also be equipped with a face recognition unit. This unit can analyze faces in collected videos and recognize the faces of specific children. For example, it can track the faces of a specific child in a sports day scene and generate a video that emphasizes that child's expressions. Furthermore, the face recognition unit can simultaneously recognize the faces of multiple children and emphasize group activity scenes. In addition, the face recognition unit can analyze the faces of spectators and customize videos based on their reactions. This allows for the creation of more personalized videos by utilizing facial information.

[0108] The video generation system may also include a music selection unit. This unit can select appropriate music based on the content of the collected videos and insert it into the videos. For example, it can select upbeat music to match a sports day scene and insert it into the video. It can also adjust the rhythm to match the movements of a specific child, enhancing the overall sense of unity in the video. Furthermore, the music selection unit can estimate the user's emotions and adjust the music genre and tempo based on those emotions. This allows for the creation of more emotionally impactful videos by utilizing music.

[0109] The video generation system may also include a text insertion unit. This unit can insert appropriate text into the collected videos. For example, in a sports day scene, the text insertion unit can display the names of each event and the names of the participants. It can also display cheering messages and comments in specific scenes. Furthermore, the text insertion unit can estimate the user's emotions and adjust the text content and display method based on the estimated emotions. This allows for the generation of more informative videos by utilizing text information.

[0110] The video generation system may also include an effects application unit. This unit can apply visual effects to the collected videos. For example, it can apply effects such as slow motion and zoom-in to a scene from a sports day. Furthermore, it can adjust effects to match the movements of a specific child, enhancing the visual appeal of the video. Additionally, the effects application unit can estimate the user's emotions and adjust the type and intensity of effects based on those emotions. This allows for the creation of more engaging videos by utilizing effects.

[0111] The video generation system can also be equipped with a data storage unit. This unit can efficiently store collected video data and analysis results. For example, the data storage unit can categorize data by scene from a sports day, making it easy to search later. It can also prioritize saving videos of specific children, allowing for quick access when needed. Furthermore, the data storage unit can estimate user emotions and adjust data storage methods based on these estimated emotions. This streamlines data storage and management, enabling quick access to necessary video data.

[0112] The video generation system can also be equipped with a real-time streaming unit. This unit can distribute collected videos in real time. For example, it can live stream scenes from a school sports day, allowing parents and other attendees in remote locations to watch in real time. It can also prioritize the distribution of scenes featuring specific children, ensuring viewers don't miss anything of interest. Furthermore, the real-time streaming unit can estimate user emotions and adjust the content based on those emotions. This enhances the real-time viewing experience, allowing more people to enjoy the event.

[0113] The video generation system can also be equipped with a data compression unit. This unit can efficiently compress the collected video data, improving the efficiency of storage and distribution. For example, the unit can compress data scene by scene from a sports day, saving storage space. It can also prioritize the compression of videos of specific children, making them readily accessible when needed. Furthermore, the unit can estimate the user's emotions and adjust the compression method based on those emotions. This streamlines data compression and management, allowing for quick access to the necessary video data.

[0114] The video generation system can also include a user feedback unit. This unit can collect user feedback on the generated videos and use it to improve the system. For example, it can collect ratings and comments on videos of school sports days and incorporate them into future video generation. It can also collect feedback on specific scenes or children to help customize the videos. Furthermore, the user feedback unit can estimate user emotions and adjust the feedback collection method based on those estimated emotions. This allows for the generation of better videos that reflect user opinions.

[0115] The video generation system can also be equipped with a data analysis unit. This unit can analyze collected video data and user feedback to improve the system. For example, it can analyze the number of views and ratings for each scene in a sports day video to identify popular scenes. It can also analyze feedback on videos of specific children and incorporate that feedback into future video generation. Furthermore, the data analysis unit can estimate user emotions and adjust the data analysis method based on these estimated emotions. This streamlines data analysis and management, contributing to system improvement.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The collection unit collects videos of school events such as sports days. The collection unit can collect videos from different angles using, for example, a fixed camera or multiple cameras. The collection unit can also estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion in real time and adjust the timing of video collection to ensure that important moments are not missed. If the user is relaxed, video collection can start before important events occur, and if the user is nervous, video collection can be temporarily stopped to help alleviate the tension. Step 2: The analysis unit analyzes the video collected by the collection unit. The analysis unit can, for example, recognize the movements of each child in the video and generate individual videos. The analysis unit can also optimize the analysis results by recognizing movements based on the algorithm used and the accuracy of the analysis, and by taking background and environmental information into consideration. For example, it can optimize the analysis results by taking background color and brightness into consideration. Step 3: The generation unit generates individual videos for each child based on the data analyzed by the analysis unit. For example, the generation unit can track each child's running in a footrace scene and create a video for each child. The generation unit can also generate videos to emphasize specific actions or facial expressions. Step 4: The reception desk receives the user's requests. For example, the reception desk can receive requests from users using an app. The reception desk can also estimate the user's emotions and adjust the request reception method based on the estimated emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a simple and quick reception method. Step 5: The customization department customizes the video based on the requests received by the reception department. The customization department can, for example, highlight specific scenes or prioritize the display of footage of a particular child. The customization department can also estimate the user's emotions and adjust the customization based on those emotions. For example, if the user is excited, the AI ​​can detect the emotion and provide a visually stimulating customization.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0120] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reception unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to collect videos of the sports day and the specific processing unit 290 of the data processing unit 12 analyzes the collected videos. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and recognizes the movements of each child to generate individual videos. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates videos for each child based on the analyzed data. The reception unit is implemented in the specific processing unit 46A of the smart device 14 and receives the user's requests. The customization unit is implemented in the specific processing unit 290 of the data processing unit 12 and customizes the videos based on the requests received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reception unit, and customization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to collect video of the sports day and the specific processing unit 290 of the data processing unit 12 analyzes the collected video. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and recognizes the movements of each child to generate individual videos. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates videos for each child based on the analyzed data. The reception unit is implemented, for example, in the control unit 46A of the smart glasses 214, and receives the user's requests. The customization unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and customizes the videos based on the requests received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reception unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects video of the sports day using the camera 42 of the headset terminal 314 and analyzes the collected video using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and recognizes the movements of each child to generate individual videos. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates videos for each child based on the analyzed data. The reception unit is implemented in the control unit 46A of the headset terminal 314 and receives the user's requests. The customization unit is implemented in the specific processing unit 290 of the data processing unit 12 and customizes the videos based on the requests received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0162] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, reception unit, and customization unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to collect video of the sports day and the specific processing unit 290 of the data processing unit 12 analyzes the collected video. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 to recognize the movements of each child and generate individual videos. The generation unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 to generate videos for each child based on the analyzed data. The reception unit is implemented in, for example, the control unit 46A of the robot 414 to receive user requests. The customization unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 to customize the videos based on the requests received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0173] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0174] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0175] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0179] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0180] 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.

[0181] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0182] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0183] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0184] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0186] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0187] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0188] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0189] (Note 1) The collection department collects videos of school events such as sports days, An analysis unit analyzes the video collected by the aforementioned collection unit, A generation unit generates a video for each child based on the data analyzed by the analysis unit, A reception desk that takes user requests, The system includes a customization unit that customizes the video based on the requests received by the reception unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect video from different angles using multiple cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system recognizes the movements of each child in the video and generates individual videos for each child. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is In a foot race scene, track each child's running and create a video of each child individually. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Customize the display to highlight specific scenes or prioritize the display of videos of specific children. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Multiple cameras are used to collect video from different angles, and the footage from each camera is integrated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During collection, the system automatically detects specific events or actions to determine the scope of the video to be collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting videos that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system recognizes the movements of each child in the video and performs analysis to highlight specific actions and facial expressions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing videos, the analysis results are optimized by taking background and environmental information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing videos, the analysis algorithm is optimized by referring to the user's past video viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing videos, we improve the accuracy of the analysis by referencing relevant external data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the content of the generated video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When tracking each child's running in a footrace scene and creating individual videos for each child, specific actions or facial expressions are emphasized. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the video length and frame rate are adjusted to provide the optimal viewing experience. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the order of generated videos based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the generation algorithm is optimized by referring to the user's past video viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the accuracy of the generation is improved by referencing relevant external data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is It estimates the user's emotions and adjusts the preferred reception method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is When a request is received, the system will refer to the user's past request history to select the most suitable method of acceptance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is When receiving a request, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is When receiving requests, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is When customizing the display to highlight specific scenes or prioritize videos of specific children, the user's past customization history is referenced. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customization unit is During customization, we adjust the video length and frame rate to provide the optimal viewing experience. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned customization unit is During customization, the customization algorithm is optimized by referencing the user's past video viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned customization unit is During customization, we refer to relevant external data to improve the accuracy of the customization. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects videos of school events such as sports days, An analysis unit analyzes the video collected by the aforementioned collection unit, A generation unit generates a video for each child based on the data analyzed by the analysis unit, A reception desk that takes user requests, The system includes a customization unit that customizes the video based on the requests received by the reception unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect video from different angles using multiple cameras. The system according to feature 1.

3. The aforementioned analysis unit, The system recognizes the movements of each child in the video and generates individual videos for each child. The system according to feature 1.

4. The generating unit is In a foot race scene, track each child's running and create a video of each child individually. The system according to feature 1.

5. The aforementioned customization unit is Customize the display to highlight specific scenes or prioritize the display of videos of specific children. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is Multiple cameras are used to collect video from different angles, and the footage from each camera is integrated in real time. The system according to feature 1.

8. The aforementioned collection unit is During collection, the system automatically detects specific events or actions to determine the scope of the video to be collected. The system according to feature 1.

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

10. The aforementioned collection unit is During data collection, the system prioritizes collecting videos that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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