system

Abstract

The system according to this embodiment aims to automate the editing of pet videos, the deletion of personal information, and the analysis of viewer data. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an editing unit, an analysis unit, and a revenue unit. The reception unit films pet videos and uploads them to the platform. The analysis unit analyzes the videos uploaded by the reception unit and detects and removes personal information and backgrounds that the user does not want to show. The editing unit automatically edits the videos analyzed by the analysis unit and automatically adds music, narration, and subtitles. The analysis unit analyzes viewer reactions and data to the videos edited by the editing unit and suggests content for the next time. The revenue unit provides monetization and donation functions based on the data obtained by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

[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, the method including 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 conventional technology, there is a problem that it is necessary to manually edit pet videos, delete personal information, and analyze viewer data, which is time-consuming.

[0005] The system according to the embodiment aims to automate the editing of pet videos, the deletion of personal information, and the analysis of viewer data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an editing unit, an analysis unit, and a revenue unit. The reception unit films pet videos and uploads them to the platform. The analysis unit analyzes the videos uploaded by the reception unit and detects and removes personal information and backgrounds that the user does not want to show. The editing unit automatically edits the videos analyzed by the analysis unit and automatically adds music, narration, and subtitles. The analysis unit analyzes viewer reactions and data to the videos edited by the editing unit and suggests content for the next project. The revenue unit provides monetization and donation functions based on the data obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate tasks such as editing pet videos, deleting personal information, and analyzing viewer data. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 three or more matters are expressed by connecting them with "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). [[ID=第十九]]

[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 platform according to an embodiment of the present invention is a system that utilizes an AI agent to automatically edit pet videos, remove personal information, and analyze viewer data. This system provides an environment in which users can easily act as pet video streamers, and also includes a donation function, aiming to support rescue cats and reduce euthanasia. For example, a user films a pet video and uploads it to the platform. Next, the AI ​​agent analyzes the video and detects and removes personal information and unwanted backgrounds in the footage. This enables safe video posting. Furthermore, the AI ​​agent analyzes the pet video, automatically edits it, and automatically adds music, narration, and subtitles. It can also automatically generate narration that learns the pet's perspective and the user's voice. In addition, the AI ​​agent autonomously replies to comments and manages social media, and analyzes viewer reactions and data to suggest future content. This allows users to easily create and post engaging content. Furthermore, the platform is equipped with a tipping function, allowing users to monetize their content. A portion of the revenue is donated to support rescue cats and reduce euthanasia. This reduces the burden on rescue cat facilities and increases the number of pets they can accept. This platform targets cat shelters, local animal welfare organizations, beginner pet owners who want to share pet videos on social media, and people who want to post pet videos. It addresses challenges such as a lack of video editing and posting knowledge, potential privacy risks in recorded footage, and a lack of ideas or time to create engaging content. This allows the platform to provide an environment where users can easily become pet video creators.

[0029] The platform according to this embodiment comprises a reception unit, an analysis unit, an editing unit, an analysis unit, and a revenue unit. The reception unit uploads pet videos taken by users to the platform. The reception unit provides, for example, an interface that allows users to easily upload pet videos they have taken. The reception unit can also save pet videos taken by users to cloud storage. The reception unit can also automatically adjust the video format and resolution when users upload pet videos they have taken. The analysis unit analyzes the uploaded videos and detects and removes personal information and backgrounds that users do not want to show. The analysis unit can, for example, use facial recognition technology to detect personal information in the video and apply mosaic processing. The analysis unit can also remove backgrounds that users do not want to show by applying background blur processing. The analysis unit can also use speech recognition technology to detect audio containing personal information in the video and remove the audio. The editing unit automatically edits the analyzed videos and automatically adds music, narration, and subtitles. The editing unit can, for example, automatically select appropriate music according to the content of the video and add it to the video. The editing unit can also automatically generate narration and add it to the video. The editorial team can automatically generate subtitles and add them to videos. The editorial team can also automatically generate narration that learns from pet perspectives and user feedback. The analytics team analyzes viewer reactions and data on edited videos and suggests content for the next video. The analytics team collects and analyzes data such as the number of views, comments, and likes. Based on viewer reactions, the analytics team can also suggest themes and content for the next video. Based on viewer data, the analytics team can identify target audiences and suggest content for the next video. The revenue team provides monetization and donation functions based on the data obtained. For example, the revenue team can insert advertisements into videos and generate advertising revenue. The revenue team can also provide a tipping function for viewers and generate revenue. The revenue team can also provide a mechanism to donate a portion of the revenue to support rescued cats and reduce euthanasia. In this way, the platform according to the embodiment can provide an environment in which users can easily act as pet video streamers.

[0030] The reception desk allows users to upload pet videos they have filmed to the platform. For example, the reception desk provides an interface that makes it easy for users to upload their pet videos. Specifically, it offers intuitive drag-and-drop functionality and a file selection dialog so users can easily upload videos from their smartphones or computers. The reception desk can also save user-filmed pet videos to cloud storage. The cloud storage uses reliable servers and ensures data redundancy to facilitate video backup and sharing. Furthermore, the reception desk can automatically adjust the video format and resolution when users upload pet videos. For example, even if a video is uploaded in a different format, the platform automatically converts it to a supported standard format and optimizes the resolution, providing viewers with high-quality videos. This allows users to easily upload videos without technical knowledge, and viewers can enjoy videos of consistent quality.

[0031] The analysis unit analyzes uploaded videos to detect and remove personal information and unwanted backgrounds. For example, the analysis unit uses facial recognition technology to detect personal information in videos and apply mosaic processing. Specifically, an AI-based facial recognition algorithm detects the faces of people in the video and automatically applies mosaic processing to protect privacy. The analysis unit can also remove unwanted backgrounds by blurring them. For example, it can detect unwanted backgrounds or areas containing personal information that appear in videos shot by users and apply blurring processing to hide information that viewers do not want to see. Furthermore, the analysis unit can use speech recognition technology to detect and remove audio containing personal information in videos. For example, it can detect personal information such as names and addresses spoken in videos using speech recognition technology and prevent privacy violations through audio by muting those parts or replacing them with other audio. In this way, the analysis unit can provide an environment where users can upload videos with peace of mind and provide viewers with safe content.

[0032] The editorial team automatically edits the analyzed videos and automatically adds music, narration, and subtitles. For example, the editorial team automatically selects appropriate music according to the video content and adds it to the video. Specifically, the AI ​​analyzes the video content, selects music from the library that matches the pet's movements and atmosphere, and synchronizes it with the video. The editorial team can also automatically generate and add narration to the video. For example, the AI ​​generates natural narration that matches the pet's actions and scenes, and inserts it into the video to provide more engaging content for viewers. Furthermore, the editorial team can also automatically generate and add subtitles to the video. For example, by converting the audio in the video into text and displaying it as subtitles, it is possible to accommodate users with hearing impairments or those who watch with the sound off. The editorial team can also automatically generate narration that learns from the pet's perspective or the user's voice. For example, by generating narration from the pet's point of view or narration that mimics the user's voice, it is possible to create more individual and unique videos. In this way, the editorial team can provide an environment in which users can create high-quality videos without much effort and provide engaging content for viewers.

[0033] The analytics department analyzes viewer reactions and data from edited videos to suggest future content. For example, it collects and analyzes data such as view counts, comments, and likes. Specifically, after a video is released, it collects viewer behavior data in real time and analyzes metrics such as view counts, watch time, and engagement rates. Based on viewer reactions, the analytics department can also suggest themes and content for future videos. For example, if videos of a particular type of pet are popular, it analyzes this trend and suggests it as a theme for the next video. Furthermore, the analytics department can identify target audiences based on viewer data and suggest future content. For example, it analyzes data such as viewer age, location, and interests to suggest content tailored to specific target groups. This allows users to create content that meets viewer needs and increases viewer satisfaction. Additionally, the analytics department collects viewer feedback to identify areas for content improvement. For example, it analyzes viewer comments and ratings and provides feedback to users on how to improve video content and editing methods. This allows users to continuously improve the quality of their content.

[0034] The revenue unit provides monetization and donation functions based on the data obtained. For example, the revenue unit can insert advertisements into videos and generate advertising revenue. Specifically, it provides a system that displays advertisements before or during video playback and generates revenue from advertisers. The revenue unit can also provide a tipping function from viewers and generate revenue. For example, it provides a system where viewers can tip for videos they like, and the revenue is directly returned to the user. Furthermore, the revenue unit can provide a system that donates a portion of the revenue to support rescued cats or to reduce euthanasia. For example, it can provide an option to automatically donate a portion of the revenue earned by users, supporting social contribution activities. This allows users to not only earn revenue but also participate in social contribution. The revenue unit provides diverse means of monetization and supports users in making their activities sustainable. For example, it provides regular revenue reports to make it easier for users to understand their revenue status. In addition, the revenue unit enhances the overall attractiveness of the platform by continuously developing and providing new ideas and functions for monetization to users. In this way, the revenue unit can provide strong support for users to succeed as pet video streamers and promote the growth of the entire platform.

[0035] The analysis unit can detect and remove personal information and unwanted backgrounds within a video. For example, the analysis unit can use facial recognition technology to detect personal information in a video and apply a mosaic effect. The analysis unit can also remove unwanted backgrounds by blurring them. The analysis unit can also use speech recognition technology to detect and remove audio containing personal information within a video. This enables safe video posting by detecting and removing personal information and unwanted backgrounds within videos. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to detect personal information in a video, the analysis unit can input video data into a generating AI, which can then detect and remove the personal information.

[0036] The editorial team can analyze pet videos, automatically edit them, and automatically add music, narration, and subtitles. For example, the editorial team can automatically select appropriate music according to the video content and add it to the video. The editorial team can also automatically generate narration and add it to the video. The editorial team can also automatically generate subtitles and add them to the video. This makes it easy to create engaging content by automatically editing pet videos and automatically adding music, narration, and subtitles. Some or all of the above processes performed by the editorial team may be done using AI, for example, or not. For example, the editorial team can input the video content into a generating AI, which can then perform the process of automatically generating music, narration, and subtitles.

[0037] The editorial team can automatically generate narration that learns from a pet's perspective and user feedback. For example, the editorial team can learn a pet's behavior patterns and automatically generate narration from a pet's point of view. The editorial team can also learn user feedback and automatically generate narration that reflects user opinions. By automatically generating narration that learns from a pet's perspective and user feedback, the editorial team can provide more relatable content. Some or all of the above processes in the editorial team may be performed using AI, for example, or without AI. For example, the editorial team can input a pet's behavior patterns into a generating AI, which can then execute a process to automatically generate narration from a pet's point of view.

[0038] The analytics department can analyze viewer reactions and data to suggest future content. For example, the analytics department collects and analyzes data such as the number of views, comments, and likes. Based on viewer reactions, the analytics department can also suggest themes and content for future content. This allows for effective suggestion of future content by analyzing viewer reactions and data. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input viewer reaction data into a generating AI, which then performs the process of suggesting future content.

[0039] The revenue-generating unit can provide monetization and donation functions. For example, the revenue-generating unit can insert advertisements into videos and generate advertising revenue. The revenue-generating unit can also provide a tipping function for viewers and generate revenue. The revenue-generating unit can also provide a mechanism to donate a portion of its revenue to support rescued cats and reduce euthanasia. In this way, by providing monetization and donation functions, users can earn revenue while also contributing to supporting rescued cats and reducing euthanasia. Some or all of the above processes in the revenue-generating unit may be performed using AI, for example, or not using AI. For example, the revenue-generating unit can input the insertion of advertisements for monetization into a generating AI, and the generating AI can perform the process of selecting the most suitable advertisements.

[0040] The reception desk can analyze the user's past video upload history and select the optimal upload method. For example, the reception desk can suggest time slots when the user has previously successfully uploaded videos. The reception desk can also prioritize suggesting upload methods the user has used in the past (Wi-Fi, mobile data, etc.). The reception desk can also suggest the optimal video format based on the user's past upload history. In this way, the optimal upload method can be selected by analyzing the user's past video upload 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 upload history data into a generating AI, which can then perform the process of selecting the optimal upload method.

[0041] The reception system can filter videos based on the user's current projects and areas of interest when they are uploaded. For example, the reception system can prioritize uploading videos related to the user's current projects. The reception system can also automatically assign relevant tags based on the user's areas of interest. The reception system can also suggest the optimal upload timing based on the user's project progress. This allows for the priority uploading of highly relevant videos by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's project data into a generating AI, which can then perform the optimal filtering process.

[0042] The reception system can prioritize uploading videos that are highly relevant to the user, taking into account the user's geographical location information. For example, if a user is in a specific region, the reception system will prioritize uploading videos related to that region. The reception system can also automatically assign optimal tags based on the user's geographical location information. The reception system can also suggest the optimal upload timing, taking into account the user's location information. This allows for the effective distribution of region-related videos by prioritizing the upload of highly relevant videos based on the user's geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's geographical location information into a generating AI, which can then perform the process of selecting highly relevant videos.

[0043] The reception desk can analyze a user's social media activity when uploading videos and upload relevant videos. For example, the reception desk can prioritize uploading relevant videos based on content the user has shared on social media. The reception desk can also automatically assign the most appropriate tags based on the user's social media activity. The reception desk can also analyze the user's social media activity and suggest the optimal upload timing. This allows for the effective uploading of relevant videos by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then perform the process of selecting relevant videos.

[0044] The analysis unit can prioritize the analysis of specific objects or scenes within a video during video analysis. For example, the analysis unit can prioritize the analysis of a pet's face. The analysis unit can also prioritize the analysis of objects specified by the user. The analysis unit can also prioritize the analysis of specific scenes (e.g., scenes of pets playing). By prioritizing the analysis of specific objects or scenes within the video, important parts can be effectively analyzed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data of specific objects or scenes into a generating AI, which can then perform processes that prioritize the analysis of those objects or scenes.

[0045] The analysis unit can optimize the analysis algorithm by referring to the user's past analysis history when analyzing a video. For example, the analysis unit optimizes the analysis algorithm based on video data that the user has previously analyzed. The analysis unit can also suggest the optimal analysis method based on the user's past analysis history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis history. In this way, by referring to the user's past analysis history, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes 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 analysis history data into a generating AI, and the generating AI can perform a process to optimize the analysis algorithm.

[0046] The editorial team can adjust the level of detail in editing based on the importance of the video. For example, the editorial team can edit important scenes in detail. They can also edit general scenes simply. The editorial team can also prioritize editing important scenes specified by the user. This allows for detailed editing of important scenes by adjusting the level of detail based on the importance of the video. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input video importance data into a generating AI, which can then perform the process of adjusting the level of detail in editing.

[0047] The editorial team can apply different editing algorithms depending on the video category during editing. For example, for pet videos, the editorial team might apply an editing algorithm that emphasizes the pet's movements. For landscape videos, the editorial team might apply an editing algorithm that emphasizes the beauty of the scenery. For event videos, the editorial team might apply an editing algorithm that emphasizes the highlights of the event. This allows for optimal editing by applying different editing algorithms depending on the video category. Some or all of the above processes in the editorial team may be performed using AI, for example, or not using AI. For example, the editorial team can input video category data into a generating AI, and the generating AI can perform the process of applying different editing algorithms.

[0048] The editorial team can determine editing priorities based on when the videos were shot. For example, the editorial team might prioritize editing the most recent videos. They can also prioritize editing videos shot at a time specified by the user. They can also prioritize editing videos related to a specific event. This allows for prioritizing the editing of the most recent videos by determining editing priorities based on when the videos were shot. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input video shooting date data into a generating AI, which can then perform the process of determining editing priorities.

[0049] The editorial team can adjust the editing order based on the relevance of the videos during the editing process. For example, the editorial team can edit related videos consecutively. The editorial team can also determine the editing order based on user-specified relevance. The editorial team can also prioritize editing videos related to a specific theme. This allows for the effective editing of related videos by adjusting the editing order based on their relevance. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input video relevance data into a generating AI, which can then perform the process of adjusting the editing order.

[0050] The analysis unit can predict current audience reactions by referring to past audience data during analysis. For example, the analysis unit predicts current audience reactions based on past audience data. The analysis unit can also suggest optimal content from past audience data. The analysis unit can also analyze audience reactions by referring to past audience data. This allows for effective prediction of current audience reactions by referring to past audience data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past audience data into a generating AI, which then performs a process to predict current audience reactions.

[0051] The analysis unit can apply different analysis methods to each video category during analysis. For example, the analysis unit can apply an analysis method that emphasizes the pet's movements to pet videos. The analysis unit can also apply an analysis method that emphasizes the beauty of the scenery to landscape videos. The analysis unit can also apply an analysis method that emphasizes the highlights of the event to event videos. By applying different analysis methods to each video category, the data can be analyzed in the most optimal way. 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 category data into a generating AI, and the generating AI can perform the process of applying different analysis methods.

[0052] The analysis unit can analyze changes in viewer reactions based on the video posting date during analysis. For example, the analysis unit can analyze viewer reactions to videos posted during a specific season. The analysis unit can also analyze viewer reactions to videos posted during a specific event period. The analysis unit can also analyze viewer reactions to videos posted during a specific time period. This allows content to be delivered at the optimal time by analyzing changes in viewer reactions based on the video posting date. 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 posting date data into a generating AI, which can then perform the process of analyzing changes in viewer reactions.

[0053] The analysis unit can analyze viewer reactions by referring to relevant market data for the video during the analysis process. For example, the analysis unit can predict viewer reactions based on relevant market data. The analysis unit can also suggest optimal content based on relevant market data. The analysis unit can also analyze viewer reactions by referring to relevant market data. This allows for effective analysis of viewer reactions by referring to relevant market data for the video. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant market data into a generating AI, which can then perform the process of analyzing viewer reactions.

[0054] The revenue unit can select the optimal method of providing monetization and donation functions by referring to the user's past revenue history. For example, the revenue unit can propose the optimal monetization method based on the user's past revenue history. The revenue unit can also propose the optimal donation method based on the user's past donation history. The revenue unit can also improve the accuracy of monetization by referring to the user's past revenue history. This allows for optimal monetization and donation by referring to the user's past revenue history. Some or all of the above processes in the revenue unit may be performed using AI, for example, or not using AI. For example, the revenue unit can input the user's past revenue history data into a generating AI, which can then perform the process of selecting the optimal method of providing monetization.

[0055] The revenue unit can customize the means of providing monetization and donation functions based on the user's current living situation. For example, the revenue unit can suggest the optimal monetization method considering the user's current living situation. The revenue unit can also suggest the optimal donation method based on the user's current living situation. The revenue unit can also improve the accuracy of monetization by referring to the user's current living situation. This allows for optimal monetization and donation by customizing the means of providing services based on the user's current living situation. Some or all of the above processes in the revenue unit may be performed using AI, for example, or not using AI. For example, the revenue unit can input the user's current living situation data into a generating AI, which can then perform the process of customizing the means of providing services.

[0056] The revenue unit can select the optimal method of providing monetization and donation functions by considering the user's geographical location. For example, the revenue unit can propose the optimal monetization method based on the user's geographical location. The revenue unit can also propose the optimal donation method based on the user's geographical location. The revenue unit can also improve the accuracy of monetization by referring to the user's geographical location. This allows for optimal monetization and donations by considering the user's geographical location. Some or all of the above processes in the revenue unit may be performed using AI, for example, or not using AI. For example, the revenue unit can input the user's geographical location data into a generating AI, which can then perform the process of selecting the optimal method of providing monetization.

[0057] The revenue generation unit can analyze users' social media activity and propose methods for providing monetization and donation functions. For example, the revenue generation unit can propose the optimal monetization method based on the user's social media activity. The revenue generation unit can also propose the optimal donation method based on the user's social media activity. The revenue generation unit can also improve the accuracy of monetization by referring to the user's social media activity. This allows for optimal monetization and donation methods by analyzing the user's social media activity. Some or all of the above processes in the revenue generation unit may be performed using AI, for example, or not. For example, the revenue generation unit can input user social media activity data into a generating AI, which then performs the process of proposing methods for providing monetization.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception system can automatically generate tags based on the content of videos when users upload them. For example, it can assign tags based on the type of pet or its behavior. Users can also manually add tags. This improves the searchability of videos, making it easier for viewers to find content that interests them. Furthermore, the reception system can analyze the popularity of tags and suggest the most suitable tags.

[0060] The analysis unit can detect specific sounds within a video and automatically edit parts of the video based on the content of the sounds. For example, it can detect pet noises or user voices and edit to highlight those parts. The analysis unit can also automatically extract scenes containing specific sounds. This allows for the effective highlighting of scenes that are of interest to the viewer.

[0061] The editorial team can automatically add effects based on the video's content. For example, they can add a slow-motion effect to a scene where a pet jumps. The editorial team can also apply filters to specific scenes, enhancing the video's visual appeal. Furthermore, the editorial team can provide an interface for users to manually add effects.

[0062] The editorial team can automatically add transitions based on the video content. For example, they can add fade-in and fade-out transitions when switching scenes. The editorial team can also apply smooth transitions between specific scenes, keeping the video flowing naturally. Furthermore, the editorial team can provide an interface for users to manually add transitions.

[0063] The analytics department can analyze audience demographic data and suggest content best suited to the target audience. For example, it can suggest content themes based on the audience's age and gender. The analytics department can also customize content based on the audience's region, thereby providing content tailored to their interests.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk uploads the pet videos taken by the user to the platform. The reception desk provides an easy interface for users to upload their pet videos and can also save them to cloud storage. It also has a function to automatically adjust the video format and resolution. Step 2: The analysis unit analyzes the uploaded video and detects and removes personal information and unwanted backgrounds. The analysis unit uses facial recognition technology to detect personal information in the video and applies mosaic processing. It can also use background blurring and speech recognition technology to remove unwanted backgrounds and audio containing personal information. Step 3: The editorial team automatically edits the analyzed videos and adds music, narration, and subtitles. The editorial team automatically selects appropriate music based on the video content and automatically generates and adds narration and subtitles to the video. Furthermore, it can also automatically generate narration that learns from a pet's perspective or the user's voice. Step 4: The analytics department analyzes viewer reactions and data from the edited videos and proposes content for the next episode. The analytics department collects data such as the number of views, comments, and likes, and proposes themes and content for the next episode based on viewer reactions. They can also identify target audiences based on viewer data and propose content for the next episode accordingly. Step 5: The revenue unit provides monetization and donation functions based on the data obtained. The revenue unit can insert ads into videos and generate advertising revenue. It can also provide a tipping function for viewers and generate revenue. Furthermore, it can provide a system to donate a portion of the revenue to support rescued cats and reduce euthanasia.

[0066] (Example of form 2) The platform according to an embodiment of the present invention is a system that utilizes an AI agent to automatically edit pet videos, remove personal information, and analyze viewer data. This system provides an environment in which users can easily act as pet video streamers, and also includes a donation function, aiming to support rescue cats and reduce euthanasia. For example, a user films a pet video and uploads it to the platform. Next, the AI ​​agent analyzes the video and detects and removes personal information and unwanted backgrounds in the footage. This enables safe video posting. Furthermore, the AI ​​agent analyzes the pet video, automatically edits it, and automatically adds music, narration, and subtitles. It can also automatically generate narration that learns the pet's perspective and the user's voice. In addition, the AI ​​agent autonomously replies to comments and manages social media, and analyzes viewer reactions and data to suggest future content. This allows users to easily create and post engaging content. Furthermore, the platform is equipped with a tipping function, allowing users to monetize their content. A portion of the revenue is donated to support rescue cats and reduce euthanasia. This reduces the burden on rescue cat facilities and increases the number of pets they can accept. This platform targets cat shelters, local animal welfare organizations, beginner pet video creators, and people who want to share pet videos on social media. It addresses challenges such as a lack of video editing and posting knowledge, potential privacy risks in recorded footage, and a lack of ideas or time to create engaging content. This allows the platform to provide an environment where users can easily become pet video creators.

[0067] The platform according to this embodiment comprises a reception unit, an analysis unit, an editing unit, an analysis unit, and a revenue unit. The reception unit uploads pet videos taken by users to the platform. The reception unit provides, for example, an interface that allows users to easily upload pet videos they have taken. The reception unit can also save pet videos taken by users to cloud storage. The reception unit can also automatically adjust the video format and resolution when users upload pet videos they have taken. The analysis unit analyzes the uploaded videos and detects and removes personal information and backgrounds that users do not want to show. The analysis unit can, for example, use facial recognition technology to detect personal information in the video and apply mosaic processing. The analysis unit can also remove backgrounds that users do not want to show by applying background blur processing. The analysis unit can also use speech recognition technology to detect audio containing personal information in the video and remove the audio. The editing unit automatically edits the analyzed videos and automatically adds music, narration, and subtitles. The editing unit can, for example, automatically select appropriate music according to the content of the video and add it to the video. The editing unit can also automatically generate narration and add it to the video. The editorial team can automatically generate subtitles and add them to videos. The editorial team can also automatically generate narration that learns from pet perspectives and user feedback. The analytics team analyzes viewer reactions and data on edited videos and suggests content for the next video. The analytics team collects and analyzes data such as the number of views, comments, and likes. Based on viewer reactions, the analytics team can also suggest themes and content for the next video. Based on viewer data, the analytics team can identify target audiences and suggest content for the next video. The revenue team provides monetization and donation functions based on the data obtained. For example, the revenue team can insert advertisements into videos and generate advertising revenue. The revenue team can also provide a tipping function for viewers and generate revenue. The revenue team can also provide a mechanism to donate a portion of the revenue to support rescued cats and reduce euthanasia. In this way, the platform according to the embodiment can provide an environment in which users can easily act as pet video streamers.

[0068] The reception desk allows users to upload pet videos they have filmed to the platform. For example, the reception desk provides an interface that makes it easy for users to upload their pet videos. Specifically, it offers intuitive drag-and-drop functionality and a file selection dialog so users can easily upload videos from their smartphones or computers. The reception desk can also save user-filmed pet videos to cloud storage. The cloud storage uses reliable servers and ensures data redundancy to facilitate video backup and sharing. Furthermore, the reception desk can automatically adjust the video format and resolution when users upload pet videos. For example, even if a video is uploaded in a different format, the platform automatically converts it to a supported standard format and optimizes the resolution, providing viewers with high-quality videos. This allows users to easily upload videos without technical knowledge, and viewers can enjoy videos of consistent quality.

[0069] The analysis unit analyzes uploaded videos to detect and remove personal information and unwanted backgrounds. For example, the analysis unit uses facial recognition technology to detect personal information in videos and apply mosaic processing. Specifically, an AI-based facial recognition algorithm detects the faces of people in the video and automatically applies mosaic processing to protect privacy. The analysis unit can also remove unwanted backgrounds by blurring them. For example, it can detect unwanted backgrounds or areas containing personal information that appear in videos shot by users and apply blurring processing to hide information that viewers do not want to see. Furthermore, the analysis unit can use speech recognition technology to detect and remove audio containing personal information in videos. For example, it can detect personal information such as names and addresses spoken in videos using speech recognition technology and prevent privacy violations through audio by muting those parts or replacing them with other audio. In this way, the analysis unit can provide an environment where users can upload videos with peace of mind and provide viewers with safe content.

[0070] The editorial team automatically edits the analyzed videos and automatically adds music, narration, and subtitles. For example, the editorial team automatically selects appropriate music according to the video content and adds it to the video. Specifically, the AI ​​analyzes the video content, selects music from the library that matches the pet's movements and atmosphere, and synchronizes it with the video. The editorial team can also automatically generate and add narration to the video. For example, the AI ​​generates natural narration that matches the pet's actions and scenes, and inserts it into the video to provide more engaging content for viewers. Furthermore, the editorial team can also automatically generate and add subtitles to the video. For example, by converting the audio in the video into text and displaying it as subtitles, it is possible to accommodate users with hearing impairments or those who watch with the sound off. The editorial team can also automatically generate narration that learns from the pet's perspective or the user's voice. For example, by generating narration from the pet's point of view or narration that mimics the user's voice, it is possible to create more individual and unique videos. In this way, the editorial team can provide an environment in which users can create high-quality videos without much effort and provide engaging content for viewers.

[0071] The analytics department analyzes viewer reactions and data from edited videos to suggest future content. For example, it collects and analyzes data such as view counts, comments, and likes. Specifically, after a video is released, it collects viewer behavior data in real time and analyzes metrics such as view counts, watch time, and engagement rates. Based on viewer reactions, the analytics department can also suggest themes and content for future videos. For example, if videos of a particular type of pet are popular, it analyzes this trend and suggests it as a theme for the next video. Furthermore, the analytics department can identify target audiences based on viewer data and suggest future content. For example, it analyzes data such as viewer age, location, and interests to suggest content tailored to specific target groups. This allows users to create content that meets viewer needs and increases viewer satisfaction. Additionally, the analytics department collects viewer feedback to identify areas for content improvement. For example, it analyzes viewer comments and ratings and provides feedback to users on how to improve video content and editing methods. This allows users to continuously improve the quality of their content.

[0072] The revenue unit provides monetization and donation functions based on the data obtained. For example, the revenue unit can insert advertisements into videos and generate advertising revenue. Specifically, it provides a system that displays advertisements before or during video playback and generates revenue from advertisers. The revenue unit can also provide a tipping function from viewers and generate revenue. For example, it provides a system where viewers can tip for videos they like, and the revenue is directly returned to the user. Furthermore, the revenue unit can provide a system that donates a portion of the revenue to support rescued cats or to reduce euthanasia. For example, it can provide an option to automatically donate a portion of the revenue earned by users, supporting social contribution activities. This allows users to not only earn revenue but also participate in social contribution. The revenue unit provides diverse means of monetization and supports users in making their activities sustainable. For example, it provides regular revenue reports to make it easier for users to understand their revenue status. In addition, the revenue unit enhances the overall attractiveness of the platform by continuously developing and providing new ideas and functions for monetization to users. In this way, the revenue unit can provide strong support for users to succeed as pet video streamers and promote the growth of the entire platform.

[0073] The analysis unit can detect and remove personal information and unwanted backgrounds within a video. For example, the analysis unit can use facial recognition technology to detect personal information in a video and apply a mosaic effect. The analysis unit can also remove unwanted backgrounds by blurring them. The analysis unit can also use speech recognition technology to detect and remove audio containing personal information within a video. This enables safe video posting by detecting and removing personal information and unwanted backgrounds within videos. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to detect personal information in a video, the analysis unit can input video data into a generating AI, which can then detect and remove the personal information.

[0074] The editorial team can analyze pet videos, automatically edit them, and automatically add music, narration, and subtitles. For example, the editorial team can automatically select appropriate music according to the video content and add it to the video. The editorial team can also automatically generate narration and add it to the video. The editorial team can also automatically generate subtitles and add them to the video. This makes it easy to create engaging content by automatically editing pet videos and automatically adding music, narration, and subtitles. Some or all of the above processes performed by the editorial team may be done using AI, for example, or not. For example, the editorial team can input the video content into a generating AI, which can then perform the process of automatically generating music, narration, and subtitles.

[0075] The editorial team can automatically generate narration that learns from a pet's perspective and user feedback. For example, the editorial team can learn a pet's behavior patterns and automatically generate narration from a pet's point of view. The editorial team can also learn user feedback and automatically generate narration that reflects user opinions. By automatically generating narration that learns from a pet's perspective and user feedback, the editorial team can provide more relatable content. Some or all of the above processes in the editorial team may be performed using AI, for example, or without AI. For example, the editorial team can input a pet's behavior patterns into a generating AI, which can then execute a process to automatically generate narration from a pet's point of view.

[0076] The analytics department can analyze viewer reactions and data to suggest future content. For example, the analytics department collects and analyzes data such as the number of views, comments, and likes. Based on viewer reactions, the analytics department can also suggest themes and content for future content. This allows for effective suggestion of future content by analyzing viewer reactions and data. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input viewer reaction data into a generating AI, which then performs the process of suggesting future content.

[0077] The revenue-generating unit can provide monetization and donation functions. For example, the revenue-generating unit can insert advertisements into videos and generate advertising revenue. The revenue-generating unit can also provide a tipping function for viewers and generate revenue. The revenue-generating unit can also provide a mechanism to donate a portion of its revenue to support rescued cats and reduce euthanasia. In this way, by providing monetization and donation functions, users can earn revenue while also contributing to supporting rescued cats and reducing euthanasia. Some or all of the above processes in the revenue-generating unit may be performed using AI, for example, or not using AI. For example, the revenue-generating unit can input the insertion of advertisements for monetization into a generating AI, and the generating AI can perform the process of selecting the most suitable advertisements.

[0078] The reception desk can estimate the user's emotions and adjust the video upload timing based on the estimated emotions. For example, if the user is excited, the reception desk can allow the video to be uploaded immediately. If the user is tired, the reception desk can also set a reminder to upload later. If the user is relaxed, the reception desk can also suggest the optimal upload timing. This allows the video to be uploaded at the optimal time by adjusting the upload timing 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the process of estimating the user's emotions.

[0079] The reception desk can analyze the user's past video upload history and select the optimal upload method. For example, the reception desk can suggest time slots when the user has previously successfully uploaded videos. The reception desk can also prioritize suggesting upload methods the user has used in the past (Wi-Fi, mobile data, etc.). The reception desk can also suggest the optimal video format based on the user's past upload history. In this way, the optimal upload method can be selected by analyzing the user's past video upload 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 upload history data into a generating AI, which can then perform the process of selecting the optimal upload method.

[0080] The reception system can filter videos based on the user's current projects and areas of interest when they are uploaded. For example, the reception system can prioritize uploading videos related to the user's current projects. The reception system can also automatically assign relevant tags based on the user's areas of interest. The reception system can also suggest the optimal upload timing based on the user's project progress. This allows for the priority uploading of highly relevant videos by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's project data into a generating AI, which can then perform the optimal filtering process.

[0081] The reception desk can estimate the user's emotions and determine the priority of videos to upload based on the estimated emotions. For example, if the user is excited, the reception desk may prioritize videos to upload immediately. If the user is relaxed, the reception desk may also prioritize videos to upload later. If the user is tired, the reception desk may also suggest the optimal upload time. This allows for the priority uploading of the most suitable videos by determining the priority of videos to upload 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 using AI. For example, the reception desk may input user emotion data into a generative AI, which can then perform the process of determining the priority of videos.

[0082] The reception system can prioritize uploading videos that are highly relevant to the user, taking into account the user's geographical location information. For example, if a user is in a specific region, the reception system will prioritize uploading videos related to that region. The reception system can also automatically assign optimal tags based on the user's geographical location information. The reception system can also suggest the optimal upload timing, taking into account the user's location information. This allows for the effective distribution of region-related videos by prioritizing the upload of highly relevant videos based on the user's geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's geographical location information into a generating AI, which can then perform the process of selecting highly relevant videos.

[0083] The reception desk can analyze a user's social media activity when uploading videos and upload relevant videos. For example, the reception desk can prioritize uploading relevant videos based on content the user has shared on social media. The reception desk can also automatically assign the most appropriate tags based on the user's social media activity. The reception desk can also analyze the user's social media activity and suggest the optimal upload timing. This allows for the effective uploading of relevant videos by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI, which can then perform the process of selecting relevant videos.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of detecting and deleting personal information and unwanted backgrounds based on the estimated user emotions. For example, the analysis unit can increase the accuracy of detection and deletion if the user is tense. It can also adjust the accuracy of detection and deletion if the user is relaxed. It can also optimize the accuracy of detection and deletion if the user is excited. This allows for detection and deletion with optimal accuracy by adjusting the accuracy of detecting and deleting personal information and unwanted backgrounds 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 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 the generative AI can perform a process to adjust the accuracy of detection and deletion.

[0085] The analysis unit can prioritize the analysis of specific objects or scenes within a video during video analysis. For example, the analysis unit can prioritize the analysis of a pet's face. The analysis unit can also prioritize the analysis of objects specified by the user. The analysis unit can also prioritize the analysis of specific scenes (e.g., scenes of pets playing). By prioritizing the analysis of specific objects or scenes within the video, important parts can be effectively analyzed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data of specific objects or scenes into a generating AI, which can then perform processes that prioritize the analysis of those objects or scenes.

[0086] The analysis unit can optimize the analysis algorithm by referring to the user's past analysis history when analyzing a video. For example, the analysis unit optimizes the analysis algorithm based on video data that the user has previously analyzed. The analysis unit can also suggest the optimal analysis method based on the user's past analysis history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis history. In this way, by referring to the user's past analysis history, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes 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 analysis history data into a generating AI, and the generating AI can perform a process to optimize the analysis algorithm.

[0087] The editorial team can estimate the user's emotions and adjust the editing style based on those emotions. For example, if the user is relaxed, the editorial team might add calming music. If the user is excited, they might add energetic music. If the user is sad, they might add soothing music. This allows for optimal video editing by adjusting the editing style 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 editorial team may be performed using AI or not. For example, the editorial team can input user emotion data into a generative AI, which can then perform the process of adjusting the editing style.

[0088] The editorial team can adjust the level of detail in editing based on the importance of the video. For example, the editorial team can edit important scenes in detail. They can also edit general scenes simply. The editorial team can also prioritize editing important scenes specified by the user. This allows for detailed editing of important scenes by adjusting the level of detail based on the importance of the video. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input video importance data into a generating AI, which can then perform the process of adjusting the level of detail in editing.

[0089] The editorial team can apply different editing algorithms depending on the video category during editing. For example, for pet videos, the editorial team might apply an editing algorithm that emphasizes the pet's movements. For landscape videos, the editorial team might apply an editing algorithm that emphasizes the beauty of the scenery. For event videos, the editorial team might apply an editing algorithm that emphasizes the highlights of the event. This allows for optimal editing by applying different editing algorithms depending on the video category. Some or all of the above processes in the editorial team may be performed using AI, for example, or not using AI. For example, the editorial team can input video category data into a generating AI, and the generating AI can perform the process of applying different editing algorithms.

[0090] The editorial team can estimate the user's emotions and adjust the length of the edit based on the estimated emotions. For example, if the user is in a hurry, the editorial team can create a short edit. If the user is relaxed, the editorial team can create a longer edit. If the user is excited, the editorial team can suggest an optimal edit length. This allows for editing videos to be of the optimal length by adjusting the edit length based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, or not using AI. For example, the editorial team can input user emotion data into a generative AI, which can then perform the process of adjusting the edit length.

[0091] The editorial team can determine editing priorities based on when the videos were shot. For example, the editorial team might prioritize editing the most recent videos. They can also prioritize editing videos shot at a time specified by the user. They can also prioritize editing videos related to a specific event. This allows for prioritizing the editing of the most recent videos by determining editing priorities based on when the videos were shot. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input video shooting date data into a generating AI, which can then perform the process of determining editing priorities.

[0092] The editorial team can adjust the editing order based on the relevance of the videos during the editing process. For example, the editorial team can edit related videos consecutively. The editorial team can also determine the editing order based on user-specified relevance. The editorial team can also prioritize editing videos related to a specific theme. This allows for the effective editing of related videos by adjusting the editing order based on their relevance. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input video relevance data into a generating AI, which can then perform the process of adjusting the editing order.

[0093] The analysis unit can estimate the user's emotions and adjust the viewer response and data analysis methods based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can also perform a rapid analysis. If the user is sad, the analysis unit can also perform an emotion-sensitive analysis. This allows for optimal data analysis by adjusting the viewer response and data analysis methods 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform processing to adjust the viewer response and data analysis methods.

[0094] The analysis unit can predict current audience reactions by referring to past audience data during analysis. For example, the analysis unit predicts current audience reactions based on past audience data. The analysis unit can also suggest optimal content from past audience data. The analysis unit can also analyze audience reactions by referring to past audience data. This allows for effective prediction of current audience reactions by referring to past audience data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past audience data into a generating AI, which then performs a process to predict current audience reactions.

[0095] The analysis unit can apply different analysis methods to each video category during analysis. For example, the analysis unit can apply an analysis method that emphasizes the pet's movements to pet videos. The analysis unit can also apply an analysis method that emphasizes the beauty of the scenery to landscape videos. The analysis unit can also apply an analysis method that emphasizes the highlights of the event to event videos. By applying different analysis methods to each video category, the data can be analyzed in the most optimal way. 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 category data into a generating AI, and the generating AI can perform the process of applying different analysis methods.

[0096] The analysis unit can estimate the user's emotions and adjust the importance of viewer responses and data based on the estimated user emotions. For example, if the user is relaxed, the analysis unit may prioritize detailed data. If the user is excited, the analysis unit may also prioritize rapid data. If the user is sad, the analysis unit may also prioritize emotion-sensitive data. This allows for data analysis with optimal importance by adjusting the importance of viewer responses and data 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then perform the process of adjusting viewer responses and data importance.

[0097] The analysis unit can analyze changes in viewer reactions based on the video posting date during analysis. For example, the analysis unit can analyze viewer reactions to videos posted during a specific season. The analysis unit can also analyze viewer reactions to videos posted during a specific event period. The analysis unit can also analyze viewer reactions to videos posted during a specific time period. This allows content to be delivered at the optimal time by analyzing changes in viewer reactions based on the video posting date. 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 posting date data into a generating AI, which can then perform the process of analyzing changes in viewer reactions.

[0098] The analysis unit can analyze viewer reactions by referring to relevant market data for the video during the analysis process. For example, the analysis unit can predict viewer reactions based on relevant market data. The analysis unit can also suggest optimal content based on relevant market data. The analysis unit can also analyze viewer reactions by referring to relevant market data. This allows for effective analysis of viewer reactions by referring to relevant market data for the video. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant market data into a generating AI, which can then perform the process of analyzing viewer reactions.

[0099] The revenue unit can estimate the user's emotions and adjust how monetization and donation features are provided based on the estimated emotions. For example, if the user is relaxed, the revenue unit can suggest detailed monetization methods. If the user is excited, the revenue unit can also suggest quick monetization methods. If the user is sad, the revenue unit can also suggest emotionally sensitive donation methods. This allows for optimal monetization and donation by adjusting how monetization and donation features are provided based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the revenue unit may be performed using AI or not. For example, the revenue unit can input user emotion data into a generative AI, which can then perform the process of adjusting how monetization and donation features are provided.

[0100] The revenue unit can select the optimal method of providing monetization and donation functions by referring to the user's past revenue history. For example, the revenue unit can propose the optimal monetization method based on the user's past revenue history. The revenue unit can also propose the optimal donation method based on the user's past donation history. The revenue unit can also improve the accuracy of monetization by referring to the user's past revenue history. This allows for optimal monetization and donation by referring to the user's past revenue history. Some or all of the above processes in the revenue unit may be performed using AI, for example, or not using AI. For example, the revenue unit can input the user's past revenue history data into a generating AI, which can then perform the process of selecting the optimal method of providing monetization.

[0101] The revenue unit can customize the means of providing monetization and donation functions based on the user's current living situation. For example, the revenue unit can suggest the optimal monetization method considering the user's current living situation. The revenue unit can also suggest the optimal donation method based on the user's current living situation. The revenue unit can also improve the accuracy of monetization by referring to the user's current living situation. This allows for optimal monetization and donation by customizing the means of providing services based on the user's current living situation. Some or all of the above processes in the revenue unit may be performed using AI, for example, or not using AI. For example, the revenue unit can input the user's current living situation data into a generating AI, which can then perform the process of customizing the means of providing services.

[0102] The revenue unit can estimate the user's emotions and prioritize monetization and donation features based on those estimated emotions. For example, if the user is relaxed, the revenue unit might prioritize detailed monetization methods. If the user is excited, the revenue unit might prioritize quick monetization methods. If the user is sad, the revenue unit might prioritize emotionally sensitive donation methods. This allows for optimal monetization and donation by prioritizing monetization and donation features based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the revenue unit may be performed using AI or not. For example, the revenue unit can input user emotion data into a generative AI, which can then perform the process of prioritizing monetization and donation features.

[0103] The revenue unit can select the optimal method of providing monetization and donation functions by considering the user's geographical location. For example, the revenue unit can propose the optimal monetization method based on the user's geographical location. The revenue unit can also propose the optimal donation method based on the user's geographical location. The revenue unit can also improve the accuracy of monetization by referring to the user's geographical location. This allows for optimal monetization and donations by considering the user's geographical location. Some or all of the above processes in the revenue unit may be performed using AI, for example, or not using AI. For example, the revenue unit can input the user's geographical location data into a generating AI, which can then perform the process of selecting the optimal method of providing monetization.

[0104] The revenue generation unit can analyze users' social media activity and propose methods for providing monetization and donation functions. For example, the revenue generation unit can propose the optimal monetization method based on the user's social media activity. The revenue generation unit can also propose the optimal donation method based on the user's social media activity. The revenue generation unit can also improve the accuracy of monetization by referring to the user's social media activity. This allows for optimal monetization and donation methods by analyzing the user's social media activity. Some or all of the above processes in the revenue generation unit may be performed using AI, for example, or not. For example, the revenue generation unit can input user social media activity data into a generating AI, which then performs the process of proposing methods for providing monetization.

[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 reception system can automatically generate tags based on the content of videos when users upload them. For example, it can assign tags based on the type of pet or its behavior. Users can also manually add tags. This improves the searchability of videos, making it easier for viewers to find content that interests them. Furthermore, the reception system can analyze the popularity of tags and suggest the most suitable tags.

[0107] The analysis unit can detect specific sounds within a video and automatically edit parts of the video based on the content of the sounds. For example, it can detect pet noises or user voices and edit to highlight those parts. The analysis unit can also automatically extract scenes containing specific sounds. This allows for the effective highlighting of scenes that are of interest to the viewer.

[0108] The editorial team can automatically add effects based on the video's content. For example, they can add a slow-motion effect to a scene where a pet jumps. The editorial team can also apply filters to specific scenes, enhancing the video's visual appeal. Furthermore, the editorial team can provide an interface for users to manually add effects.

[0109] The editorial team can automatically add transitions based on the video content. For example, they can add fade-in and fade-out transitions when switching scenes. The editorial team can also apply smooth transitions between specific scenes, keeping the video flowing naturally. Furthermore, the editorial team can provide an interface for users to manually add transitions.

[0110] The analytics department can analyze audience demographic data and suggest content best suited to the target audience. For example, it can suggest content themes based on the audience's age and gender. The analytics department can also customize content based on the audience's region, thereby providing content tailored to their interests.

[0111] The reception desk can estimate the user's emotions and customize the video upload interface based on that estimation. For example, if the user is excited, it can provide a simple interface. If the user is relaxed, the reception desk can also provide more detailed options. This allows for the provision of an optimal interface tailored to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and customize the video analysis results based on those emotions. For example, if the user is tense, the analysis results will be displayed concisely. If the user is relaxed, the analysis unit can also display detailed analysis results. This allows for the provision of optimal analysis results tailored to the user's emotions.

[0113] The editorial team can estimate the user's emotions and customize the editing style based on that estimation. For example, if the user is excited, a dynamic editing style can be applied. If the user is relaxed, the editorial team can also apply a calm editing style. This allows them to provide the optimal editing style according to the user's emotions.

[0114] The analysis unit can estimate the user's emotions and customize viewer response data based on those estimated emotions. For example, if the user is excited, it will highlight positive response data. If the user is relaxed, the analysis unit can also display overall response data. This allows for the provision of optimal response data tailored to the user's emotions.

[0115] The revenue generation system can estimate the user's emotions and customize monetization suggestions based on those emotions. For example, if the user is excited, it can suggest a quick monetization method. If the user is relaxed, the revenue generation system can also suggest a more detailed monetization method. This allows the system to provide the optimal monetization method tailored to the user's emotions.

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

[0117] Step 1: The reception desk uploads the pet videos taken by the user to the platform. The reception desk provides an easy interface for users to upload their pet videos and can also save them to cloud storage. It also has a function to automatically adjust the video format and resolution. Step 2: The analysis unit analyzes the uploaded video and detects and removes personal information and unwanted backgrounds. The analysis unit uses facial recognition technology to detect personal information in the video and applies mosaic processing. It can also use background blurring and speech recognition technology to remove unwanted backgrounds and audio containing personal information. Step 3: The editorial team automatically edits the analyzed videos and adds music, narration, and subtitles. The editorial team automatically selects appropriate music based on the video content and automatically generates and adds narration and subtitles to the video. Furthermore, it can also automatically generate narration that learns from a pet's perspective or the user's voice. Step 4: The analytics department analyzes viewer reactions and data from the edited videos and proposes content for the next episode. The analytics department collects data such as the number of views, comments, and likes, and proposes themes and content for the next episode based on viewer reactions. They can also identify target audiences based on viewer data and propose content for the next episode accordingly. Step 5: The revenue unit provides monetization and donation functions based on the data obtained. The revenue unit can insert ads into videos and generate advertising revenue. It can also provide a tipping function for viewers and generate revenue. Furthermore, it can provide a system to donate a portion of the revenue to support rescued cats and reduce euthanasia.

[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 reception unit, analysis unit, editing unit, data analysis unit, and revenue unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface that allows users to easily upload pet videos they have taken. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded videos to detect and remove personal information and backgrounds that the user does not want to show. The editing unit is implemented by, for example, the control unit 46A of the smart device 14 and automatically edits the analyzed videos and automatically adds music, narration, and subtitles. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes viewer reactions and data to the edited videos and suggests content for the next time. The revenue unit is implemented by, for example, the control unit 46A of the smart device 14 and provides monetization and donation functions based on the obtained data. 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.

[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 commands and other instructions from the user by receiving voice signals. 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 reception unit, analysis unit, editing unit, data analysis unit, and revenue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface that allows users to easily upload pet videos they have taken. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded videos to detect and remove personal information and backgrounds that the user does not want to show. The editing unit is implemented by the control unit 46A of the smart glasses 214 and automatically edits the analyzed videos and automatically adds music, narration, and subtitles. The data analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes viewer reactions and data to the edited videos and suggests content for the next time. The revenue unit is implemented by the control unit 46A of the smart glasses 214 and provides monetization and donation functions based on the obtained data. 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.

[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 commands and other instructions from the user by receiving voice signals. 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 reception unit, analysis unit, editing unit, data analysis unit, and revenue unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface that allows users to easily upload pet videos they have taken. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded videos to detect and remove personal information and backgrounds that the user does not want to show. The editing unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically edits the analyzed videos and automatically adds music, narration, and subtitles. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes viewer reactions and data to the edited videos and suggests content for the next time. The revenue unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides monetization and donation functions based on the obtained data. 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.

[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 commands and other instructions from the user by receiving voice signals. 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 reception unit, analysis unit, editing unit, data analysis unit, and revenue unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface that allows users to easily upload pet videos they have taken. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the uploaded videos to detect and remove personal information and backgrounds that the user does not want to show. The editing unit is implemented by, for example, the control unit 46A of the robot 414 and automatically edits the analyzed videos and automatically adds music, narration, and subtitles. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes viewer reactions and data to the edited videos and suggests content for the next time. The revenue unit is implemented by, for example, the control unit 46A of the robot 414 and provides monetization and donation functions based on the obtained data. 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 reception desk takes pet videos and uploads them to the platform, The aforementioned reception unit analyzes the uploaded videos and detects and removes personal information and backgrounds that the user does not want to show, The editing unit automatically edits the video analyzed by the aforementioned analysis unit and automatically adds music, narration, and subtitles. The aforementioned editorial department analyzes viewer reactions and data from the videos it edits, and proposes content for the next episode. The system comprises a revenue unit that provides monetization and donation functions based on the data obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Detects and removes personal information and unwanted backgrounds from videos. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned editorial department, Analyzes pet videos, automatically edits them, and automatically adds music, narration, and subtitles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned editorial department, Automatically generates narration that learns from the perspective of pets and user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We analyze viewer reactions and data to suggest content for the next episode. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned revenue-generating section is, Provides monetization and donation features. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the video upload timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past video upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading videos, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates user sentiment and prioritizes uploading videos based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When uploading videos, the system prioritizes uploading videos that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When uploading a video, the system analyzes the user's social media activity and uploads relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of detecting and removing personal information and unwanted backgrounds based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing a video, prioritize analyzing specific objects or scenes within the video. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing videos, the analysis algorithm is optimized by referring to the user's past analysis history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editorial department, It estimates the user's emotions and adjusts the editing style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editorial department, During editing, adjust the level of detail based on the importance of the video. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editorial department, During editing, different editing algorithms are applied depending on the video category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editorial department, It estimates the user's emotions and adjusts the length of the edit based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editorial department, When editing, prioritize editing based on when the video was shot. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editorial department, During editing, adjust the editing order based on the relevance of the videos. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates user emotions and adjusts viewer responses and data analysis methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During analysis, past viewer data is referenced to predict current viewer reactions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, different analysis methods are applied to each video category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates user emotions and adjusts viewer responses and data importance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During the analysis, we analyze how viewer reactions change based on when the video was posted. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, we refer to relevant market data for the video to analyze viewer reactions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned revenue-generating section is, We estimate user sentiment and adjust monetization and donation features based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned revenue-generating section is, When providing monetization or donation features, the system will refer to the user's past revenue history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned revenue-generating section is, When providing monetization and donation features, customize the methods of provision based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned revenue-generating section is, It estimates user sentiment and prioritizes monetization and donation features based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned revenue-generating section is, When providing monetization or donation features, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned revenue-generating section is, When providing monetization or donation features, we analyze users' social media activity and suggest appropriate methods for offering these features. 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 reception desk takes pet videos and uploads them to the platform, The aforementioned reception unit analyzes the uploaded videos and detects and removes personal information and backgrounds that the user does not want to show, The editing unit automatically edits the video analyzed by the aforementioned analysis unit and automatically adds music, narration, and subtitles. The aforementioned editorial department analyzes viewer reactions and data from the videos it edits, and proposes content for the next episode. The system comprises a revenue unit that provides monetization and donation functions based on the data obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Detects and removes personal information and unwanted backgrounds from videos. The system according to feature 1.

3. The aforementioned editorial department, Analyzes pet videos, automatically edits them, and automatically adds music, narration, and subtitles. The system according to feature 1.

4. The aforementioned editorial department, Automatically generates narration that learns from the perspective of pets and user feedback. The system according to feature 1.

5. The aforementioned analysis unit is We analyze viewer reactions and data to suggest content for the next episode. The system according to feature 1.

6. The aforementioned revenue-generating section is, Provides monetization and donation features. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the video upload timing based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past video upload history and select the optimal upload method. The system according to feature 1.

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