System

Abstract

An object of a system according to an embodiment is to automatically create a cut-out moving image at low cost.SOLUTION: A system includes a comment analysis unit, an emotion analysis unit, a content analysis unit, and a cutout generation unit. The comment analysis unit analyzes the comment field. The emotion analysis unit analyzes the emotion of the distributor based on the data in the comment field analyzed by the comment analysis unit. The detail analysis unit analyzes details of the content on the basis of the emotion data of the distributor analyzed by the emotion analysis unit. The cutout generation unit generates a cutout moving image on the basis of content data of the content analyzed by the content analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques require manual work to create clipped videos, which is costly.

[0005] The system according to the embodiment aims to automatically create a cut-out video at low cost. [Means for solving the problem]

[0006] The system according to the embodiment includes a comment analysis unit, an emotion analysis unit, a content analysis unit, and a clip generation unit. The comment analysis unit analyzes the comment section. The emotion analysis unit analyzes the emotions of the distributor based on the data of the comment section analyzed by the comment analysis unit. The content analysis unit analyzes the content based on the emotion data of the distributor analyzed by the emotion analysis unit. The clip generation unit generates a clip video based on the content data of the content analyzed by the content analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create a cut-out video at low cost. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The cutout video creation system according to the embodiment of the present invention is a system that automatically creates cutout videos by taking into consideration the comment section, the emotions of the distributor, and the content of the content. As a result, the cutout video creation system can provide cutout videos efficiently at low cost.

[0029] A clip video creation system according to an embodiment includes a comment analysis unit, an emotion analysis unit, a content analysis unit, and a clip generation unit. The comment analysis unit analyzes the comment section. For example, the comment analysis unit analyzes text data in the comment section of a video to identify parts that viewers are particularly interested in or parts that receive a lot of responses. The comment analysis unit can also analyze the timeline of comments and identify highlights based on comments concentrated in a specific time period. The comment analysis unit can also perform emotion analysis of comments and prioritize parts with a lot of positive responses as clipping candidates. The emotion analysis unit analyzes the emotions of the streamer based on the comment section data analyzed by the comment analysis unit. For example, the emotion analysis unit analyzes audio data and video data of the streamer to detect changes in the streamer's emotions. The emotion analysis unit can also analyze changes in the streamer's voice tone and identify parts where emotions are particularly high as clipping candidates. The emotion analysis unit can also analyze changes in the streamer's facial expressions in detail to identify parts where smiling or surprised expressions are common. The content analysis unit analyzes the content based on the streamer's emotion data analyzed by the emotion analysis unit. For example, the content analysis unit analyzes text data of a video and identifies portions that contain many particularly important keywords. The content analysis unit can also analyze the viewing time for each scene of the video and identify portions with long viewing times as clipping candidates. The content analysis unit can also use an emotion estimation function to analyze the viewer's emotional response to the content of the video and identify scenes that are emotionally charged. The clip generation unit generates clipped videos based on the content data of the content analyzed by the content analysis unit. For example, the clip generation unit can improve the clipped video generation algorithm and select optimal clipping points based on the viewer's viewing history. The clip generation unit can also reflect viewer feedback in real time when generating clipped videos and perform optimal editing. The clip generation unit can also use the emotion estimation function to dynamically change the editing points of the clipped videos based on the viewer's emotional response. This allows the clipped video creation system according to the embodiment to provide clipped videos efficiently and at low cost.For example, by automatically cutting out scenes that viewers are particularly interested in, scenes that make the streamer feel more emotional, and important parts of the story, it is possible to provide content that is valuable to viewers.

[0030] The comment analysis unit analyzes the timeline of comments and can identify highlights based on comments that are concentrated in a particular time period. The comment analysis unit, for example, analyzes the timeline of the comments section of a video and extracts comments that are concentrated in a particular time period. For example, if many comments are concentrated on a particular scene, the comment analysis unit identifies that scene as a highlight. The comment analysis unit also analyzes comments that are concentrated in a particular time period based on the time the comments were posted and identifies scenes that viewers are particularly paying attention to. For example, it selects a time period when the number of comments increases sharply as a highlight. The comment analysis unit also analyzes the timeline of comments and identifies highlights based on comments that are concentrated in a particular time period. For example, if many comments are concentrated on a particular scene, the scene is identified as a highlight. This makes it possible to identify scenes that viewers are particularly paying attention to.

[0031] The comment analysis unit can compare the analysis results of the comment section with comment data from other video platforms to identify common highlights. The comment analysis unit, for example, compares the analysis results of the comment section with comment data from other video platforms to identify common highlights. For example, if many comments are concentrated on the same scene on multiple platforms, the scene is identified as a highlight. The comment analysis unit also collects comment data from other video platforms and compares the analysis results to identify common highlights. For example, if many positive comments are concentrated on the same scene on different platforms, the scene is selected as a highlight. The comment analysis unit also compares the analysis results of the comment section with comment data from other video platforms to identify common highlights. For example, if many comments are concentrated on the same scene on multiple platforms, the scene is identified as a highlight. This makes it possible to identify common highlights across multiple platforms.

[0032] The comment analysis unit can automatically translate the language of the comments and analyze comments in different languages. The comment analysis unit, for example, automatically translates the language of the comments and analyzes comments in different languages. For example, comments in English or Chinese are translated into Japanese and analyzed. The comment analysis unit also uses an automatic translation function to analyze comments in different languages. For example, if viewers leave comments in multiple languages, all of these are translated and analyzed. The comment analysis unit also automatically translates the language of the comments and analyzes comments in different languages. For example, comments in English or Chinese are translated into Japanese and analyzed. In this way, by analyzing comments in multiple languages, it is possible to reflect the reactions of a wider range of viewers.

[0033] The emotion analysis unit can compare the emotion analysis results of a streamer with the data of other streamers to identify common emotional peaks. For example, the emotion analysis unit compares the emotion analysis results of a streamer with the data of other streamers to identify common emotional peaks. For example, if multiple streamers become emotional in the same scene, the scene is identified as a highlight. The emotion analysis unit also collects emotion data from other streamers and compares the analysis results to identify common emotional peaks. For example, if different streamers become emotional in the same scene, the scene is selected as a highlight. The emotion analysis unit also compares the emotion analysis results of a streamer with the data of other streamers to identify common emotional peaks. For example, if multiple streamers become emotional in the same scene, the scene is identified as a highlight. In this way, common emotional peaks among multiple streamers can be identified.

[0034] The emotion analysis unit visualizes the emotional changes of the streamer, allowing viewers to intuitively understand the flow of emotions. The emotion analysis unit, for example, visualizes the emotional changes of the streamer, allowing viewers to intuitively understand the flow of emotions. For example, it displays emotion scores in a graph or chart. The emotion analysis unit also visualizes the emotion analysis results, allowing viewers to intuitively understand the emotional changes. For example, it highlights emotional peaks. The emotion analysis unit also visualizes the emotional changes of the streamer, allowing viewers to intuitively understand the flow of emotions. For example, it displays emotion scores in a graph or chart. This allows viewers to intuitively understand the flow of emotions of the streamer.

[0035] The content analysis unit can analyze the text data of a video and identify parts that contain many particularly important keywords. The content analysis unit, for example, analyzes the text data of a video and identifies parts that contain many particularly important keywords. For example, it extracts scenes that include the climax of the story or important statements. The content analysis unit also uses text analysis technology to analyze the content of the video and identify parts that contain many important keywords. For example, it selects scenes in which keywords related to a specific topic frequently appear as highlights. The content analysis unit also analyzes the text data of a video and identifies parts that contain many particularly important keywords. For example, it extracts scenes that include the climax of the story or important statements. This makes it possible to identify important parts of the video.

[0036] The content analysis unit can analyze the viewing time for each scene of the video and set parts with long viewing times as cutout candidates. The content analysis unit, for example, analyzes the viewing time for each scene of the video and identifies parts with long viewing times. For example, it selects scenes that viewers watch for long periods of time as highlights. The content analysis unit also analyzes viewing data and identifies scenes with long viewing times. For example, it selects scenes that viewers frequently pause or rewind as highlights. The content analysis unit also analyzes the viewing time for each scene of the video and sets parts with long viewing times as cutout candidates. For example, it selects scenes that viewers watch for long periods of time as highlights. This makes it possible to identify scenes that viewers watch for long periods of time.

[0037] The content analysis unit can compare the content analysis results of a video with other video content and identify common important scenes. The content analysis unit, for example, compares the content analysis results of a video with other video content and identifies common important scenes. For example, if the same scene is considered important in multiple videos, the scene is identified as a highlight. The content analysis unit also collects and compares the analysis results of other video content to identify common important scenes. For example, if the same topic is considered important in different videos, the scene is selected as a highlight. The content analysis unit also compares the content analysis results of a video with other video content and identifies common important scenes. For example, if the same scene is considered important in multiple videos, the scene is identified as a highlight. This makes it possible to identify common important scenes in multiple videos.

[0038] The content analysis unit can compare the content of a video with content of different genres and discover new highlights. For example, the content analysis unit compares the content of a video with content of different genres and discovers new highlights. For example, entertainment videos and educational videos are compared and common highlights are identified. The content analysis unit also analyzes and compares content of different genres to discover new highlights. For example, sports videos and documentary videos are compared and common important scenes are selected as highlights. The content analysis unit also compares the content of a video with content of different genres and discovers new highlights. For example, entertainment videos and educational videos are compared and common highlights are identified. This makes it possible to discover new highlights from content of different genres.

[0039] The clip generation unit improves the clip video generation algorithm and can select optimal clipping points based on the viewer's viewing history. The clip generation unit, for example, improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. The clip generation unit also develops an algorithm for identifying scenes that are likely to be of interest to the viewer based on the viewing history. For example, it prioritizes scenes that the viewer has given high ratings to as clipping candidates. The clip generation unit also improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. This makes it possible to select optimal clipping points based on the viewer's viewing history.

[0040] The clip generation unit can reflect viewer feedback in real time when generating a clipped video and perform optimal editing. The clip generation unit, for example, reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." The clip generation unit also develops an algorithm that dynamically changes the editing points of the clipped video based on real-time viewer feedback. For example, it selects scenes that receive strong viewer reactions as highlights. The clip generation unit also reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." This allows optimal editing by reflecting viewer feedback in real time.

[0041] The clip generation unit can share the results of the clipped video generation with other video platforms and improve the results based on viewer reactions. The clip generation unit, for example, shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. The clip generation unit also analyzes viewing data from other video platforms and improves the results of the clipped video generation. For example, it identifies highlights based on viewer reactions on different platforms. The clip generation unit also shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. This allows the clipped video to be improved based on viewer reactions on different platforms.

[0042] The clip generation unit can apply the clip video generation algorithm to content of different genres to discover new clipping points. The clip generation unit, for example, applies the clip video generation algorithm to content of different genres to discover new clipping points. For example, it identifies highlights common to entertainment videos and educational videos. The clip generation unit can also analyze content of different genres and discover new clipping points by applying the clip video generation algorithm. For example, it can select important scenes common to sports videos and documentary videos as highlights. The clip generation unit can also apply the clip video generation algorithm to content of different genres to discover new clipping points. For example, it can identify highlights common to entertainment videos and educational videos. This allows new clipping points to be discovered from content of different genres.

[0043] The clip generation unit improves the clip video generation algorithm and can select optimal clipping points based on the viewer's viewing history. The clip generation unit, for example, improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. The clip generation unit also develops an algorithm for identifying scenes that are likely to be of interest to the viewer based on the viewing history. For example, it prioritizes scenes that the viewer has given high ratings to as clipping candidates. The clip generation unit also improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. This makes it possible to select optimal clipping points based on the viewer's viewing history.

[0044] The clip generation unit can reflect viewer feedback in real time when generating a clipped video and perform optimal editing. The clip generation unit, for example, reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." The clip generation unit also develops an algorithm that dynamically changes the editing points of the clipped video based on real-time viewer feedback. For example, it selects scenes that receive strong viewer reactions as highlights. The clip generation unit also reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." This allows optimal editing by reflecting viewer feedback in real time.

[0045] The clip generation unit can share the results of the clipped video generation with other video platforms and improve the results based on viewer reactions. The clip generation unit, for example, shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. The clip generation unit also analyzes viewing data from other video platforms and improves the results of the clipped video generation. For example, it identifies highlights based on viewer reactions on different platforms. The clip generation unit also shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. This allows the clipped video to be improved based on viewer reactions on different platforms.

[0046] The clip generation unit can apply the clip video generation algorithm to content of different genres to discover new clipping points. The clip generation unit, for example, applies the clip video generation algorithm to content of different genres to discover new clipping points. For example, it identifies highlights common to entertainment videos and educational videos. The clip generation unit can also analyze content of different genres and discover new clipping points by applying the clip video generation algorithm. For example, it can select important scenes common to sports videos and documentary videos as highlights. The clip generation unit can also apply the clip video generation algorithm to content of different genres to discover new clipping points. For example, it can identify highlights common to entertainment videos and educational videos. This allows new clipping points to be discovered from content of different genres.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The cut-out video creation system can further include a viewing history analysis unit that analyzes the user's viewing history. The viewing history analysis unit analyzes data on videos the user has previously viewed and identifies scenes that the user is likely to be particularly interested in. For example, scenes that the user has previously given high ratings or scenes that have been played many times can be prioritized as cut-out candidates. The viewing history analysis unit can also analyze the user's viewing patterns and select scenes related to a specific genre or theme as highlights. This makes it possible to provide more personalized cut-out videos based on the user's viewing history.

[0049] The cutout video creation system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects comments and ratings on cutout videos viewed by users and analyzes the data. For example, a section where a user commented, "This scene was good," may be prioritized as a cutout candidate. The feedback collection unit may also analyze user evaluation scores and select highly rated scenes as highlights. This makes it possible to provide cutout videos that reflect user feedback.

[0050] The cut-out video creation system can further include a genre analysis unit that analyzes content of different genres. The genre analysis unit analyzes video content of different genres and identifies common highlights. For example, it can compare entertainment videos and educational videos and select important scenes that they share as highlights. The genre analysis unit can also analyze content of different genres and discover new cut-out points. This allows new highlights to be discovered from content of different genres, increasing the variety of cut-out videos.

[0051] The cut-out video creation system may further include a data collection unit that collects data from other video platforms. The data collection unit collects and analyzes comment data and viewing data from other video platforms. For example, if many comments are concentrated on the same scene across multiple platforms, that scene can be identified as a highlight. The data collection unit can also analyze viewing data from other platforms to identify common highlights. This allows common highlights across multiple platforms to be identified, improving the accuracy of cut-out videos.

[0052] The cut-out video creation system can further include an environment analysis unit that analyzes the user's viewing environment. The environment analysis unit analyzes the user's viewing device and network environment and provides the optimal cut-out video. For example, a user viewing on a mobile device can be provided with a cut-out video that can be viewed in a short time, while a user viewing on a desktop device can be provided with a longer cut-out video. The environment analysis unit can also analyze the user's network speed and select the optimal video quality with minimal buffering. This makes it possible to provide the optimal cut-out video according to the user's viewing environment.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The comment analysis unit analyzes the comment section. For example, the comment analysis unit analyzes the text data in the comment section of a video to identify parts that viewers are particularly paying attention to or that have received a lot of responses. The comment analysis unit can also analyze the timeline of comments and identify highlights based on comments that are concentrated in a particular time period. Furthermore, it can perform a sentiment analysis of the comments and prioritize parts that have received a lot of positive responses as candidates for cutting. Step 2: The emotion analysis unit analyzes the streamer's emotions based on the data in the comment section analyzed by the comment analysis unit. For example, the emotion analysis unit analyzes the streamer's audio and video data to detect changes in the streamer's emotions. It can also analyze changes in the streamer's tone of voice and select parts where emotions are particularly high as candidates for clipping. It can also analyze changes in the streamer's facial expressions in detail to identify parts where smiling or surprised expressions are common. Step 3: The content analysis unit analyzes the content based on the distributor's emotional data analyzed by the emotion analysis unit. For example, the content analysis unit analyzes the text data of the video and identifies parts that contain many particularly important keywords. It can also analyze the viewing time for each scene in the video and identify parts with long viewing times as candidates for clipping. Furthermore, it can use the emotion estimation function to analyze the viewer's emotional response to the video content and identify scenes that are likely to heighten emotions. Step 4: The clip generation unit generates a clipped video based on the content data analyzed by the content analysis unit. For example, the clip generation unit can improve the clip generation algorithm and select optimal clipping points based on the viewer's viewing history. It can also reflect viewer feedback in real time when generating a clipped video to optimize editing. Furthermore, it can use an emotion estimation function to dynamically change the editing points of the clipped video based on the viewer's emotional response.

[0055] (Example 2) The cutout video creation system according to the embodiment of the present invention is a system that automatically creates cutout videos by taking into consideration the comment section, the emotions of the distributor, and the content of the content. As a result, the cutout video creation system can provide cutout videos efficiently at low cost.

[0056] A clip video creation system according to an embodiment includes a comment analysis unit, an emotion analysis unit, a content analysis unit, and a clip generation unit. The comment analysis unit analyzes the comment section. For example, the comment analysis unit analyzes text data in the comment section of a video to identify parts that viewers are particularly interested in or parts that receive a lot of responses. The comment analysis unit can also analyze the timeline of comments and identify highlights based on comments concentrated in a specific time period. The comment analysis unit can also perform emotion analysis of comments and prioritize parts with a lot of positive responses as clipping candidates. The emotion analysis unit analyzes the emotions of the streamer based on the comment section data analyzed by the comment analysis unit. For example, the emotion analysis unit analyzes audio data and video data of the streamer to detect changes in the streamer's emotions. The emotion analysis unit can also analyze changes in the streamer's voice tone and identify parts where emotions are particularly high as clipping candidates. The emotion analysis unit can also analyze changes in the streamer's facial expressions in detail to identify parts where smiling or surprised expressions are common. The content analysis unit analyzes the content based on the streamer's emotion data analyzed by the emotion analysis unit. For example, the content analysis unit analyzes text data of a video and identifies portions that contain many particularly important keywords. The content analysis unit can also analyze the viewing time for each scene of the video and identify portions with long viewing times as clipping candidates. The content analysis unit can also use an emotion estimation function to analyze the viewer's emotional response to the content of the video and identify scenes that are emotionally charged. The clip generation unit generates clipped videos based on the content data of the content analyzed by the content analysis unit. For example, the clip generation unit can improve the clipped video generation algorithm and select optimal clipping points based on the viewer's viewing history. The clip generation unit can also reflect viewer feedback in real time when generating clipped videos and perform optimal editing. The clip generation unit can also use the emotion estimation function to dynamically change the editing points of the clipped videos based on the viewer's emotional response. This allows the clipped video creation system according to the embodiment to provide clipped videos efficiently and at low cost.For example, by automatically cutting out scenes that viewers are particularly interested in, scenes that make the streamer feel more emotional, and important parts of the story, it is possible to provide content that is valuable to viewers.

[0057] The comment analysis unit analyzes the timeline of comments and can identify highlights based on comments that are concentrated in a particular time period. The comment analysis unit, for example, analyzes the timeline of the comments section of a video and extracts comments that are concentrated in a particular time period. For example, if many comments are concentrated on a particular scene, the comment analysis unit identifies that scene as a highlight. The comment analysis unit also analyzes comments that are concentrated in a particular time period based on the time the comments were posted and identifies scenes that viewers are particularly paying attention to. For example, it selects a time period when the number of comments increases sharply as a highlight. The comment analysis unit also analyzes the timeline of comments and identifies highlights based on comments that are concentrated in a particular time period. For example, if many comments are concentrated on a particular scene, the scene is identified as a highlight. This makes it possible to identify scenes that viewers are particularly paying attention to.

[0058] The comment analysis unit performs sentiment analysis of the comments and can prioritize parts with many positive reactions as cutout candidates. The comment analysis unit, for example, performs sentiment analysis of the comments and identifies parts with many positive reactions. For example, scenes where viewers have left many positive comments such as "interesting" or "moving" are selected as cutout candidates. The comment analysis unit also analyzes sentiment scores of the comments and prioritizes parts with many positive reactions as cutout candidates. For example, scenes with high sentiment scores are selected as highlights. The comment analysis unit also performs sentiment analysis of the comments and prioritizes parts with many positive reactions as cutout candidates. For example, scenes where viewers have left many positive comments such as "interesting" or "moving" are selected as cutout candidates. This makes it possible to prioritize cutout scenes that are valuable to viewers.

[0059] The comment analysis unit can use the emotion estimation function to analyze the emotions of comments and select parts where emotions are particularly high as cutout candidates. The comment analysis unit, for example, uses the emotion estimation function to analyze the emotions of comments and identify parts where emotions are particularly high. For example, scenes where there are many comments expressing strong emotions from viewers, such as "This is great!" or "I cried," are selected as cutout candidates. The comment analysis unit also analyzes the emotion scores of comments and preferentially selects parts where emotions are particularly high as cutout candidates. For example, scenes where the emotion score rises sharply are selected as highlights. The comment analysis unit also uses the emotion estimation function to analyze the emotions of comments and select parts where emotions are particularly high as cutout candidates. For example, scenes where there are many comments expressing strong emotions from viewers, such as "This is great!" or "I cried," are selected as cutout candidates. This makes it possible to effectively cut out scenes where viewers' emotions are high.

[0060] The comment analysis unit can compare the analysis results of the comment section with comment data from other video platforms to identify common highlights. The comment analysis unit, for example, compares the analysis results of the comment section with comment data from other video platforms to identify common highlights. For example, if many comments are concentrated on the same scene on multiple platforms, the scene is identified as a highlight. The comment analysis unit also collects comment data from other video platforms and compares the analysis results to identify common highlights. For example, if many positive comments are concentrated on the same scene on different platforms, the scene is selected as a highlight. The comment analysis unit also compares the analysis results of the comment section with comment data from other video platforms to identify common highlights. For example, if many comments are concentrated on the same scene on multiple platforms, the scene is identified as a highlight. This makes it possible to identify common highlights across multiple platforms.

[0061] The comment analysis unit can automatically translate the language of the comments and analyze comments in different languages. The comment analysis unit, for example, automatically translates the language of the comments and analyzes comments in different languages. For example, comments in English or Chinese are translated into Japanese and analyzed. The comment analysis unit also uses an automatic translation function to analyze comments in different languages. For example, if viewers leave comments in multiple languages, all of these are translated and analyzed. The comment analysis unit also automatically translates the language of the comments and analyzes comments in different languages. For example, comments in English or Chinese are translated into Japanese and analyzed. In this way, by analyzing comments in multiple languages, it is possible to reflect the reactions of a wider range of viewers.

[0062] The comment analysis unit can use the emotion estimation function to analyze the emotions of comments in real time and dynamically change clipping candidates in accordance with changes in the viewer's emotions. The comment analysis unit, for example, uses the emotion estimation function to analyze the emotions of comments in real time and dynamically change clipping candidates in accordance with changes in the viewer's emotions. For example, it selects scenes in which the viewer's emotions change drastically as highlights. The comment analysis unit also analyzes the emotion scores of comments in real time and dynamically change clipping candidates in accordance with changes in the viewer's emotions. For example, it identifies scenes in which the emotion scores increase rapidly as highlights. The comment analysis unit also uses the emotion estimation function to analyze the emotions of comments in real time and dynamically change clipping candidates in accordance with changes in the viewer's emotions. For example, it selects scenes in which the viewer's emotions change drastically as highlights. This makes it possible to dynamically change clipping candidates in accordance with changes in the viewer's emotions.

[0063] The emotion analysis unit analyzes changes in the streamer's voice tone and can designate parts where emotions are particularly high as cutout candidates. The emotion analysis unit, for example, analyzes changes in the streamer's voice tone and identifies parts where emotions are particularly high. For example, scenes where the streamer is excited or moved are designated as cutout candidates. The emotion analysis unit also uses audio analysis technology to detect changes in the streamer's voice tone and identify parts where emotions are particularly high. For example, it analyzes changes in the pitch and volume of the voice. The emotion analysis unit also analyzes changes in the streamer's voice tone and can designate parts where emotions are particularly high as cutout candidates. For example, scenes where the streamer is excited or moved are designated as cutout candidates. This makes it possible to effectively cut out scenes where emotions are particularly high in the streamer.

[0064] The emotion analysis unit can analyze the streamer's facial expression changes in detail and identify parts where smiling or surprised expressions are common. The emotion analysis unit, for example, analyzes the streamer's facial expression in detail and identifies parts where smiling or surprised expressions are common. For example, it uses facial recognition technology to analyze the streamer's facial expressions. The emotion analysis unit also uses facial expression analysis technology to identify parts where smiling or surprised expressions are common. For example, it analyzes facial expression changes in real time. The emotion analysis unit also analyzes the streamer's facial expression in detail and identifies parts where smiling or surprised expressions are common. For example, it uses facial recognition technology to analyze the streamer's facial expressions. In this way, by analyzing the streamer's facial expression changes in detail, it is possible to identify scenes that are valuable to viewers.

[0065] The emotion analysis unit can use the emotion estimation function to analyze the emotions of the streamer in real time and select emotional peaks as cutout candidates. The emotion analysis unit, for example, uses the emotion estimation function to analyze the emotions of the streamer in real time and identify emotional peaks. For example, scenes in which the streamer is most emotional are selected as cutout candidates. The emotion analysis unit also analyzes the streamer's emotion score in real time and selects emotional peaks as cutout candidates. For example, scenes with the highest emotion score are selected as highlights. The emotion analysis unit also uses the emotion estimation function to analyze the streamer's emotions in real time and select emotional peaks as cutout candidates. For example, scenes in which the streamer is most emotional are selected as cutout candidates. This makes it possible to effectively cut out the emotional peaks of the streamer.

[0066] The emotion analysis unit can compare the emotion analysis results of a streamer with the data of other streamers to identify common emotional peaks. For example, the emotion analysis unit compares the emotion analysis results of a streamer with the data of other streamers to identify common emotional peaks. For example, if multiple streamers become emotional in the same scene, the scene is identified as a highlight. The emotion analysis unit also collects emotion data from other streamers and compares the analysis results to identify common emotional peaks. For example, if different streamers become emotional in the same scene, the scene is selected as a highlight. The emotion analysis unit also compares the emotion analysis results of a streamer with the data of other streamers to identify common emotional peaks. For example, if multiple streamers become emotional in the same scene, the scene is identified as a highlight. In this way, common emotional peaks among multiple streamers can be identified.

[0067] The emotion analysis unit visualizes the emotional changes of the streamer, allowing viewers to intuitively understand the flow of emotions. The emotion analysis unit, for example, visualizes the emotional changes of the streamer, allowing viewers to intuitively understand the flow of emotions. For example, it displays emotion scores in a graph or chart. The emotion analysis unit also visualizes the emotion analysis results, allowing viewers to intuitively understand the emotional changes. For example, it highlights emotional peaks. The emotion analysis unit also visualizes the emotional changes of the streamer, allowing viewers to intuitively understand the flow of emotions. For example, it displays emotion scores in a graph or chart. This allows viewers to intuitively understand the flow of emotions of the streamer.

[0068] The emotion analysis unit uses the emotion estimation function to provide feedback on the streamer's emotional changes to viewers in real time and dynamically change the clipping candidates based on the viewer's reactions. The emotion analysis unit, for example, uses the emotion estimation function to provide feedback on the streamer's emotional changes to viewers in real time and dynamically change the clipping candidates based on the viewer's reactions. For example, it selects scenes that receive strong viewers' reactions as highlights. The emotion analysis unit also feeds back the streamer's emotional score to viewers in real time and dynamically changes the clipping candidates based on the viewer's reactions. For example, it identifies scenes that receive a sudden increase in viewer reactions as highlights. The emotion analysis unit also uses the emotion estimation function to provide feedback on the streamer's emotional changes to viewers in real time and dynamically change the clipping candidates based on the viewer's reactions. For example, it selects scenes that receive strong viewers' reactions as highlights. This makes it possible to dynamically change the clipping candidates according to the viewer's reactions.

[0069] The content analysis unit can analyze the text data of a video and identify parts that contain many particularly important keywords. The content analysis unit, for example, analyzes the text data of a video and identifies parts that contain many particularly important keywords. For example, it extracts scenes that include the climax of the story or important statements. The content analysis unit also uses text analysis technology to analyze the content of the video and identify parts that contain many important keywords. For example, it selects scenes in which keywords related to a specific topic frequently appear as highlights. The content analysis unit also analyzes the text data of a video and identifies parts that contain many particularly important keywords. For example, it extracts scenes that include the climax of the story or important statements. This makes it possible to identify important parts of the video.

[0070] The content analysis unit can analyze the viewing time for each scene of the video and set parts with long viewing times as cutout candidates. The content analysis unit, for example, analyzes the viewing time for each scene of the video and identifies parts with long viewing times. For example, it selects scenes that viewers watch for long periods of time as highlights. The content analysis unit also analyzes viewing data and identifies scenes with long viewing times. For example, it selects scenes that viewers frequently pause or rewind as highlights. The content analysis unit also analyzes the viewing time for each scene of the video and sets parts with long viewing times as cutout candidates. For example, it selects scenes that viewers watch for long periods of time as highlights. This makes it possible to identify scenes that viewers watch for long periods of time.

[0071] The content analysis unit can use the emotion estimation function to analyze the viewer's emotional response to the content of the video and identify scenes that heighten emotions. The content analysis unit, for example, uses the emotion estimation function to analyze the viewer's emotional response to the content of the video and identify scenes that heighten emotions. For example, it selects scenes in which the viewer expresses strong emotions such as being "moved" or "surprised" as highlights. The content analysis unit also analyzes the viewer's emotion score and identifies scenes that heighten emotions. For example, it identifies scenes in which the emotion score rises sharply as highlights. The content analysis unit also uses the emotion estimation function to analyze the viewer's emotional response to the content of the video and identify scenes that heighten emotions. For example, it selects scenes in which the viewer expresses strong emotions such as being "moved" or "surprised" as highlights. In this way, it is possible to identify scenes that heighten emotions of the viewer.

[0072] The content analysis unit can compare the content analysis results of a video with other video content and identify common important scenes. The content analysis unit, for example, compares the content analysis results of a video with other video content and identifies common important scenes. For example, if the same scene is considered important in multiple videos, the scene is identified as a highlight. The content analysis unit also collects and compares the analysis results of other video content to identify common important scenes. For example, if the same topic is considered important in different videos, the scene is selected as a highlight. The content analysis unit also compares the content analysis results of a video with other video content and identifies common important scenes. For example, if the same scene is considered important in multiple videos, the scene is identified as a highlight. This makes it possible to identify common important scenes in multiple videos.

[0073] The content analysis unit can compare the content of a video with content of different genres and discover new highlights. For example, the content analysis unit compares the content of a video with content of different genres and discovers new highlights. For example, entertainment videos and educational videos are compared and common highlights are identified. The content analysis unit also analyzes and compares content of different genres to discover new highlights. For example, sports videos and documentary videos are compared and common important scenes are selected as highlights. The content analysis unit also compares the content of a video with content of different genres and discovers new highlights. For example, entertainment videos and educational videos are compared and common highlights are identified. This makes it possible to discover new highlights from content of different genres.

[0074] The content analysis unit can use the emotion estimation function to analyze the viewer's emotional response to the content of the video in real time and select emotional peaks as cutout candidates. The content analysis unit, for example, uses the emotion estimation function to analyze the viewer's emotional response to the content of the video in real time and identify emotional peaks. For example, it selects scenes in which the viewer is most emotional as cutout candidates. The content analysis unit also analyzes the viewer's emotion scores in real time and selects emotional peaks as cutout candidates. For example, it identifies scenes with the highest emotion scores as highlights. The content analysis unit also uses the emotion estimation function to analyze the viewer's emotional response to the content of the video in real time and selects emotional peaks as cutout candidates. For example, it selects scenes in which the viewer is most emotional as cutout candidates. This makes it possible to effectively cut out the viewer's emotional peaks.

[0075] The clip generation unit improves the clip video generation algorithm and can select optimal clipping points based on the viewer's viewing history. The clip generation unit, for example, improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. The clip generation unit also develops an algorithm for identifying scenes that are likely to be of interest to the viewer based on the viewing history. For example, it prioritizes scenes that the viewer has given high ratings to as clipping candidates. The clip generation unit also improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. This makes it possible to select optimal clipping points based on the viewer's viewing history.

[0076] The clip generation unit can reflect viewer feedback in real time when generating a clipped video and perform optimal editing. The clip generation unit, for example, reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." The clip generation unit also develops an algorithm that dynamically changes the editing points of the clipped video based on real-time viewer feedback. For example, it selects scenes that receive strong viewer reactions as highlights. The clip generation unit also reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." This allows optimal editing by reflecting viewer feedback in real time.

[0077] The clip generation unit can use the emotion estimation function to dynamically change the editing points of the clipped video based on the emotional response of the viewer. The clip generation unit, for example, uses the emotion estimation function to dynamically change the editing points of the clipped video based on the emotional response of the viewer. For example, it prioritizes cutting out scenes in which the viewer is most emotional. The clip generation unit also analyzes the viewer's emotion score in real time and selects emotional peaks as clipping candidates. For example, it identifies scenes with the highest emotion score as highlights. The clip generation unit also uses the emotion estimation function to dynamically change the editing points of the clipped video based on the emotional response of the viewer. For example, it prioritizes cutting out scenes in which the viewer is most emotional. This makes it possible to dynamically change the editing points based on the viewer's emotional response.

[0078] The clip generation unit can share the results of the clipped video generation with other video platforms and improve the results based on viewer reactions. The clip generation unit, for example, shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. The clip generation unit also analyzes viewing data from other video platforms and improves the results of the clipped video generation. For example, it identifies highlights based on viewer reactions on different platforms. The clip generation unit also shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. This allows the clipped video to be improved based on viewer reactions on different platforms.

[0079] The clip generation unit can apply the clip video generation algorithm to content of different genres to discover new clipping points. The clip generation unit, for example, applies the clip video generation algorithm to content of different genres to discover new clipping points. For example, it identifies highlights common to entertainment videos and educational videos. The clip generation unit can also analyze content of different genres and discover new clipping points by applying the clip video generation algorithm. For example, it can select important scenes common to sports videos and documentary videos as highlights. The clip generation unit can also apply the clip video generation algorithm to content of different genres to discover new clipping points. For example, it can identify highlights common to entertainment videos and educational videos. This allows new clipping points to be discovered from content of different genres.

[0080] The clip generation unit uses the emotion estimation function to analyze the viewer's emotional response to the generated clip video in real time and perform optimal editing. The clip generation unit, for example, uses the emotion estimation function to analyze the viewer's emotional response to the generated clip video in real time and perform optimal editing. For example, it prioritizes cutting out scenes in which the viewer is most emotional. The clip generation unit also analyzes the viewer's emotion score in real time and selects emotional peaks as clipping candidates. For example, it identifies scenes with the highest emotion score as highlights. The clip generation unit also uses the emotion estimation function to analyze the viewer's emotional response to the generated clip video in real time and perform optimal editing. For example, it prioritizes cutting out scenes in which the viewer is most emotional. This allows optimal editing based on the viewer's emotional response.

[0081] The clip generation unit improves the clip video generation algorithm and can select optimal clipping points based on the viewer's viewing history. The clip generation unit, for example, improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. The clip generation unit also develops an algorithm for identifying scenes that are likely to be of interest to the viewer based on the viewing history. For example, it prioritizes scenes that the viewer has given high ratings to as clipping candidates. The clip generation unit also improves the clip video generation algorithm and selects optimal clipping points based on the viewer's viewing history. For example, it analyzes data on videos that the viewer has viewed in the past and selects scenes that are likely to be of interest. This makes it possible to select optimal clipping points based on the viewer's viewing history.

[0082] The clip generation unit can reflect viewer feedback in real time when generating a clipped video and perform optimal editing. The clip generation unit, for example, reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." The clip generation unit also develops an algorithm that dynamically changes the editing points of the clipped video based on real-time viewer feedback. For example, it selects scenes that receive strong viewer reactions as highlights. The clip generation unit also reflects viewer feedback in real time when generating a clipped video and performs optimal editing. For example, it prioritizes cutting out parts where viewers commented, "This scene was good." This allows optimal editing by reflecting viewer feedback in real time.

[0083] The clip generation unit can use the emotion estimation function to dynamically change the editing points of the clipped video based on the emotional response of the viewer. The clip generation unit, for example, uses the emotion estimation function to dynamically change the editing points of the clipped video based on the emotional response of the viewer. For example, it prioritizes cutting out scenes in which the viewer is most emotional. The clip generation unit also analyzes the viewer's emotion score in real time and selects emotional peaks as clipping candidates. For example, it identifies scenes with the highest emotion score as highlights. The clip generation unit also uses the emotion estimation function to dynamically change the editing points of the clipped video based on the emotional response of the viewer. For example, it prioritizes cutting out scenes in which the viewer is most emotional. This makes it possible to dynamically change the editing points based on the viewer's emotional response.

[0084] The clip generation unit can share the results of the clipped video generation with other video platforms and improve the results based on viewer reactions. The clip generation unit, for example, shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. The clip generation unit also analyzes viewing data from other video platforms and improves the results of the clipped video generation. For example, it identifies highlights based on viewer reactions on different platforms. The clip generation unit also shares the results of the clipped video generation with other video platforms and improves the results based on viewer reactions. For example, it collects viewing data on different platforms and adjusts clip points. This allows the clipped video to be improved based on viewer reactions on different platforms.

[0085] The clip generation unit can apply the clip video generation algorithm to content of different genres to discover new clipping points. The clip generation unit, for example, applies the clip video generation algorithm to content of different genres to discover new clipping points. For example, it identifies highlights common to entertainment videos and educational videos. The clip generation unit can also analyze content of different genres and discover new clipping points by applying the clip video generation algorithm. For example, it can select important scenes common to sports videos and documentary videos as highlights. The clip generation unit can also apply the clip video generation algorithm to content of different genres to discover new clipping points. For example, it can identify highlights common to entertainment videos and educational videos. This allows new clipping points to be discovered from content of different genres.

[0086] The clip generation unit uses the emotion estimation function to analyze the viewer's emotional response to the generated clip video in real time and perform optimal editing. The clip generation unit, for example, uses the emotion estimation function to analyze the viewer's emotional response to the generated clip video in real time and perform optimal editing. For example, it prioritizes cutting out scenes in which the viewer is most emotional. The clip generation unit also analyzes the viewer's emotion score in real time and selects emotional peaks as clipping candidates. For example, it identifies scenes with the highest emotion score as highlights. The clip generation unit also uses the emotion estimation function to analyze the viewer's emotional response to the generated clip video in real time and perform optimal editing. For example, it prioritizes cutting out scenes in which the viewer is most emotional. This allows optimal editing based on the viewer's emotional response.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The cut-out video creation system can further include a viewing history analysis unit that analyzes the user's viewing history. The viewing history analysis unit analyzes data on videos the user has previously viewed and identifies scenes that the user is likely to be particularly interested in. For example, scenes that the user has previously given high ratings or scenes that have been played many times can be prioritized as cut-out candidates. The viewing history analysis unit can also analyze the user's viewing patterns and select scenes related to a specific genre or theme as highlights. This makes it possible to provide more personalized cut-out videos based on the user's viewing history.

[0089] The cutout video creation system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects comments and ratings on cutout videos viewed by users and analyzes the data. For example, a section where a user commented, "This scene was good," may be prioritized as a cutout candidate. The feedback collection unit may also analyze user evaluation scores and select highly rated scenes as highlights. This makes it possible to provide cutout videos that reflect user feedback.

[0090] The cut-out video creation system can further include a genre analysis unit that analyzes content of different genres. The genre analysis unit analyzes video content of different genres and identifies common highlights. For example, it can compare entertainment videos and educational videos and select important scenes that they share as highlights. The genre analysis unit can also analyze content of different genres and discover new cut-out points. This allows new highlights to be discovered from content of different genres, increasing the variety of cut-out videos.

[0091] The cut-out video creation system may further include a data collection unit that collects data from other video platforms. The data collection unit collects and analyzes comment data and viewing data from other video platforms. For example, if many comments are concentrated on the same scene across multiple platforms, that scene can be identified as a highlight. The data collection unit can also analyze viewing data from other platforms to identify common highlights. This allows common highlights across multiple platforms to be identified, improving the accuracy of cut-out videos.

[0092] The cut-out video creation system can further include an environment analysis unit that analyzes the user's viewing environment. The environment analysis unit analyzes the user's viewing device and network environment and provides the optimal cut-out video. For example, a user viewing on a mobile device can be provided with a cut-out video that can be viewed in a short time, while a user viewing on a desktop device can be provided with a longer cut-out video. The environment analysis unit can also analyze the user's network speed and select the optimal video quality with minimal buffering. This makes it possible to provide the optimal cut-out video according to the user's viewing environment.

[0093] The cutout video creation system may further include an emotion estimation unit that estimates the user's emotion and selects cutout candidates based on the estimated emotion. The emotion estimation unit analyzes the user's facial expressions and voice while watching to detect changes in emotion. For example, scenes in which the user is smiling or surprised may be selected as cutout candidates. The emotion estimation unit may also analyze the user's emotion score and prioritize parts that are particularly emotional in selecting cutout candidates. This makes it possible to provide cutout videos based on the user's emotion.

[0094] The cut-out video creation system can also analyze the user's emotional changes in real time and select emotional peaks as cut-out candidates. For example, it can identify the scene in which the user is most emotional while watching and select that scene as a cut-out candidate. In addition, by analyzing emotional changes in real time, it can grasp the flow of emotions while watching and select emotional peaks as highlights. This makes it possible to provide cut-out videos that correspond to the user's emotional changes.

[0095] The cut-out video creation system can also compare the results of a user's emotion analysis with data from other users to identify common emotional peaks. For example, if multiple users become emotional in the same scene, that scene can be identified as a highlight. It can also identify common emotional peaks by collecting other users' emotion data and comparing the analysis results. This allows for identifying common emotional peaks across multiple users and improving the accuracy of cut-out videos.

[0096] The cut-out video creation system can also visualize the user's emotional changes, allowing viewers to intuitively understand the flow of emotions. For example, it can display emotion scores in graphs or charts, allowing viewers to intuitively understand the changes in emotions. It can also highlight emotional peaks to emphasize scenes that are particularly important to viewers. This makes it easier for viewers to intuitively understand the flow of emotions of the streamer.

[0097] The cut-out video creation system can also provide feedback on changes in the user's emotions to the viewer in real time and dynamically change the cut-out candidates based on the viewer's reactions. For example, scenes that elicit strong reactions from viewers can be selected as highlights and the cut-out candidates can be changed in real time. The system can also analyze emotion scores in real time and dynamically change the cut-out candidates according to changes in the viewer's emotions. This makes it possible to provide optimal cut-out videos that match the viewer's reactions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The comment analysis unit analyzes the comment section. For example, the comment analysis unit analyzes the text data in the comment section of a video to identify parts that viewers are particularly paying attention to or that have received a lot of responses. The comment analysis unit can also analyze the timeline of comments and identify highlights based on comments that are concentrated in a particular time period. Furthermore, it can perform a sentiment analysis of the comments and prioritize parts that have received a lot of positive responses as candidates for cutting. Step 2: The emotion analysis unit analyzes the streamer's emotions based on the data in the comment section analyzed by the comment analysis unit. For example, the emotion analysis unit analyzes the streamer's audio and video data to detect changes in the streamer's emotions. It can also analyze changes in the streamer's tone of voice and select parts where emotions are particularly high as candidates for clipping. It can also analyze changes in the streamer's facial expressions in detail to identify parts where smiling or surprised expressions are common. Step 3: The content analysis unit analyzes the content based on the distributor's emotional data analyzed by the emotion analysis unit. For example, the content analysis unit analyzes the text data of the video and identifies parts that contain many particularly important keywords. It can also analyze the viewing time for each scene in the video and identify parts with long viewing times as candidates for clipping. Furthermore, it can use the emotion estimation function to analyze the viewer's emotional response to the video content and identify scenes that are likely to heighten emotions. Step 4: The clip generation unit generates a clipped video based on the content data analyzed by the content analysis unit. For example, the clip generation unit can improve the clip generation algorithm and select optimal clipping points based on the viewer's viewing history. It can also reflect viewer feedback in real time when generating a clipped video to optimize editing. Furthermore, it can use an emotion estimation function to dynamically change the editing points of the clipped video based on the viewer's emotional response.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a comment analysis unit that analyzes the comment field; an emotion analysis unit that analyzes emotions of a distributor based on the data in the comment field analyzed by the comment analysis unit; a content analysis unit that analyzes the content based on the emotion data of the distributor analyzed by the emotion analysis unit; a clip generation unit that generates a clip video based on the content data of the content analyzed by the content analysis unit. A system characterized by:

2. The comment analysis unit Analyzing the timeline of comments and identifying the highlights based on comments concentrated in a specific time period 2. The system of claim 1.

3. The comment analysis unit Analyze the sentiment of the comments and prioritize the parts with the most positive reactions as candidates for the clipping.

2. The system of claim 1.

4. The comment analysis unit Analyzing the sentiment of the comments and selecting the part where the sentiment is particularly high as the cutout candidate.

2. The system of claim 1.

5. The comment analysis unit Compare the analysis of the comment section with comment data from other video platforms to identify common points of interest.

2. The system of claim 1.

6. The comment analysis unit The language of the comment is automatically translated, and the comment in a different language is also analyzed.

2. The system of claim 1.

Images