System

Abstract

An object of a system according to an embodiment is to automatically generate a thumbnail of a moving image.SOLUTION: A system includes a reception unit, an analysis unit, and a generation unit. The receiving unit receives a moving image from a user. The analysis unit analyzes the moving image received by the reception unit and specifies a point that attracts a viewer. The generation unit generates an attractive thumbnail on the basis of the point specified by the 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 have had the problem that manually creating thumbnails for videos takes a lot of time and effort.

[0005] The system according to the embodiment aims to automatically generate thumbnails of videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, and a generation unit. The receiving unit receives videos from users. The analysis unit analyzes the videos received by the receiving unit and identifies features that will attract viewers. The generation unit generates attractive thumbnails based on the features identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate thumbnails for videos. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generation AI to analyze videos and images and generate attractive thumbnails that attract viewers. In this system, a user uploads a video, and the generation AI analyzes the video, identifies key features that attract viewers, and generates an attractive thumbnail. For example, the generation AI divides the video into frames, extracts characteristics of each frame, and identifies key features that attract viewers. The generation AI then generates an attractive thumbnail based on the identified features. This system can also be expanded to services that summarize and document footage from fixed cameras, such as surveillance cameras and dashcams. For example, analyzing surveillance camera footage, extracting and summarizing key scenes, enables efficient surveillance. Furthermore, analyzing dashcam footage, extracting and documenting the moment of an accident or key scenes, can be used to investigate the cause of the accident or as evidence. This allows a system using the generation AI to automatically generate attractive thumbnails that attract viewers. For example, for video creators such as YouTubers, video thumbnails play an important role in attracting viewers, and the present invention eliminates this effort. In addition, by summarizing and documenting footage from surveillance cameras and dashcams, it becomes possible to monitor more efficiently and investigate the causes of accidents.

[0029] A video analysis system according to an embodiment includes a receiving unit, an analysis unit, and a generation unit. The receiving unit receives videos from users. Examples of videos from users include, but are not limited to, MP4, AVI, and streaming videos. The receiving unit stores videos uploaded by users on a server. The receiving unit can also receive metadata (e.g., title, description, tags, etc.) for the videos. The analysis unit uses a generation AI to analyze the videos received by the receiving unit and identify points that will attract viewers. The analysis unit, for example, divides the video into frames and extracts features of each frame. For example, the analysis unit can extract important edges within a frame using edge detection technology. The analysis unit can also analyze color distribution within a frame using a color histogram. The analysis unit can also analyze motion between frames using a motion vector. The generation unit uses the generation AI to generate attractive thumbnails based on the points identified by the analysis unit. For example, the generation unit generates thumbnails based on frames containing the identified points. The generation unit can also generate thumbnails taking color and composition into consideration. The generation unit can also generate thumbnails by adding text. This allows the video analysis system according to the embodiment to efficiently analyze a user's videos and generate attractive thumbnails.

[0030] The analysis unit can divide video frames and extract features of each frame. For example, the analysis unit divides video frames at regular time intervals. For example, the analysis unit can divide frames every second. The analysis unit can also divide frames by detecting scene changes. For example, the analysis unit can divide frames at the timing of scene changes using a scene change detection algorithm. The analysis unit extracts features of each divided frame. For example, the analysis unit can extract important edges within a frame using edge detection technology. The analysis unit can also analyze color distribution within a frame using a color histogram. The analysis unit can also analyze movement between frames using a motion vector. As a result, by dividing video frames and extracting features, the accuracy of the analysis is improved.

[0031] The analysis unit can identify points that attract viewers based on the extracted features. The analysis unit can identify points that attract viewers using, for example, eye-tracking data. For example, the analysis unit can analyze the eye-tracking data and identify points that viewers pay attention to. The analysis unit can also identify points that attract viewers using click rates. For example, the analysis unit can analyze click rates for each frame of the video and identify points with high click rates. The analysis unit can also identify points that attract viewers using viewing time. For example, the analysis unit can analyze viewing time for each frame of the video and identify points with long viewing times. By identifying points that attract viewers in this way, it is possible to generate attractive thumbnails.

[0032] The generation unit can generate attractive thumbnails based on the identified points. For example, the generation unit generates thumbnails based on frames of the identified points. For example, the generation unit generates thumbnails by cutting out frames of the identified points. The generation unit can also generate thumbnails taking color and composition into consideration. For example, the generation unit can adjust the color of the thumbnail based on color theory. The generation unit can also generate thumbnails based on composition theory such as the golden ratio or the rule of thirds. The generation unit can also generate thumbnails by adding text. For example, the generation unit can add a catchphrase to the thumbnail that will attract the viewer's interest. In this way, generating thumbnails based on the identified points can attract the viewer's interest.

[0033] The analysis unit can analyze footage from surveillance cameras and dashcams to extract important scenes. For example, the analysis unit analyzes footage from surveillance cameras and extracts important scenes based on the magnitude of movement. For example, the analysis unit uses a motion detection algorithm to extract scenes with large movements as important scenes. The analysis unit can also extract important scenes based on changes in audio. For example, the analysis unit uses audio analysis technology to extract scenes with large changes in audio as important scenes. The analysis unit can also extract important scenes based on specific events. For example, the analysis unit uses face recognition technology to extract scenes in which a specific person appears as important scenes. This enables efficient monitoring and investigation of the causes of accidents by analyzing footage from surveillance cameras and dashcams to extract important scenes.

[0034] The generation unit can summarize and document the extracted important scenes. For example, the generation unit summarizes and documents the extracted important scenes. For example, the generation unit creates a summary by cutting out frames of important scenes. The generation unit can also create a summary by adjusting the length of the scenes. For example, the generation unit creates a summary by shortening the length of important scenes. The generation unit can also create a summary based on an evaluation criterion for importance. For example, the generation unit prioritizes including scenes with high importance in the summary. This enables efficient information management by summarizing and documenting important scenes.

[0035] The reception unit can analyze the user's past video upload history and select the optimal reception method. The reception unit, for example, analyzes the user's past video upload history. For example, the reception unit analyzes time periods in which the user frequently uploaded in the past and prioritizes reception during those time periods. The reception unit can also analyze the genre of videos the user has uploaded in the past and prioritize reception of videos of the same genre. The reception unit can also analyze the user's past upload frequency and suggest a reception method according to the frequency. For example, if the user has uploaded frequently in the past, the reception unit suggests a reception method according to that frequency. In this way, by analyzing the past upload history, the optimal reception method can be provided to the user.

[0036] When receiving videos, the reception unit can filter the videos based on the user's current projects and areas of interest. The reception unit, for example, identifies the user's current projects and areas of interest. For example, the reception unit analyzes the user's profile information and past activity history to identify the current projects and areas of interest. The reception unit can also filter related videos based on keywords set by the user. For example, the reception unit preferentially accepts videos that match the keywords set by the user. In this way, by filtering videos based on the user's projects and areas of interest, highly relevant videos can be preferentially accepted.

[0037] When accepting a video, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the video using voice recognition technology. For example, the acceptance unit uses voice recognition software to automatically analyze the voice and save it as text. Furthermore, if the user selects text input, the acceptance unit can also accept the video using text analysis technology. For example, the acceptance unit uses text analysis software to automatically analyze the text and save it as video metadata. Furthermore, if the user selects image input, the acceptance unit can also accept the video using image recognition technology. For example, the acceptance unit uses image recognition software to automatically analyze the image and save it as a thumbnail of the video. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0038] When receiving videos, the reception unit can preferentially receive highly relevant videos by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit acquires the user's location information using GPS data. The reception unit can also estimate the user's location information using an IP address. The reception unit can also acquire the user's location information using a location information service. The reception unit preferentially receives highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, videos related to that area are preferentially received. If the user is traveling, videos related to the travel destination are preferentially received. If the user is at home, videos related to the area around the user's home are preferentially received. In this way, highly relevant videos can be preferentially received by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and receive related videos when receiving a video. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit preferentially receives new videos related to videos shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and receive related videos. The reception unit can also receive related videos by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant videos.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a video. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit preferentially adopts the reception method for videos that the user has previously rated highly. The reception unit can also avoid the reception method for videos that the user has previously rated poorly. The reception unit can also analyze the user's past feedback and suggest the optimal reception method. For example, the reception unit suggests the optimal reception method for the user based on the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. The analysis unit, for example, evaluates the importance of the video. For example, the analysis unit evaluates the importance of the video based on the number of views or user ratings. The analysis unit can also evaluate the importance of the video based on the urgency of the content. For example, the analysis unit evaluates the importance of a news video based on the urgency. The analysis unit adjusts the level of detail of the analysis based on the importance of the video. For example, for a video with many important scenes, a detailed analysis is performed. Also, for a video with few important scenes, a concise analysis is performed. The depth of the analysis can also be adjusted according to the importance. For example, a more detailed analysis is performed for videos with high importance. In this way, by adjusting the level of detail of the analysis based on the importance of the video, efficient analysis is possible.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. The analysis unit, for example, identifies the category of the video. For example, the analysis unit analyzes metadata of the video (e.g., title, description, tags, etc.) to identify the category of the video. The analysis unit can also analyze the content of the video to identify the category. For example, the analysis unit analyzes the content of the video to identify a category such as entertainment, education, or news. The analysis unit applies different analysis algorithms depending on the category of the video. For example, an analysis algorithm that emphasizes visual elements is applied to entertainment videos. An analysis algorithm that emphasizes comprehension of the content is applied to educational videos. An analysis algorithm that emphasizes accuracy of the information is applied to news videos. In this way, by applying different analysis algorithms depending on the category of the video, more appropriate analysis results can be provided.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, analyzes the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously rated highly and performs a similar analysis. The analysis unit can also perform a different analysis by avoiding analysis results that the user has previously rated poorly. The analysis unit can also analyze the user's past analysis results and suggest an optimal analysis method. For example, the analysis unit suggests an optimal analysis method based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the video. The analysis unit, for example, acquires the time of submission of the video. For example, the analysis unit records the submission date and time and identifies the time of submission. The analysis unit can also record the submission order and identify the time of submission. The analysis unit determines the analysis priority based on the time of submission of the video. For example, the latest video is analyzed first. Older videos are also postponed. The analysis priority can also be adjusted depending on the time of submission. For example, the priority is determined based on the urgency or importance of the time of submission. In this way, by determining the analysis priority based on the time of submission of the video, the latest video can be analyzed first.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. The analysis unit, for example, evaluates the relevance of the videos. For example, the analysis unit evaluates the relevance of the videos based on the similarity of content or the degree of keyword matching. The analysis unit can also evaluate the relevance of the videos based on viewer reactions and click rates. For example, the analysis unit analyzes viewer reaction data to identify highly relevant videos. The analysis unit adjusts the order of analysis based on the relevance of the videos. For example, highly relevant videos are analyzed preferentially. Also, videos with low relevance are postponed. The order of analysis can also be adjusted according to relevance. For example, analysis is performed in order of highest relevance. In this way, by adjusting the order of analysis based on the relevance of the videos, highly relevant videos can be analyzed preferentially.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the level of expertise based on the user's occupation and past learning history. The analysis unit can also evaluate the level of expertise based on the user's profile information and past activity history. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, for a user with high level of expertise, the analysis uses a lot of technical terms. For a user with low level of expertise, the analysis can be performed using simpler language. The use of technical terms in the analysis can also be adjusted according to the user's level of expertise. For example, definitions and explanations of technical terms can be added. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0047] The generation unit can adjust the level of detail of the thumbnail based on the importance of the identified points during generation. The generation unit, for example, evaluates the importance of the identified points. For example, the generation unit evaluates the importance of the points based on viewer reactions and click rates. The generation unit can also evaluate the importance of the points based on viewing time and viewer comments. The generation unit adjusts the level of detail of the thumbnail based on the importance of the identified points. For example, if there are many important points, a detailed thumbnail is generated. On the other hand, if there are few important points, a simple thumbnail is generated. The level of detail of the thumbnail can also be adjusted according to the importance. For example, a more detailed thumbnail is generated for a point with high importance. In this way, by adjusting the level of detail of the thumbnail based on the importance of the identified points, it is possible to generate thumbnails that attract the viewer's interest.

[0048] The generation unit can apply different generation algorithms depending on the category of the video during generation. The generation unit, for example, identifies the category of the video. For example, the generation unit analyzes metadata (e.g., title, description, tags, etc.) of the video to identify the category of the video. The generation unit can also analyze the content of the video to identify the category. For example, the generation unit analyzes the content of the video to identify a category such as entertainment, education, or news. The generation unit applies different generation algorithms depending on the category of the video. For example, an algorithm that generates visually attractive thumbnails is applied to entertainment videos. Furthermore, an algorithm that generates thumbnails that help understand the content is applied to educational videos. Furthermore, an algorithm that generates thumbnails that emphasize accuracy of information is applied to news videos. In this way, by applying different generation algorithms depending on the category of the video, thumbnails that attract the viewer's interest can be generated.

[0049] The generation unit can improve the accuracy of thumbnails by referring to the user's past generation results when generating thumbnails. The generation unit, for example, analyzes the user's past generation results. For example, the generation unit generates similar thumbnails by referring to thumbnails that the user has previously rated highly. The generation unit can also generate different thumbnails by avoiding thumbnails that the user has previously rated poorly. The generation unit can also analyze the user's past generation results and suggest optimal thumbnails. For example, the generation unit suggests optimal thumbnails based on the user's past generation results. In this way, the accuracy of thumbnails is improved by referring to the user's past generation results.

[0050] The generation unit can determine the priority of thumbnails based on the submission times of the identified points when they are generated. The generation unit, for example, acquires the submission times of the identified points. For example, the generation unit records the submission date and time and identifies the submission times. The generation unit can also record the submission order and identify the submission times. The generation unit determines the priority of thumbnails based on the submission times of the identified points. For example, the most recent points are reflected preferentially in the thumbnails. Furthermore, points submitted earlier are postponed. The priority of thumbnails can also be adjusted according to the submission times. For example, the priority is determined based on the urgency or importance of the submission times. In this way, by determining the priority of thumbnails based on the submission times of the identified points, thumbnails that reflect the most recent points can be generated.

[0051] The generation unit can adjust the order of thumbnails based on the relevance of the identified points during generation. The generation unit, for example, evaluates the relevance of the identified points. For example, the generation unit evaluates the relevance of the points based on similarity of content or keyword matching. The generation unit can also evaluate the relevance of the points based on viewer reactions and click rates. For example, the generation unit analyzes viewer reaction data to identify highly relevant points. The generation unit adjusts the order of thumbnails based on the relevance of the identified points. For example, highly relevant points are reflected in the thumbnails preferentially. Furthermore, less relevant points are postponed. The generation unit can also adjust the order of thumbnails according to the relevance. For example, thumbnails are generated in descending order of relevance. In this way, by adjusting the order of thumbnails based on the relevance of the identified points, thumbnails that attract the viewer's interest can be generated.

[0052] The generation unit can adjust the use of technical terms in the thumbnails according to the user's level of expertise when generating the thumbnails. The generation unit, for example, evaluates the user's level of expertise. For example, the generation unit evaluates the user's level of expertise based on the user's occupation and past learning history. The generation unit can also evaluate the user's level of expertise based on the user's profile information and past activity history. The generation unit adjusts the use of technical terms in the thumbnails according to the user's level of expertise. For example, for a user with high level of expertise, the generation unit generates thumbnails that use a lot of technical terms. For a user with low level of expertise, the generation unit can also adjust the use of technical terms in the thumbnails according to the user's level of expertise. For example, definitions and explanations of technical terms can be added. In this way, by adjusting the use of technical terms in the thumbnails according to the user's level of expertise, thumbnails that are easy for the user to understand can be generated.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the user's past viewing history and customize the video reception method based on the viewing history. For example, the reception unit can analyze the genre and viewing time of videos the user has viewed in the past and prioritize receiving videos of similar genres. The reception unit can also analyze the ratings of videos the user has viewed in the past and prioritize receiving videos of genres that have received high ratings. Furthermore, the reception unit can suggest the optimal reception time based on the user's viewing history. This allows for more personalized video reception by taking the user's viewing history into consideration.

[0055] The analysis unit can analyze the audio data of a video and identify points that will attract viewers based on audio characteristics. For example, the analysis unit can analyze changes in the pitch and tone of the audio to identify emotionally emphasized points. The analysis unit can also analyze the rhythm and tempo of the audio to identify parts that will attract viewers' attention. Furthermore, the analysis unit can analyze the content of the audio to identify important points based on specific keywords and phrases. This makes it possible to generate more attractive thumbnails by utilizing audio data to identify points that will attract viewers.

[0056] The generating unit can add visual effects when generating thumbnails based on the identified points. For example, the generating unit can add visually striking effects to frames at the identified points. The generating unit can also generate visually appealing thumbnails by enhancing color or contrast between light and dark. Furthermore, the generating unit can add animation effects to generate moving thumbnails. This makes it possible to generate thumbnails that utilize visual effects to attract the viewer's attention.

[0057] When analyzing video footage from surveillance cameras or dashcams, the analysis unit can also analyze audio data. For example, the analysis unit can synchronize the video and audio from a surveillance camera and identify important scenes based on changes in the audio. The analysis unit can also analyze the video and audio from a dashcam and identify the moment of an accident or important scenes based on the audio data. Furthermore, the analysis unit can use audio analysis technology to extract important scenes based on specific keywords or audio events. This allows for more accurate extraction of important scenes by analyzing both video and audio.

[0058] When summarizing and documenting the extracted important scenes, the generation unit can also summarize the audio data. For example, the generation unit synchronizes the video and audio of important scenes to create a summary. The generation unit can also convert the audio content into text and include it in the summary. Furthermore, the generation unit can extract important parts of the audio and include them in the summary along with the video. This allows for more comprehensive documentation by summarizing both the video and audio.

[0059] The reception unit can analyze the user's past video upload history and select the optimal reception method. For example, the reception unit can analyze the time periods in which the user frequently uploaded in the past and prioritize reception during those time periods. The reception unit can also analyze the genres of videos the user has uploaded in the past and prioritize reception of videos of the same genre. Furthermore, the reception unit can analyze the user's past upload frequency and suggest a reception method based on the frequency. In this way, by analyzing the past upload history, the optimal reception method can be provided to the user.

[0060] The reception unit can customize the method of receiving videos based on the user's current project or area of ​​interest. For example, the reception unit may preferentially receive videos related to the project the user is currently working on. The reception unit may also filter and receive related videos based on the user's area of ​​interest. Furthermore, the reception unit may suggest the optimal reception method depending on the progress of the user's project. This allows for more appropriate video reception based on the user's project or area of ​​interest.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives a video from a user. The video from the user may be, for example, MP4, AVI, streaming video, or the like, but is not limited to these examples. The reception unit, for example, stores the video uploaded by the user on a server. The reception unit may also receive metadata (for example, title, description, tags, etc.) of the video. Step 2: The analysis unit uses the generation AI to analyze the video received by the reception unit and identify points that will attract viewers. For example, the analysis unit divides the video into frames and extracts the characteristics of each frame. For example, the analysis unit uses edge detection technology to extract important edges within the frame. The analysis unit can also analyze the color distribution within the frame using a color histogram. The analysis unit can also analyze the movement between frames using a motion vector. Step 3: The generator uses AI to generate attractive thumbnails based on the points identified by the analyzer. For example, the generator generates thumbnails based on frames of the identified points. The generator can also generate thumbnails taking color and composition into consideration. The generator can also generate thumbnails by adding text.

[0063] (Example 2) A system according to an embodiment of the present invention uses a generation AI to analyze videos and images and generate attractive thumbnails that attract viewers. In this system, a user uploads a video, and the generation AI analyzes the video, identifies key features that attract viewers, and generates an attractive thumbnail. For example, the generation AI divides the video into frames, extracts characteristics of each frame, and identifies key features that attract viewers. The generation AI then generates an attractive thumbnail based on the identified features. This system can also be expanded to services that summarize and document footage from fixed cameras, such as surveillance cameras and dashcams. For example, analyzing surveillance camera footage, extracting and summarizing key scenes, enables efficient surveillance. Furthermore, analyzing dashcam footage, extracting and documenting the moment of an accident or key scenes, can be used to investigate the cause of the accident or as evidence. This allows a system using the generation AI to automatically generate attractive thumbnails that attract viewers. For example, for video creators such as YouTubers, video thumbnails play an important role in attracting viewers, and the present invention eliminates this effort. In addition, by summarizing and documenting footage from surveillance cameras and dashcams, it becomes possible to monitor more efficiently and investigate the causes of accidents.

[0064] A video analysis system according to an embodiment includes a receiving unit, an analysis unit, and a generation unit. The receiving unit receives videos from users. Examples of videos from users include, but are not limited to, MP4, AVI, and streaming videos. The receiving unit stores videos uploaded by users on a server. The receiving unit can also receive metadata (e.g., title, description, tags, etc.) for the videos. The analysis unit uses a generation AI to analyze the videos received by the receiving unit and identify points that will attract viewers. The analysis unit, for example, divides the video into frames and extracts features of each frame. For example, the analysis unit can extract important edges within a frame using edge detection technology. The analysis unit can also analyze color distribution within a frame using a color histogram. The analysis unit can also analyze motion between frames using a motion vector. The generation unit uses the generation AI to generate attractive thumbnails based on the points identified by the analysis unit. For example, the generation unit generates thumbnails based on frames containing the identified points. The generation unit can also generate thumbnails taking color and composition into consideration. The generation unit can also generate thumbnails by adding text. This allows the video analysis system according to the embodiment to efficiently analyze a user's videos and generate attractive thumbnails.

[0065] The analysis unit can divide video frames and extract features of each frame. For example, the analysis unit divides video frames at regular time intervals. For example, the analysis unit can divide frames every second. The analysis unit can also divide frames by detecting scene changes. For example, the analysis unit can divide frames at the timing of scene changes using a scene change detection algorithm. The analysis unit extracts features of each divided frame. For example, the analysis unit can extract important edges within a frame using edge detection technology. The analysis unit can also analyze color distribution within a frame using a color histogram. The analysis unit can also analyze movement between frames using a motion vector. As a result, by dividing video frames and extracting features, the accuracy of the analysis is improved.

[0066] The analysis unit can identify points that attract viewers based on the extracted features. The analysis unit can identify points that attract viewers using, for example, eye-tracking data. For example, the analysis unit can analyze the eye-tracking data and identify points that viewers pay attention to. The analysis unit can also identify points that attract viewers using click rates. For example, the analysis unit can analyze click rates for each frame of the video and identify points with high click rates. The analysis unit can also identify points that attract viewers using viewing time. For example, the analysis unit can analyze viewing time for each frame of the video and identify points with long viewing times. By identifying points that attract viewers in this way, it is possible to generate attractive thumbnails.

[0067] The generation unit can generate attractive thumbnails based on the identified points. For example, the generation unit generates thumbnails based on frames of the identified points. For example, the generation unit generates thumbnails by cutting out frames of the identified points. The generation unit can also generate thumbnails taking color and composition into consideration. For example, the generation unit can adjust the color of the thumbnail based on color theory. The generation unit can also generate thumbnails based on composition theory such as the golden ratio or the rule of thirds. The generation unit can also generate thumbnails by adding text. For example, the generation unit can add a catchphrase to the thumbnail that will attract the viewer's interest. In this way, generating thumbnails based on the identified points can attract the viewer's interest.

[0068] The analysis unit can analyze footage from surveillance cameras and dashcams to extract important scenes. For example, the analysis unit analyzes footage from surveillance cameras and extracts important scenes based on the magnitude of movement. For example, the analysis unit uses a motion detection algorithm to extract scenes with large movements as important scenes. The analysis unit can also extract important scenes based on changes in audio. For example, the analysis unit uses audio analysis technology to extract scenes with large changes in audio as important scenes. The analysis unit can also extract important scenes based on specific events. For example, the analysis unit uses face recognition technology to extract scenes in which a specific person appears as important scenes. This enables efficient monitoring and investigation of the causes of accidents by analyzing footage from surveillance cameras and dashcams to extract important scenes.

[0069] The generation unit can summarize and document the extracted important scenes. For example, the generation unit summarizes and documents the extracted important scenes. For example, the generation unit creates a summary by cutting out frames of important scenes. The generation unit can also create a summary by adjusting the length of the scenes. For example, the generation unit creates a summary by shortening the length of important scenes. The generation unit can also create a summary based on an evaluation criterion for importance. For example, the generation unit prioritizes including scenes with high importance in the summary. This enables efficient information management by summarizing and documenting important scenes.

[0070] The reception unit can estimate the user's emotions and adjust the timing of video reception based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate. The reception unit adjusts the timing of video reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception timing can be delayed to allow the user to upload videos in a relaxed state. If the user is excited, the reception unit can immediately accept videos to take advantage of the user's momentum. If the user is tired, the reception timing can be adjusted to allow the user to upload videos after a break. This allows the video reception timing to be adjusted according to the user's emotions, making it possible to receive the video at a more appropriate timing.

[0071] The reception unit can analyze the user's past video upload history and select the optimal reception method. The reception unit, for example, analyzes the user's past video upload history. For example, the reception unit analyzes time periods in which the user frequently uploaded in the past and prioritizes reception during those time periods. The reception unit can also analyze the genre of videos the user has uploaded in the past and prioritize reception of videos of the same genre. The reception unit can also analyze the user's past upload frequency and suggest a reception method according to the frequency. For example, if the user has uploaded frequently in the past, the reception unit suggests a reception method according to that frequency. In this way, by analyzing the past upload history, the optimal reception method can be provided to the user.

[0072] When receiving videos, the reception unit can filter the videos based on the user's current projects and areas of interest. The reception unit, for example, identifies the user's current projects and areas of interest. For example, the reception unit analyzes the user's profile information and past activity history to identify the current projects and areas of interest. The reception unit can also filter related videos based on keywords set by the user. For example, the reception unit preferentially accepts videos that match the keywords set by the user. In this way, by filtering videos based on the user's projects and areas of interest, highly relevant videos can be preferentially accepted.

[0073] When accepting a video, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the video using voice recognition technology. For example, the acceptance unit uses voice recognition software to automatically analyze the voice and save it as text. Furthermore, if the user selects text input, the acceptance unit can also accept the video using text analysis technology. For example, the acceptance unit uses text analysis software to automatically analyze the text and save it as video metadata. Furthermore, if the user selects image input, the acceptance unit can also accept the video using image recognition technology. For example, the acceptance unit uses image recognition software to automatically analyze the image and save it as a thumbnail of the video. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0074] The reception unit can estimate the user's emotion and determine the priority of videos to be received based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. The reception unit determines the priority of videos to be received based on the estimated user's emotion. For example, if the user is excited, the reception unit prioritizes the video. If the user is relaxed, the reception unit prioritizes the video over other videos. If the user is stressed, the reception unit postpones the reception of the video. In this way, by determining the priority of videos based on the user's emotion, the videos can be received in a more appropriate order.

[0075] When receiving videos, the reception unit can preferentially receive highly relevant videos by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit acquires the user's location information using GPS data. The reception unit can also estimate the user's location information using an IP address. The reception unit can also acquire the user's location information using a location information service. The reception unit preferentially receives highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, videos related to that area are preferentially received. If the user is traveling, videos related to the travel destination are preferentially received. If the user is at home, videos related to the area around the user's home are preferentially received. In this way, highly relevant videos can be preferentially received by taking into account the user's geographical location information.

[0076] The reception unit can analyze the user's social media activity and receive related videos when receiving a video. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit preferentially receives new videos related to videos shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and receive related videos. The reception unit can also receive related videos by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant videos.

[0077] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a video. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit preferentially adopts the reception method for videos that the user has previously rated highly. The reception unit can also avoid the reception method for videos that the user has previously rated poorly. The reception unit can also analyze the user's past feedback and suggest the optimal reception method. For example, the reception unit suggests the optimal reception method for the user based on the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0078] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. The analysis unit adjusts the presentation method of the analysis based on the estimated user's emotion. For example, if the user is relaxed, the analysis results are displayed in detail. If the user is in a hurry, the analysis results are displayed briefly. If the user is excited, the analysis results are displayed with visually stimulating effects. In this way, by adjusting the presentation method of the analysis according to the user's emotion, more appropriate analysis results can be provided.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. The analysis unit, for example, evaluates the importance of the video. For example, the analysis unit evaluates the importance of the video based on the number of views or user ratings. The analysis unit can also evaluate the importance of the video based on the urgency of the content. For example, the analysis unit evaluates the importance of a news video based on the urgency. The analysis unit adjusts the level of detail of the analysis based on the importance of the video. For example, for a video with many important scenes, a detailed analysis is performed. Also, for a video with few important scenes, a concise analysis is performed. The depth of the analysis can also be adjusted according to the importance. For example, a more detailed analysis is performed for videos with high importance. In this way, by adjusting the level of detail of the analysis based on the importance of the video, efficient analysis is possible.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. The analysis unit, for example, identifies the category of the video. For example, the analysis unit analyzes metadata of the video (e.g., title, description, tags, etc.) to identify the category of the video. The analysis unit can also analyze the content of the video to identify the category. For example, the analysis unit analyzes the content of the video to identify a category such as entertainment, education, or news. The analysis unit applies different analysis algorithms depending on the category of the video. For example, an analysis algorithm that emphasizes visual elements is applied to entertainment videos. An analysis algorithm that emphasizes comprehension of the content is applied to educational videos. An analysis algorithm that emphasizes accuracy of the information is applied to news videos. In this way, by applying different analysis algorithms depending on the category of the video, more appropriate analysis results can be provided.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, analyzes the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously rated highly and performs a similar analysis. The analysis unit can also perform a different analysis by avoiding analysis results that the user has previously rated poorly. The analysis unit can also analyze the user's past analysis results and suggest an optimal analysis method. For example, the analysis unit suggests an optimal analysis method based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. The analysis unit adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis can be short and to the point. If the user is relaxed, the analysis can be detailed. If the user is excited, the analysis can be enhanced with visually stimulating effects. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0083] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the video. The analysis unit, for example, acquires the time of submission of the video. For example, the analysis unit records the submission date and time and identifies the time of submission. The analysis unit can also record the submission order and identify the time of submission. The analysis unit determines the analysis priority based on the time of submission of the video. For example, the latest video is analyzed first. Older videos are also postponed. The analysis priority can also be adjusted depending on the time of submission. For example, the priority is determined based on the urgency or importance of the time of submission. In this way, by determining the analysis priority based on the time of submission of the video, the latest video can be analyzed first.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. The analysis unit, for example, evaluates the relevance of the videos. For example, the analysis unit evaluates the relevance of the videos based on the similarity of content or the degree of keyword matching. The analysis unit can also evaluate the relevance of the videos based on viewer reactions and click rates. For example, the analysis unit analyzes viewer reaction data to identify highly relevant videos. The analysis unit adjusts the order of analysis based on the relevance of the videos. For example, highly relevant videos are analyzed preferentially. Also, videos with low relevance are postponed. The order of analysis can also be adjusted according to relevance. For example, analysis is performed in order of highest relevance. In this way, by adjusting the order of analysis based on the relevance of the videos, highly relevant videos can be analyzed preferentially.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the level of expertise based on the user's occupation and past learning history. The analysis unit can also evaluate the level of expertise based on the user's profile information and past activity history. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, for a user with high level of expertise, the analysis uses a lot of technical terms. For a user with low level of expertise, the analysis can be performed using simpler language. The use of technical terms in the analysis can also be adjusted according to the user's level of expertise. For example, definitions and explanations of technical terms can be added. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0086] The generation unit can estimate the user's emotion and adjust the representation of the generated thumbnail based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on heart rate fluctuations. The generation unit adjusts the representation of the generated thumbnail based on the estimated user's emotion. For example, if the user is relaxed, the generation unit generates a thumbnail with calm colors. If the user is excited, the generation unit generates a thumbnail with vivid colors. If the user is stressed, the generation unit generates a thumbnail with subdued colors. In this way, by adjusting the representation of the thumbnail based on the user's emotion, it is possible to generate thumbnails that attract the viewer's attention.

[0087] The generation unit can adjust the level of detail of the thumbnail based on the importance of the identified points during generation. The generation unit, for example, evaluates the importance of the identified points. For example, the generation unit evaluates the importance of the points based on viewer reactions and click rates. The generation unit can also evaluate the importance of the points based on viewing time and viewer comments. The generation unit adjusts the level of detail of the thumbnail based on the importance of the identified points. For example, if there are many important points, a detailed thumbnail is generated. On the other hand, if there are few important points, a simple thumbnail is generated. The level of detail of the thumbnail can also be adjusted according to the importance. For example, a more detailed thumbnail is generated for a point with high importance. In this way, by adjusting the level of detail of the thumbnail based on the importance of the identified points, it is possible to generate thumbnails that attract the viewer's interest.

[0088] The generation unit can apply different generation algorithms depending on the category of the video during generation. The generation unit, for example, identifies the category of the video. For example, the generation unit analyzes metadata (e.g., title, description, tags, etc.) of the video to identify the category of the video. The generation unit can also analyze the content of the video to identify the category. For example, the generation unit analyzes the content of the video to identify a category such as entertainment, education, or news. The generation unit applies different generation algorithms depending on the category of the video. For example, an algorithm that generates visually attractive thumbnails is applied to entertainment videos. Furthermore, an algorithm that generates thumbnails that help understand the content is applied to educational videos. Furthermore, an algorithm that generates thumbnails that emphasize accuracy of information is applied to news videos. In this way, by applying different generation algorithms depending on the category of the video, thumbnails that attract the viewer's interest can be generated.

[0089] The generation unit can improve the accuracy of thumbnails by referring to the user's past generation results when generating thumbnails. The generation unit, for example, analyzes the user's past generation results. For example, the generation unit generates similar thumbnails by referring to thumbnails that the user has previously rated highly. The generation unit can also generate different thumbnails by avoiding thumbnails that the user has previously rated poorly. The generation unit can also analyze the user's past generation results and suggest optimal thumbnails. For example, the generation unit suggests optimal thumbnails based on the user's past generation results. In this way, the accuracy of thumbnails is improved by referring to the user's past generation results.

[0090] The generation unit can estimate the user's emotion and adjust the length of the thumbnail to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on heart rate fluctuations. The generation unit adjusts the length of the thumbnail to be generated based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit generates a short and to-the-point thumbnail. If the user is relaxed, the generation unit generates a detailed thumbnail. If the user is excited, the generation unit generates a thumbnail with a visually stimulating effect. In this way, by adjusting the length of the thumbnail based on the user's emotion, it is possible to generate thumbnails that attract the viewer's attention.

[0091] The generation unit can determine the priority of thumbnails based on the submission times of the identified points when they are generated. The generation unit, for example, acquires the submission times of the identified points. For example, the generation unit records the submission date and time and identifies the submission times. The generation unit can also record the submission order and identify the submission times. The generation unit determines the priority of thumbnails based on the submission times of the identified points. For example, the most recent points are reflected preferentially in the thumbnails. Furthermore, points submitted earlier are postponed. The priority of thumbnails can also be adjusted according to the submission times. For example, the priority is determined based on the urgency or importance of the submission times. In this way, by determining the priority of thumbnails based on the submission times of the identified points, thumbnails that reflect the most recent points can be generated.

[0092] The generation unit can adjust the order of thumbnails based on the relevance of the identified points during generation. The generation unit, for example, evaluates the relevance of the identified points. For example, the generation unit evaluates the relevance of the points based on similarity of content or keyword matching. The generation unit can also evaluate the relevance of the points based on viewer reactions and click rates. For example, the generation unit analyzes viewer reaction data to identify highly relevant points. The generation unit adjusts the order of thumbnails based on the relevance of the identified points. For example, highly relevant points are reflected in the thumbnails preferentially. Furthermore, less relevant points are postponed. The generation unit can also adjust the order of thumbnails according to the relevance. For example, thumbnails are generated in descending order of relevance. In this way, by adjusting the order of thumbnails based on the relevance of the identified points, thumbnails that attract the viewer's interest can be generated.

[0093] The generation unit can adjust the use of technical terms in the thumbnails according to the user's level of expertise when generating the thumbnails. The generation unit, for example, evaluates the user's level of expertise. For example, the generation unit evaluates the user's level of expertise based on the user's occupation and past learning history. The generation unit can also evaluate the user's level of expertise based on the user's profile information and past activity history. The generation unit adjusts the use of technical terms in the thumbnails according to the user's level of expertise. For example, for a user with high level of expertise, the generation unit generates thumbnails that use a lot of technical terms. For a user with low level of expertise, the generation unit can also adjust the use of technical terms in the thumbnails according to the user's level of expertise. For example, definitions and explanations of technical terms can be added. In this way, by adjusting the use of technical terms in the thumbnails according to the user's level of expertise, thumbnails that are easy for the user to understand can be generated. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The reception unit can analyze the user's past viewing history and customize the video reception method based on the viewing history. For example, the reception unit can analyze the genre and viewing time of videos the user has viewed in the past and prioritize receiving videos of similar genres. The reception unit can also analyze the ratings of videos the user has viewed in the past and prioritize receiving videos of genres that have received high ratings. Furthermore, the reception unit can suggest the optimal reception time based on the user's viewing history. This allows for more personalized video reception by taking the user's viewing history into consideration.

[0096] The analysis unit can analyze the audio data of a video and identify points that will attract viewers based on audio characteristics. For example, the analysis unit can analyze changes in the pitch and tone of the audio to identify emotionally emphasized points. The analysis unit can also analyze the rhythm and tempo of the audio to identify parts that will attract viewers' attention. Furthermore, the analysis unit can analyze the content of the audio to identify important points based on specific keywords and phrases. This makes it possible to generate more attractive thumbnails by utilizing audio data to identify points that will attract viewers.

[0097] The analysis unit can estimate the user's emotions and identify points that will attract viewers based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Then, based on the estimated emotions, it identifies points to which viewers are likely to respond emotionally. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology, thereby similarly identifying emotionally emphasized points. Furthermore, the analysis unit can collect the user's biometric data and estimate the emotions using an emotion estimation algorithm to identify emotionally important points. This allows for more effective thumbnails to be generated by identifying points that will attract viewers based on the user's emotions.

[0098] The generating unit can add visual effects when generating thumbnails based on the identified points. For example, the generating unit can add visually striking effects to frames at the identified points. The generating unit can also generate visually appealing thumbnails by enhancing color or contrast between light and dark. Furthermore, the generating unit can add animation effects to generate moving thumbnails. This makes it possible to generate thumbnails that utilize visual effects to attract the viewer's attention.

[0099] When analyzing video footage from surveillance cameras or dashcams, the analysis unit can also analyze audio data. For example, the analysis unit can synchronize the video and audio from a surveillance camera and identify important scenes based on changes in the audio. The analysis unit can also analyze the video and audio from a dashcam and identify the moment of an accident or important scenes based on the audio data. Furthermore, the analysis unit can use audio analysis technology to extract important scenes based on specific keywords or audio events. This allows for more accurate extraction of important scenes by analyzing both video and audio.

[0100] When summarizing and documenting the extracted important scenes, the generation unit can also summarize the audio data. For example, the generation unit synchronizes the video and audio of important scenes to create a summary. The generation unit can also convert the audio content into text and include it in the summary. Furthermore, the generation unit can extract important parts of the audio and include them in the summary along with the video. This allows for more comprehensive documentation by summarizing both the video and audio.

[0101] The reception unit can estimate the user's emotions and customize the video reception method based on the estimated user's emotions. For example, if the user is relaxed, the reception unit can receive videos using simple steps. If the user is excited, the reception unit can also receive videos by providing detailed steps. Furthermore, if the user is stressed, the reception unit can also receive videos using simplified steps. In this way, customizing the video reception method according to the user's emotions enables smoother video reception.

[0102] The reception unit can analyze the user's past video upload history and select the optimal reception method. For example, the reception unit can analyze the time periods in which the user frequently uploaded in the past and prioritize reception during those time periods. The reception unit can also analyze the genres of videos the user has uploaded in the past and prioritize reception of videos of the same genre. Furthermore, the reception unit can analyze the user's past upload frequency and suggest a reception method based on the frequency. In this way, by analyzing the past upload history, the optimal reception method can be provided to the user.

[0103] The reception unit can customize the method of receiving videos based on the user's current project or area of ​​interest. For example, the reception unit may preferentially receive videos related to the project the user is currently working on. The reception unit may also filter and receive related videos based on the user's area of ​​interest. Furthermore, the reception unit may suggest the optimal reception method depending on the progress of the user's project. This allows for more appropriate video reception based on the user's project or area of ​​interest.

[0104] The reception unit can estimate the user's emotions and determine the priority of videos to be received based on the estimated user's emotions. For example, if the user is excited, the reception unit will preferentially receive that video. If the user is relaxed, the reception unit will receive that video with the same priority as other videos. If the user is feeling stressed, the reception unit will postpone receiving that video. In this way, by determining the priority of videos based on the user's emotions, it is possible to receive videos in a more appropriate order.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The reception unit receives a video from a user. The video from the user may be, for example, MP4, AVI, streaming video, or the like, but is not limited to these examples. The reception unit, for example, stores the video uploaded by the user on a server. The reception unit may also receive metadata (for example, title, description, tags, etc.) of the video. Step 2: The analysis unit uses the generation AI to analyze the video received by the reception unit and identify points that will attract viewers. For example, the analysis unit divides the video into frames and extracts the characteristics of each frame. For example, the analysis unit uses edge detection technology to extract important edges within the frame. The analysis unit can also analyze the color distribution within the frame using a color histogram. The analysis unit can also analyze the movement between frames using a motion vector. Step 3: The generator uses AI to generate attractive thumbnails based on the points identified by the analyzer. For example, the generator generates thumbnails based on frames of the identified points. The generator can also generate thumbnails taking color and composition into consideration. The generator can also generate thumbnails by adding text.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 reception unit that receives videos from users; an analysis unit that analyzes the video received by the reception unit and identifies points that will attract viewers; a generation unit that generates an attractive thumbnail based on the points identified by the analysis unit. A system characterized by:

2. The analysis unit Divide the video into frames and extract the features of each frame 2. The system of claim 1.

3. The analysis unit Identify audience engagement points based on extracted features 2. The system of claim 1.

4. The generation unit Generate attractive thumbnails based on identified points 2. The system of claim 1.

5. The analysis unit Analyzing footage from surveillance cameras and dashcams to extract important scenes 2. The system of claim 1.

6. The generation unit Summarize and document the extracted important scenes 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of video reception based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past video upload history and select the optimal reception method 2. The system of claim 1.

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