system

Abstract

The system according to the embodiment aims to extract important parts from a video of a meeting and provide edited video and text. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an extraction unit, a generation unit, and a provision unit. The reception unit uploads a video of a meeting. The analysis unit analyzes the video uploaded by the reception unit. The extraction unit extracts important portions from the video analyzed by the analysis unit. The generation unit generates an edited video or text based on the important portions extracted by the extraction unit. The provision unit provides the edited video or text generated by the generation unit to a user.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently extract and edit important parts from meeting videos, and this required time and effort.

[0005] The system according to the embodiment aims to extract important parts from a video of a meeting and provide edited video and text. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an extraction unit, a generation unit, and a provision unit. The reception unit uploads a video of a meeting. The analysis unit analyzes the video uploaded by the reception unit. The extraction unit extracts important portions from the video analyzed by the analysis unit. The generation unit generates edited video or text based on the important portions extracted by the extraction unit. The provision unit provides the edited video or text generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can extract important parts from the video of the meeting and provide edited video and text. [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) An AI tool according to an embodiment of the present invention is a system in which, simply by uploading a meeting video, AI extracts important parts and provides them as edited video or text. The AI ​​tool uploads the meeting video, analyzes the video, extracts important parts, and generates edited video or text, which is then provided to the user. For example, a user uploads a meeting video. The AI ​​tool then uses AI to analyze the video and identify important information, such as speech content and presentation slides. The AI ​​tool then extracts important parts from the analyzed video. The AI ​​tool then generates edited video or text based on the extracted important parts. The AI ​​tool then provides the generated edited video or text to the user. This allows the user to quickly grasp the key points of the meeting. For example, long meeting videos can be reviewed in a short time, saving time. Furthermore, providing the text makes it easy to search and share. This allows the AI ​​tool to facilitate internal communication and improve productivity. The AI ​​tool efficiently analyzes meeting videos, extracts important parts, and provides edited content, thereby smoothing internal communication and improving productivity. For example, the speed of decision-making increases when important information is shared quickly. It also reduces the burden on employees by making it easier to grasp the key points of meetings. Furthermore, AI can continuously learn and improve its analysis accuracy, enabling more accurate information extraction and improving the quality of edited content. For example, learning from past meeting data can identify specific patterns and trends and be useful for future meetings.

[0029] The AI ​​tool according to the embodiment includes a receiving unit, an analysis unit, an extraction unit, a generation unit, and a provision unit. The reception unit allows a user to upload a video of a meeting. The videos uploaded by the user include, but are not limited to, video conferences, presentations, and discussions. The reception unit automatically uploads the video from a folder specified by the user. The reception unit also allows a user to upload a video by dragging and dropping. The reception unit also allows a user to upload a video by entering a URL. The analysis unit uses AI to analyze the uploaded video. The analysis may be performed using, but is not limited to, audio analysis, video analysis, text analysis, or other methods. For example, the analysis unit may convert speech content in the video into text using audio analysis. The analysis unit may also identify presentation slides using video analysis. The analysis unit may also extract keywords from speech content using text analysis. The extraction unit extracts important portions from the video analyzed by the analysis unit. The important portions may be extracted based on, for example, but not limited to, the frequency of keyword appearance or the importance of the speaker. For example, the extraction unit extracts important portions based on the frequency of keyword appearance. The extraction unit can also extract important portions based on the importance of speakers. The extraction unit can also extract important portions based on the content of presentation slides. The generation unit generates edited video and text based on the important portions extracted by the extraction unit. The edited video and text are generated in the form of, for example, a highlight video or summary text, but are not limited to these examples. For example, the generation unit generates a highlight video by connecting the extracted important portions. The generation unit can also generate text by summarizing the extracted important portions. The generation unit can also generate infographics based on the extracted important portions. The provision unit provides the edited video and text generated by the generation unit to a user. The provision is performed, for example, through a web application or a mobile application, but is not limited to these examples.For example, the providing unit displays the generated edited video and text to the user through a web application. The providing unit can also display the generated edited video and text to the user through a mobile application. The providing unit can also send the generated edited video and text to the user by email. As a result, the AI ​​tool according to the embodiment can efficiently analyze meeting videos, extract important parts, and provide edited content, thereby facilitating internal communication and improving productivity.

[0030] The reception unit can analyze the user's past upload history and select an appropriate upload method. For example, the reception unit can prioritize and suggest upload methods (such as Wi-Fi or mobile data) that the user has frequently used in the past. The reception unit can also analyze the user's past upload times and prompt the user to upload at the optimal time. The reception unit can also analyze the cause of the user's past upload failures and select the optimal method to prevent similar problems from occurring. In this way, by analyzing the user's past upload history, the optimal upload method can be selected and efficient uploading can be achieved. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past upload history data into the generation AI and cause the generation AI to select the optimal upload method.

[0031] The reception unit may filter videos based on the user's current project or area of ​​interest when uploading the videos. For example, the reception unit may filter videos so that the user uploads only videos related to the user's current project. The reception unit may also prioritize uploading highly relevant videos based on the user's area of ​​interest. The reception unit may also analyze the content of videos previously uploaded by the user and filter and upload videos with similar themes. This allows for efficient uploading of highly relevant videos by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's project data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0032] When uploading a video, the reception unit can select an appropriate upload means depending on the user's input method. For example, if the user uses voice input, the reception unit can upload the video using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also upload the video using text analysis technology. Furthermore, if the user uses image input, the reception unit can also upload the video using image recognition technology. This allows for efficient video uploading by selecting the optimal upload means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal upload means.

[0033] When uploading videos, the reception unit can prioritize uploading highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize uploading videos related to that area. Furthermore, if the user is traveling, the reception unit can prioritize uploading videos related to the location closest to the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize uploading videos related to the event. In this way, by taking the user's geographical location information into account, highly relevant videos can be efficiently uploaded. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant videos.

[0034] The reception unit can analyze the user's social media activity and upload related videos when uploading a video. For example, the reception unit prioritizes uploading videos that the user has shared on social media. The reception unit can also analyze the content of the user's social media posts and upload related videos. The reception unit can also upload 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, highly relevant videos can be efficiently uploaded. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related videos.

[0035] The reception unit can customize the upload method based on the user's past feedback when uploading a video. For example, the reception unit can suggest an optimal upload method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific upload method based on the user's past feedback. The reception unit can also analyze the user's feedback and continuously improve the upload method. This makes it possible to provide an optimal upload method by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's feedback data into a generation AI and cause the generation AI to customize the upload method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, the analysis unit performs a detailed analysis on a video with high importance. The analysis unit can also perform a simplified analysis on a video with low importance. The analysis unit can also adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, the analysis unit can apply an algorithm that analyzes the content of slides to a presentation video. The analysis unit can also apply an algorithm that analyzes the content of remarks to a discussion video. The analysis unit can also apply an algorithm that analyzes procedures and operation methods to a training video. This allows the application of an appropriate analysis algorithm depending on the category of the video, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and detect similar patterns. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the videos were uploaded. For example, the analysis unit prioritizes the analysis of the most recent videos. The analysis unit can also prioritize the analysis of videos within a period specified by the user. The analysis unit can also prioritize the analysis of videos related to a specific event. This allows for efficient analysis by determining the analysis priority based on the time when the videos were uploaded. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video upload time data to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. For example, the analysis unit prioritizes analysis of highly relevant videos. The analysis unit can also prioritize analysis of videos related to a theme specified by the user. The analysis unit can also prioritize analysis of highly relevant videos based on past analysis results. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the videos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0042] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between videos during extraction. The extraction unit, for example, analyzes the content of related videos and extracts important common parts. The extraction unit can also extract important parts by taking into account the interrelationships between videos. The extraction unit can also improve the accuracy of extraction based on the interrelationships between videos. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between videos. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input video interrelationship data into a generation AI and cause the generation AI to improve the accuracy of extraction.

[0043] The extraction unit can perform extraction while taking into account attribute information of the person who submitted the video. The extraction unit extracts important parts based on, for example, the submitter's job title or field of expertise. The extraction unit can also analyze the submitter's past comments and extract important parts. The extraction unit can also improve the accuracy of extraction based on the submitter's attribute information. In this way, the accuracy of extraction can be improved by taking into account the attribute information of the video submitter. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input attribute information data of the submitter into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0044] The extraction unit can weight the extraction based on the frequency of video submission during extraction. For example, the extraction unit prioritizes extraction of high-importance parts for videos with a high submission frequency. The extraction unit can also extract low-importance parts for videos with a low submission frequency. The extraction unit can also weight the extraction based on the submission frequency. In this way, by weighting the extraction based on the frequency of video submission, important parts can be preferentially extracted. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input submission frequency data to a generation AI and cause the generation AI to perform extraction weighting.

[0045] The extraction unit can perform extraction while taking into account the geographical distribution of the videos. For example, the extraction unit can preferentially extract videos related to a specific region. The extraction unit can also extract videos related to geographically nearby locations. The extraction unit can also extract important parts based on the geographical distribution. This allows for efficient extraction of highly relevant parts by taking the geographical distribution of the videos into consideration. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input geographical distribution data of the videos to the generation AI and cause the generation AI to improve the accuracy of the extraction.

[0046] The extraction unit can improve the accuracy of extraction by referring to related literature of the video during extraction. The extraction unit, for example, extracts important parts based on related literature. The extraction unit can also improve the accuracy of extraction by referring to literature related to the content of the video. The extraction unit can also analyze related literature and adjust the extraction criteria. In this way, by referring to related literature of the video, the accuracy of extraction can be improved. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input related literature data into a generation AI and have the generation AI improve the accuracy of extraction.

[0047] The extraction unit can perform extraction taking into account the market value of the video. For example, the extraction unit can prioritize extraction of important parts for videos with high market value. The extraction unit can also perform simplified extraction for videos with low market value. The extraction unit can also adjust the extraction criteria based on the market value. This allows important parts to be preferentially extracted by taking the market value of the video into consideration. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input market value data into the generation AI and have the generation AI adjust the extraction criteria.

[0048] The generation unit can adjust the level of detail of the generated content based on the importance of the extracted information during generation. For example, the generation unit generates content including a detailed explanation for information with high importance. The generation unit can also generate simplified content for information with low importance. The generation unit can also adjust the level of detail of the generated content according to the importance. As a result, efficient content generation can be achieved by adjusting the level of detail of the generated content based on the importance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the extracted information to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0049] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a generation algorithm that emphasizes the content of slides to a presentation video. The generation unit can also apply a generation algorithm that emphasizes the content of remarks to a discussion video. The generation unit can also apply a generation algorithm that emphasizes procedures or operation methods to a training video. In this way, by applying an appropriate generation algorithm depending on the content category, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input content category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0050] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can adjust the generation algorithm based on, for example, feedback provided by the user in the past. The generation unit can also analyze the user's past generation results and detect similar patterns. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] During generation, the generation unit can determine the generation priority based on the submission time of the extracted information. For example, the generation unit prioritizes generating the latest information. The generation unit can also prioritize generating information within a period specified by the user. The generation unit can also prioritize generating information related to a specific event. This enables efficient content generation by determining the generation priority based on the submission time of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission time data of the extracted information into a generation AI and have the generation AI determine the generation priority.

[0052] The generation unit can adjust the order of generation based on the relevance of the extracted information during generation. For example, the generation unit prioritizes generating highly relevant information. The generation unit can also prioritize generating information related to a theme specified by the user. The generation unit can also prioritize generating highly relevant information based on past generation results. This allows for efficient content generation by adjusting the order of generation based on the relevance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the extracted information into a generation AI and cause the generation AI to adjust the order of generation.

[0053] The generation unit can adjust the use of technical terms in the content to be generated according to the user's level of expertise during generation. For example, if the user is an expert, the generation unit can generate content that uses a lot of technical terms. Furthermore, if the user is a beginner, the generation unit can also generate content that avoids technical terms. The generation unit can also adjust the way the content is expressed according to the user's level of expertise. This allows for adjusting the use of technical terms in the content according to the user's level of expertise, thereby providing content that is easy for the user to understand. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0054] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also analyze the user's past operation history and suggest the optimal display method. The providing unit can also customize the display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generation AI and cause the generation AI to select the display method.

[0055] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit can prioritize displaying information related to a project the user is currently working on. The providing unit can also display highly relevant information based on the user's current task. The providing unit can also customize the display content taking into account the progress of the user's task. This makes it possible to provide highly relevant information by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the display content.

[0056] The providing unit can select the optimal display method by taking into account the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the display method.

[0057] The providing unit can select the optimal display method by taking into account the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the display method.

[0058] The providing unit can make the display content multilingual according to the user's language setting when providing the display content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide information that is easy for the user to understand by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0059] At the time of providing, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide related information.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can analyze the user's past upload history and select an appropriate upload method. For example, it can prioritize and suggest upload methods (such as Wi-Fi or mobile data) that the user has frequently used in the past. The reception unit can also analyze the user's past upload times and prompt the user to upload at the optimal time. The reception unit can also analyze the cause of the user's past upload failures and select the optimal method to prevent similar problems from occurring. In this way, by analyzing the user's past upload history, the optimal upload method can be selected and efficient uploading can be achieved. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past upload history data into the generation AI and cause the generation AI to select the optimal upload method.

[0062] The reception unit may filter videos based on the user's current project or area of ​​interest when uploading them. For example, the reception unit may filter videos so that only videos related to the user's current project are uploaded. The reception unit may also prioritize uploading highly relevant videos based on the user's area of ​​interest. The reception unit may also analyze the content of videos previously uploaded by the user and filter and upload videos with similar themes. This allows for efficient uploading of highly relevant videos by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's project data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0063] When uploading a video, the reception unit can select an appropriate upload method depending on the user's input method. For example, if the user uses voice input, the reception unit can upload the video using voice recognition technology. If the user uses text input, the reception unit can also upload the video using text analysis technology. If the user uses image input, the reception unit can also upload the video using image recognition technology. This allows for efficient video uploading by selecting the optimal upload method depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal upload method.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, a detailed analysis is performed for a video with high importance. The analysis unit can also perform a simplified analysis for a video with low importance. The analysis unit can also adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, the analysis unit can apply an algorithm that analyzes the content of slides to a presentation video. The analysis unit can also apply an algorithm that analyzes the content of remarks to a discussion video. The analysis unit can also apply an algorithm that analyzes procedures and operation methods to a training video. This allows the application of an appropriate analysis algorithm depending on the category of the video, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0066] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0067] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between videos during extraction. For example, the content of related videos is analyzed and important common parts are extracted. The extraction unit can also extract important parts by taking into account the interrelationships between videos. The extraction unit can also improve the accuracy of extraction based on the interrelationships between videos. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between videos. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input video interrelationship data into a generation AI and cause the generation AI to improve the accuracy of extraction.

[0068] The extraction unit can perform extraction while taking into account attribute information of the person who submitted the video. For example, it can extract important parts based on the submitter's job title or field of expertise. The extraction unit can also analyze the submitter's past comments and extract important parts. The extraction unit can also improve the accuracy of extraction based on the submitter's attribute information. In this way, the accuracy of extraction can be improved by taking into account the attribute information of the video submitter. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input attribute information data of the submitter into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0069] During extraction, the extraction unit can weight the extraction based on the frequency of video submission. For example, for videos with a high submission frequency, it can prioritize extraction of more important parts. The extraction unit can also extract less important parts for videos with a low submission frequency. The extraction unit can also weight the extraction based on the submission frequency. In this way, by weighting the extraction based on the frequency of video submission, it is possible to prioritize extraction of more important parts. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input submission frequency data to a generation AI and have the generation AI perform extraction weighting.

[0070] The extraction unit can perform extraction while taking into account the geographic distribution of videos. For example, videos related to a specific region can be preferentially extracted. The extraction unit can also extract videos related to geographically nearby locations. The extraction unit can also extract important parts based on the geographic distribution. This allows for efficient extraction of highly relevant parts by taking the geographic distribution of videos into consideration. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input geographic distribution data of videos to the generation AI and cause the generation AI to improve the accuracy of extraction.

[0071] The extraction unit can improve the accuracy of extraction by referring to literature related to the video during extraction. For example, important parts are extracted based on the related literature. The extraction unit can also improve the accuracy of extraction by referring to literature related to the content of the video. The extraction unit can also analyze related literature and adjust the extraction criteria. In this way, by referring to literature related to the video, the accuracy of extraction can be improved. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input related literature data into the generation AI and have the generation AI improve the accuracy of extraction.

[0072] The extraction unit can perform extraction taking into account the market value of the video. For example, important parts are preferentially extracted for videos with high market value. The extraction unit can also perform simplified extraction for videos with low market value. The extraction unit can also adjust the extraction criteria based on the market value. This allows important parts to be preferentially extracted by taking the market value of the video into consideration. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input market value data into the generation AI and have the generation AI adjust the extraction criteria.

[0073] The generation unit can adjust the level of detail of the generated content based on the importance of the extracted information during generation. For example, for information with high importance, the generation unit generates content including a detailed explanation. The generation unit can also generate simplified content for information with low importance. The generation unit can also adjust the level of detail of the generated content according to the importance. As a result, efficient content generation can be achieved by adjusting the level of detail of the generated content based on the importance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the extracted information to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0074] During generation, the generation unit can apply different generation algorithms depending on the content category. For example, the generation unit can apply a generation algorithm that emphasizes the content of slides to a presentation video. The generation unit can also apply a generation algorithm that emphasizes the content of remarks to a discussion video. The generation unit can also apply a generation algorithm that emphasizes procedures or operation methods to a training video. In this way, by applying an appropriate generation algorithm depending on the content category, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input content category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0075] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation algorithm can be adjusted based on feedback provided by the user in the past. The generation unit can also analyze the user's past generation results and detect similar patterns. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0076] During generation, the generation unit can determine the generation priority based on the submission time of the extracted information. For example, the latest information is generated with priority. The generation unit can also prioritize generating information within a period specified by the user. The generation unit can also prioritize generating information related to a specific event. This allows for efficient content generation by determining the generation priority based on the submission time of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission time data of the extracted information into a generation AI and have the generation AI determine the generation priority.

[0077] During generation, the generation unit can adjust the order of generation based on the relevance of the extracted information. For example, it can prioritize generating highly relevant information. The generation unit can also prioritize generating information related to a theme specified by the user. The generation unit can also prioritize generating highly relevant information based on past generation results. This allows for efficient content generation by adjusting the order of generation based on the relevance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the extracted information into the generation AI and cause the generation AI to adjust the order of generation.

[0078] During generation, the generation unit can adjust the use of technical terminology in the content to be generated according to the user's level of expertise. For example, if the user is an expert, the generation unit can generate content that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can also generate content that avoids technical terminology. The generation unit can also adjust the way the content is expressed according to the user's level of expertise. This allows for the provision of content that is easy for the user to understand by adjusting the use of technical terminology in the content according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0079] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also analyze the user's past operation history and suggest the optimal display method. The providing unit can also customize the display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generating AI and cause the generating AI to select the display method.

[0080] The providing unit can customize the display content according to the user's current task when providing the display content. For example, the providing unit can prioritize displaying information related to a project the user is currently working on. The providing unit can also display highly relevant information based on the user's current task. The providing unit can also customize the display content taking into account the progress of the user's task. This makes it possible to provide highly relevant information by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the display content.

[0081] When providing the display information, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the display method.

[0082] The processing flow of the first embodiment will be briefly explained below.

[0083] Step 1: The reception unit allows users to upload meeting videos. Videos uploaded by users include video conferences, presentations, discussions, etc. The reception unit can automatically upload videos from a folder specified by the user, upload videos by drag and drop, or upload videos by entering a URL. Step 2: The analysis unit uses AI to analyze the uploaded video. Analysis is performed using methods such as audio analysis, video analysis, and text analysis. For example, audio analysis is used to convert what is said in the video into text, video analysis is used to identify presentation slides, and text analysis is used to extract keywords from what is said. Step 3: The extraction unit extracts important parts from the video analyzed by the analysis unit. Important parts are extracted based on the frequency of keyword appearances and the importance of speakers. For example, important parts may be extracted based on the frequency of keyword appearances, the importance of speakers, or the content of presentation slides. Step 4: The generator generates edited video and text based on the important parts extracted by the extractor. The edited video and text are generated in the form of a highlight video, a summary text, etc. For example, the generator may generate a highlight video by connecting the extracted important parts, generate text by summarizing the extracted important parts, or generate an infographic based on the extracted important parts. Step 5: The providing unit provides the edited video and text generated by the generating unit to the user. The providing is performed through a web application or a mobile application. For example, the generated edited video and text may be displayed to the user through the web application, the mobile application, or sent to the user by email.

[0084] (Example 2) An AI tool according to an embodiment of the present invention is a system in which, simply by uploading a meeting video, AI extracts important parts and provides them as edited video or text. The AI ​​tool uploads the meeting video, analyzes the video, extracts important parts, and generates edited video or text, which is then provided to the user. For example, a user uploads a meeting video. The AI ​​tool then uses AI to analyze the video and identify important information, such as speech content and presentation slides. The AI ​​tool then extracts important parts from the analyzed video. The AI ​​tool then generates edited video or text based on the extracted important parts. The AI ​​tool then provides the generated edited video or text to the user. This allows the user to quickly grasp the key points of the meeting. For example, long meeting videos can be reviewed in a short time, saving time. Furthermore, providing the text makes it easy to search and share. This allows the AI ​​tool to facilitate internal communication and improve productivity. The AI ​​tool efficiently analyzes meeting videos, extracts important parts, and provides edited content, thereby smoothing internal communication and improving productivity. For example, the speed of decision-making increases when important information is shared quickly. It also reduces the burden on employees by making it easier to grasp the key points of meetings. Furthermore, AI can continuously learn and improve its analysis accuracy, enabling more accurate information extraction and improving the quality of edited content. For example, learning from past meeting data can identify specific patterns and trends and be useful for future meetings.

[0085] The AI ​​tool according to the embodiment includes a receiving unit, an analysis unit, an extraction unit, a generation unit, and a provision unit. The reception unit allows a user to upload a video of a meeting. The videos uploaded by the user include, but are not limited to, video conferences, presentations, and discussions. The reception unit automatically uploads the video from a folder specified by the user. The reception unit also allows a user to upload a video by dragging and dropping. The reception unit also allows a user to upload a video by entering a URL. The analysis unit uses AI to analyze the uploaded video. The analysis may be performed using, but is not limited to, audio analysis, video analysis, text analysis, or other methods. For example, the analysis unit may convert speech content in the video into text using audio analysis. The analysis unit may also identify presentation slides using video analysis. The analysis unit may also extract keywords from speech content using text analysis. The extraction unit extracts important portions from the video analyzed by the analysis unit. The important portions may be extracted based on, for example, but not limited to, the frequency of keyword appearance or the importance of the speaker. For example, the extraction unit extracts important portions based on the frequency of keyword appearance. The extraction unit can also extract important portions based on the importance of speakers. The extraction unit can also extract important portions based on the content of presentation slides. The generation unit generates edited video and text based on the important portions extracted by the extraction unit. The edited video and text are generated in the form of, for example, a highlight video or summary text, but are not limited to these examples. For example, the generation unit generates a highlight video by connecting the extracted important portions. The generation unit can also generate text by summarizing the extracted important portions. The generation unit can also generate infographics based on the extracted important portions. The provision unit provides the edited video and text generated by the generation unit to a user. The provision is performed, for example, through a web application or a mobile application, but is not limited to these examples.For example, the providing unit displays the generated edited video and text to the user through a web application. The providing unit can also display the generated edited video and text to the user through a mobile application. The providing unit can also send the generated edited video and text to the user by email. As a result, the AI ​​tool according to the embodiment can efficiently analyze meeting videos, extract important parts, and provide edited content, thereby facilitating internal communication and improving productivity.

[0086] The reception unit can estimate the user's emotions and adjust the timing of video uploads based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit automatically delays the upload using the AI ​​and prompts the user to upload when they are relaxed. Furthermore, if the user is in a hurry, the reception unit can immediately start the upload using the AI ​​to quickly proceed with the process. Furthermore, if the user is concentrating, the reception unit can also upload at the appropriate timing, achieving efficient processing. This allows the user's stress to be reduced and efficient uploads to be achieved by adjusting the video upload timing according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0087] The reception unit can analyze the user's past upload history and select an appropriate upload method. For example, the reception unit can prioritize and suggest upload methods (such as Wi-Fi or mobile data) that the user has frequently used in the past. The reception unit can also analyze the user's past upload times and prompt the user to upload at the optimal time. The reception unit can also analyze the cause of the user's past upload failures and select the optimal method to prevent similar problems from occurring. In this way, by analyzing the user's past upload history, the optimal upload method can be selected and efficient uploading can be achieved. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past upload history data into the generation AI and cause the generation AI to select the optimal upload method.

[0088] The reception unit may filter videos based on the user's current project or area of ​​interest when uploading the videos. For example, the reception unit may filter videos so that the user uploads only videos related to the user's current project. The reception unit may also prioritize uploading highly relevant videos based on the user's area of ​​interest. The reception unit may also analyze the content of videos previously uploaded by the user and filter and upload videos with similar themes. This allows for efficient uploading of highly relevant videos by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's project data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0089] When uploading a video, the reception unit can select an appropriate upload means depending on the user's input method. For example, if the user uses voice input, the reception unit can upload the video using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also upload the video using text analysis technology. Furthermore, if the user uses image input, the reception unit can also upload the video using image recognition technology. This allows for efficient video uploading by selecting the optimal upload means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal upload means.

[0090] The reception unit can estimate the user's emotions and determine the priority of videos to be uploaded based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit postpones videos of low importance and prioritizes uploading videos of high importance. Furthermore, when the user is relaxed, the reception unit can upload all videos equally. Furthermore, when the user is in a hurry, the reception unit can upload the most important videos as the highest priority. Thus, by determining the priority of videos according to the user's emotions, important videos can be uploaded preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0091] When uploading videos, the reception unit can prioritize uploading highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize uploading videos related to that area. Furthermore, if the user is traveling, the reception unit can prioritize uploading videos related to the location closest to the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize uploading videos related to the event. In this way, by taking the user's geographical location information into account, highly relevant videos can be efficiently uploaded. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant videos.

[0092] The reception unit can analyze the user's social media activity and upload related videos when uploading a video. For example, the reception unit prioritizes uploading videos that the user has shared on social media. The reception unit can also analyze the content of the user's social media posts and upload related videos. The reception unit can also upload 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, highly relevant videos can be efficiently uploaded. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related videos.

[0093] The reception unit can customize the upload method based on the user's past feedback when uploading a video. For example, the reception unit can suggest an optimal upload method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific upload method based on the user's past feedback. The reception unit can also analyze the user's feedback and continuously improve the upload method. This makes it possible to provide an optimal upload method by reflecting the user's past feedback. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's feedback data into a generation AI and cause the generation AI to customize the upload method.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand for the user by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0095] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, the analysis unit performs a detailed analysis on a video with high importance. The analysis unit can also perform a simplified analysis on a video with low importance. The analysis unit can also adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0096] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, the analysis unit can apply an algorithm that analyzes the content of slides to a presentation video. The analysis unit can also apply an algorithm that analyzes the content of remarks to a discussion video. The analysis unit can also apply an algorithm that analyzes procedures and operation methods to a training video. This allows the application of an appropriate analysis algorithm depending on the category of the video, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0097] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and detect similar patterns. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an optimal analysis result for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0099] During analysis, the analysis unit can determine the analysis priority based on the time when the videos were uploaded. For example, the analysis unit prioritizes the analysis of the most recent videos. The analysis unit can also prioritize the analysis of videos within a period specified by the user. The analysis unit can also prioritize the analysis of videos related to a specific event. This allows for efficient analysis by determining the analysis priority based on the time when the videos were uploaded. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video upload time data to the generation AI and have the generation AI determine the analysis priority.

[0100] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. For example, the analysis unit prioritizes analysis of highly relevant videos. The analysis unit can also prioritize analysis of videos related to a theme specified by the user. The analysis unit can also prioritize analysis of highly relevant videos based on past analysis results. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the videos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0101] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology 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. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0102] The extraction unit can estimate the user's emotions and adjust extraction criteria based on the estimated user emotions. For example, if the user is stressed, the extraction unit extracts only important parts. Furthermore, if the user is relaxed, the extraction unit can also extract detailed parts. Furthermore, if the user is in a hurry, the extraction unit can extract only the essential parts. By adjusting the extraction criteria according to the user's emotions, it is possible to provide optimal extraction results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, an AI. For example, the extraction unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0103] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between videos during extraction. The extraction unit, for example, analyzes the content of related videos and extracts important common parts. The extraction unit can also extract important parts by taking into account the interrelationships between videos. The extraction unit can also improve the accuracy of extraction based on the interrelationships between videos. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between videos. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input video interrelationship data into a generation AI and cause the generation AI to improve the accuracy of extraction.

[0104] The extraction unit can perform extraction while taking into account attribute information of the person who submitted the video. The extraction unit extracts important parts based on, for example, the submitter's job title or field of expertise. The extraction unit can also analyze the submitter's past comments and extract important parts. The extraction unit can also improve the accuracy of extraction based on the submitter's attribute information. In this way, the accuracy of extraction can be improved by taking into account the attribute information of the video submitter. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input attribute information data of the submitter into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0105] The extraction unit can weight the extraction based on the frequency of video submission during extraction. For example, the extraction unit prioritizes extraction of high-importance parts for videos with a high submission frequency. The extraction unit can also extract low-importance parts for videos with a low submission frequency. The extraction unit can also weight the extraction based on the submission frequency. In this way, by weighting the extraction based on the frequency of video submission, important parts can be preferentially extracted. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input submission frequency data to a generation AI and cause the generation AI to perform extraction weighting.

[0106] The extraction unit can estimate the user's emotion and adjust the order in which the extraction results are displayed based on the estimated user emotion. For example, if the user is in a hurry, the extraction unit can display the most important part first. Furthermore, if the user is relaxed, the extraction unit can also display the details in an orderly manner. Furthermore, if the user is stressed, the extraction unit can display the results in a visually easy-to-understand order. By adjusting the order in which the extraction results are displayed according to the user's emotion, it is possible to provide an optimal display order for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotion.

[0107] The extraction unit can perform extraction while taking into account the geographical distribution of the videos. For example, the extraction unit can preferentially extract videos related to a specific region. The extraction unit can also extract videos related to geographically nearby locations. The extraction unit can also extract important parts based on the geographical distribution. This allows for efficient extraction of highly relevant parts by taking the geographical distribution of the videos into consideration. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input geographical distribution data of the videos to the generation AI and cause the generation AI to improve the accuracy of the extraction.

[0108] The extraction unit can improve the accuracy of extraction by referring to related literature of the video during extraction. The extraction unit, for example, extracts important parts based on related literature. The extraction unit can also improve the accuracy of extraction by referring to literature related to the content of the video. The extraction unit can also analyze related literature and adjust the extraction criteria. In this way, by referring to related literature of the video, the accuracy of extraction can be improved. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input related literature data into a generation AI and have the generation AI improve the accuracy of extraction.

[0109] The extraction unit can perform extraction taking into account the market value of the video. For example, the extraction unit can prioritize extraction of important parts for videos with high market value. The extraction unit can also perform simplified extraction for videos with low market value. The extraction unit can also adjust the extraction criteria based on the market value. This allows important parts to be preferentially extracted by taking the market value of the video into consideration. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input market value data into the generation AI and have the generation AI adjust the extraction criteria.

[0110] The generation unit can estimate the user's emotions and adjust the expression method of the generated content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. If the user is excited, the generation unit can also generate a video that adds visually stimulating effects. By adjusting the expression method of the content according to the user's emotions, optimal content can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0111] The generation unit can adjust the level of detail of the generated content based on the importance of the extracted information during generation. For example, the generation unit generates content including a detailed explanation for information with high importance. The generation unit can also generate simplified content for information with low importance. The generation unit can also adjust the level of detail of the generated content according to the importance. As a result, efficient content generation can be achieved by adjusting the level of detail of the generated content based on the importance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the extracted information to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0112] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a generation algorithm that emphasizes the content of slides to a presentation video. The generation unit can also apply a generation algorithm that emphasizes the content of remarks to a discussion video. The generation unit can also apply a generation algorithm that emphasizes procedures or operation methods to a training video. In this way, by applying an appropriate generation algorithm depending on the content category, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input content category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0113] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can adjust the generation algorithm based on, for example, feedback provided by the user in the past. The generation unit can also analyze the user's past generation results and detect similar patterns. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0114] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point content. If the user is relaxed, the generation unit can generate longer content with detailed explanations. If the user is excited, the generation unit can generate content with visually stimulating effects. By adjusting the length of the content according to the user's emotions, optimal content can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0115] During generation, the generation unit can determine the generation priority based on the submission time of the extracted information. For example, the generation unit prioritizes generating the latest information. The generation unit can also prioritize generating information within a period specified by the user. The generation unit can also prioritize generating information related to a specific event. This enables efficient content generation by determining the generation priority based on the submission time of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission time data of the extracted information into a generation AI and have the generation AI determine the generation priority.

[0116] The generation unit can adjust the order of generation based on the relevance of the extracted information during generation. For example, the generation unit prioritizes generating highly relevant information. The generation unit can also prioritize generating information related to a theme specified by the user. The generation unit can also prioritize generating highly relevant information based on past generation results. This allows for efficient content generation by adjusting the order of generation based on the relevance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the extracted information into a generation AI and cause the generation AI to adjust the order of generation.

[0117] The generation unit can adjust the use of technical terms in the content to be generated according to the user's level of expertise during generation. For example, if the user is an expert, the generation unit can generate content that uses a lot of technical terms. Furthermore, if the user is a beginner, the generation unit can also generate content that avoids technical terms. The generation unit can also adjust the way the content is expressed according to the user's level of expertise. This allows for adjusting the use of technical terms in the content according to the user's level of expertise, thereby providing content that is easy for the user to understand. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0118] The providing unit can estimate the user's emotions and adjust the display method of the content to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. By adjusting the content display method according to the user's emotions, the optimal display method for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0119] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also analyze the user's past operation history and suggest the optimal display method. The providing unit can also customize the display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generation AI and cause the generation AI to select the display method.

[0120] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit can prioritize displaying information related to a project the user is currently working on. The providing unit can also display highly relevant information based on the user's current task. The providing unit can also customize the display content taking into account the progress of the user's task. This makes it possible to provide highly relevant information by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the display content.

[0121] The providing unit can select the optimal display method by taking into account the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the display method.

[0122] The providing unit can estimate the user's emotions and adjust the operation procedures of the content to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can also provide procedures that allow quick operation. By adjusting the operation procedures according to the user's emotions, it is possible to provide the optimal operation procedures for the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0123] The providing unit can select the optimal display method by taking into account the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the display method.

[0124] The providing unit can make the display content multilingual according to the user's language setting when providing the display content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide information that is easy for the user to understand by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0125] At the time of providing, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, extraction unit, generation unit, and provision 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 control unit 46A of the smart device 14 and performs a process in which a user uploads a video of a meeting. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded video. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important parts from the analyzed video. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates edited video or text based on the extracted important parts. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated edited video or text to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, extraction unit, generation unit, and provision 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 control unit 46A of the smart glasses 214 and performs a process in which a user uploads a video of a meeting. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded video. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important portions from the analyzed video. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates edited video or text based on the extracted important portions. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated edited video or text to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, extraction unit, generation unit, and provision 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 control unit 46A of the headset-type terminal 314 and performs a process in which a user uploads a video of a meeting. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded video. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important parts from the analyzed video. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates edited video or text based on the extracted important parts. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated edited video or text to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, extraction unit, generation unit, and provision 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 control unit 46A of the robot 414 and performs a process in which a user uploads a video of a meeting. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded video. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important parts from the analyzed video. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates edited video or text based on the extracted important parts. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated edited video or text to the user.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The reception unit can analyze the user's past upload history and select an appropriate upload method. For example, it can prioritize and suggest upload methods (such as Wi-Fi or mobile data) that the user has frequently used in the past. The reception unit can also analyze the user's past upload times and prompt the user to upload at the optimal time. The reception unit can also analyze the cause of the user's past upload failures and select the optimal method to prevent similar problems from occurring. In this way, by analyzing the user's past upload history, the optimal upload method can be selected and efficient uploading can be achieved. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past upload history data into the generation AI and cause the generation AI to select the optimal upload method.

[0128] The reception unit may filter videos based on the user's current project or area of ​​interest when uploading them. For example, the reception unit may filter videos so that only videos related to the user's current project are uploaded. The reception unit may also prioritize uploading highly relevant videos based on the user's area of ​​interest. The reception unit may also analyze the content of videos previously uploaded by the user and filter and upload videos with similar themes. This allows for efficient uploading of highly relevant videos by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's project data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0129] When uploading a video, the reception unit can select an appropriate upload method depending on the user's input method. For example, if the user uses voice input, the reception unit can upload the video using voice recognition technology. If the user uses text input, the reception unit can also upload the video using text analysis technology. If the user uses image input, the reception unit can also upload the video using image recognition technology. This allows for efficient video uploading by selecting the optimal upload method depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal upload method.

[0130] The reception unit can estimate the user's emotions and adjust the timing of video uploads based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can automatically delay uploading and prompt the user to upload when they are relaxed. Furthermore, if the user is in a hurry, the reception unit can have the AI ​​immediately start uploading to expedite the process. Furthermore, if the user is concentrating, the reception unit can also upload at the appropriate timing, achieving efficient processing. This allows the timing of video uploads to be adjusted according to the user's emotions, reducing the user's stress and achieving efficient uploading. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0131] The reception unit can estimate the user's emotions and determine the priority of videos to be uploaded based on the estimated user emotions. For example, if the user is feeling stressed, videos of lower importance are postponed and videos of higher importance are uploaded first. Furthermore, if the user is relaxed, the reception unit can upload all videos equally. Furthermore, if the user is in a hurry, the reception unit can upload the most important videos first. Thus, by determining the priority of videos according to the user's emotions, important videos can be uploaded first. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0132] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, a detailed analysis is performed for a video with high importance. The analysis unit can also perform a simplified analysis for a video with low importance. The analysis unit can also adjust the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0133] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, the analysis unit can apply an algorithm that analyzes the content of slides to a presentation video. The analysis unit can also apply an algorithm that analyzes the content of remarks to a discussion video. The analysis unit can also apply an algorithm that analyzes procedures and operation methods to a training video. This allows the application of an appropriate analysis algorithm depending on the category of the video, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0134] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary of the main points. By adjusting the way the analysis is presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0135] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0136] The extraction unit can estimate the user's emotions and adjust the extraction criteria based on the estimated user emotions. For example, if the user is stressed, it extracts only the most important parts. Furthermore, if the user is relaxed, the extraction unit can also extract detailed parts. Furthermore, if the user is in a hurry, the extraction unit can extract only the essential parts. By adjusting the extraction criteria according to the user's emotions, it is possible to provide optimal extraction results for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, an AI, or without an AI. For example, the extraction unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0137] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between videos during extraction. For example, the content of related videos is analyzed and important common parts are extracted. The extraction unit can also extract important parts by taking into account the interrelationships between videos. The extraction unit can also improve the accuracy of extraction based on the interrelationships between videos. In this way, the accuracy of extraction can be improved by taking into account the interrelationships between videos. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input video interrelationship data into a generation AI and cause the generation AI to improve the accuracy of extraction.

[0138] The extraction unit can perform extraction while taking into account attribute information of the person who submitted the video. For example, it can extract important parts based on the submitter's job title or field of expertise. The extraction unit can also analyze the submitter's past comments and extract important parts. The extraction unit can also improve the accuracy of extraction based on the submitter's attribute information. In this way, the accuracy of extraction can be improved by taking into account the attribute information of the video submitter. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input attribute information data of the submitter into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0139] During extraction, the extraction unit can weight the extraction based on the frequency of video submission. For example, for videos with a high submission frequency, it can prioritize extraction of more important parts. The extraction unit can also extract less important parts for videos with a low submission frequency. The extraction unit can also weight the extraction based on the submission frequency. In this way, by weighting the extraction based on the frequency of video submission, it is possible to prioritize extraction of more important parts. Some or all of the above-described processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input submission frequency data to a generation AI and have the generation AI perform extraction weighting.

[0140] The extraction unit can estimate the user's emotions and adjust the display order of the extraction results based on the estimated user emotions. For example, if the user is in a hurry, the extraction unit can display the most important parts first. Furthermore, if the user is relaxed, the extraction unit can display the results in an order that includes detailed parts. Furthermore, if the user is stressed, the extraction unit can display the results in a visually easy-to-understand order. By adjusting the display order of the extraction results according to the user's emotions, the optimal display order for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0141] The extraction unit can perform extraction while taking into account the geographic distribution of videos. For example, videos related to a specific region can be preferentially extracted. The extraction unit can also extract videos related to geographically nearby locations. The extraction unit can also extract important parts based on the geographic distribution. This allows for efficient extraction of highly relevant parts by taking the geographic distribution of videos into consideration. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input geographic distribution data of videos to the generation AI and cause the generation AI to improve the accuracy of extraction.

[0142] The extraction unit can improve the accuracy of extraction by referring to literature related to the video during extraction. For example, important parts are extracted based on the related literature. The extraction unit can also improve the accuracy of extraction by referring to literature related to the content of the video. The extraction unit can also analyze related literature and adjust the extraction criteria. In this way, by referring to literature related to the video, the accuracy of extraction can be improved. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input related literature data into the generation AI and have the generation AI improve the accuracy of extraction.

[0143] The extraction unit can perform extraction taking into account the market value of the video. For example, important parts are preferentially extracted for videos with high market value. The extraction unit can also perform simplified extraction for videos with low market value. The extraction unit can also adjust the extraction criteria based on the market value. This allows important parts to be preferentially extracted by taking the market value of the video into consideration. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, AI, or can be performed without using AI. For example, the extraction unit can input market value data into the generation AI and have the generation AI adjust the extraction criteria.

[0144] The generation unit can estimate the user's emotions and adjust the way the generated content is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. If the user is excited, the generation unit can generate a video that adds visually stimulating effects. By adjusting the way the content is presented based on the user's emotions, optimal content can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0145] The generation unit can adjust the level of detail of the generated content based on the importance of the extracted information during generation. For example, for information with high importance, the generation unit generates content including a detailed explanation. The generation unit can also generate simplified content for information with low importance. The generation unit can also adjust the level of detail of the generated content according to the importance. As a result, efficient content generation can be achieved by adjusting the level of detail of the generated content based on the importance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the extracted information to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0146] During generation, the generation unit can apply different generation algorithms depending on the content category. For example, the generation unit can apply a generation algorithm that emphasizes the content of slides to a presentation video. The generation unit can also apply a generation algorithm that emphasizes the content of remarks to a discussion video. The generation unit can also apply a generation algorithm that emphasizes procedures or operation methods to a training video. In this way, by applying an appropriate generation algorithm depending on the content category, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input content category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0147] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation algorithm can be adjusted based on feedback provided by the user in the past. The generation unit can also analyze the user's past generation results and detect similar patterns. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0148] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point content. If the user is relaxed, the generation unit can generate longer content with detailed explanations. If the user is excited, the generation unit can generate content with visually stimulating effects. By adjusting the length of the content according to the user's emotions, optimal content can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0149] During generation, the generation unit can determine the generation priority based on the submission time of the extracted information. For example, the latest information is generated with priority. The generation unit can also prioritize generating information within a period specified by the user. The generation unit can also prioritize generating information related to a specific event. This allows for efficient content generation by determining the generation priority based on the submission time of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission time data of the extracted information into a generation AI and have the generation AI determine the generation priority.

[0150] During generation, the generation unit can adjust the order of generation based on the relevance of the extracted information. For example, it can prioritize generating highly relevant information. The generation unit can also prioritize generating information related to a theme specified by the user. The generation unit can also prioritize generating highly relevant information based on past generation results. This allows for efficient content generation by adjusting the order of generation based on the relevance of the extracted information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the extracted information into the generation AI and cause the generation AI to adjust the order of generation.

[0151] During generation, the generation unit can adjust the use of technical terminology in the content to be generated according to the user's level of expertise. For example, if the user is an expert, the generation unit can generate content that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can also generate content that avoids technical terminology. The generation unit can also adjust the way the content is expressed according to the user's level of expertise. This allows for the provision of content that is easy for the user to understand by adjusting the use of technical terminology in the content according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0152] The providing unit can estimate the user's emotions and adjust the display method of the content to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. By adjusting the content display method according to the user's emotions, the optimal display method for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0153] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used favorably in the past. The providing unit can also analyze the user's past operation history and suggest the optimal display method. The providing unit can also customize the display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history data to the generating AI and cause the generating AI to select the display method.

[0154] The providing unit can customize the display content according to the user's current task when providing the display content. For example, the providing unit can prioritize displaying information related to a project the user is currently working on. The providing unit can also display highly relevant information based on the user's current task. The providing unit can also customize the display content taking into account the progress of the user's task. This makes it possible to provide highly relevant information by customizing the display content according to the user's current task. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the display content.

[0155] When providing the display information, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the display method.

[0156] The providing unit can estimate the user's emotions and adjust the operation procedures of the content to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide procedures that allow quick operation. By adjusting the operation procedures according to the user's emotions, the optimal operation procedures can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0157] The processing flow of the second embodiment will be briefly explained below.

[0158] Step 1: The reception unit allows users to upload meeting videos. Videos uploaded by users include video conferences, presentations, discussions, etc. The reception unit can automatically upload videos from a folder specified by the user, upload videos by drag and drop, or upload videos by entering a URL. Step 2: The analysis unit uses AI to analyze the uploaded video. Analysis is performed using methods such as audio analysis, video analysis, and text analysis. For example, audio analysis is used to convert what is said in the video into text, video analysis is used to identify presentation slides, and text analysis is used to extract keywords from what is said. Step 3: The extraction unit extracts important parts from the video analyzed by the analysis unit. Important parts are extracted based on the frequency of keyword appearances and the importance of speakers. For example, important parts may be extracted based on the frequency of keyword appearances, the importance of speakers, or the content of presentation slides. Step 4: The generator generates edited video and text based on the important parts extracted by the extractor. The edited video and text are generated in the form of a highlight video, a summary text, etc. For example, the generator may generate a highlight video by connecting the extracted important parts, generate text by summarizing the extracted important parts, or generate an infographic based on the extracted important parts. Step 5: The providing unit provides the edited video and text generated by the generating unit to the user. The providing is performed through a web application or a mobile application. For example, the generated edited video and text may be displayed to the user through the web application, the mobile application, or sent to the user by email.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0180] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0195] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0196] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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."

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Explanation of symbols]

[0231] 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 desk where meeting videos are uploaded, an analysis unit that analyzes the video uploaded by the reception unit; an extracting unit that extracts important parts from the video analyzed by the analyzing unit; a generation unit that generates edited video or text based on the important parts extracted by the extraction unit; a providing unit that provides the edited video or text generated by the generating unit to a user. A system characterized by:

2. The reception unit Estimate user emotions and adjust the timing of video uploads based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's upload history and select the appropriate upload method 2. The system of claim 1.

4. The reception unit Filtering videos based on your current project or interests when uploading 2. The system of claim 1.

5. The reception unit When uploading a video, select the appropriate upload method based on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate user sentiment and prioritize videos to upload based on the estimated sentiment 2. The system of claim 1.

7. The reception unit When uploading videos, prioritize uploading videos that are more relevant to you based on your geographic location 2. The system of claim 1.

8. The reception unit When uploading videos, analyze your social media activity to upload more relevant videos 2. The system of claim 1.

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