system

Abstract

An object of the system according to the embodiment is to allow a YouTube creator to effectively analyze the performance of his / her own channel or video content and make an optimal strategy.SOLUTION: A system includes a performance analysis part, a positioning analysis part, a trend analysis part, a content suggestion part, an SEO optimization part, and an action plan provision part. The performance analysis unit analyzes performance of a channel or moving image content of the creator. The positioning analysis unit analyzes the positioning of the creator's channel and the trend of competing channels. The trend analysis unit analyzes a market trend. The content suggestion unit suggests new moving image content to the creator. The SEO optimizer provides a strategy for SEO optimization of video content. The action plan providing unit provides an action plan for future growth of the creator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology made it difficult for YouTube creators to effectively analyze the performance of their channels and video content and develop optimal strategies.

[0005] The system according to the embodiment aims to enable YouTube creators to effectively analyze the performance of their own channels and video content and develop optimal strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a performance analysis unit, a positioning analysis unit, a trend analysis unit, a content suggestion unit, an SEO optimization unit, and an action plan provision unit. The performance analysis unit analyzes the performance of a creator's channel or video content. The positioning analysis unit analyzes the positioning of the creator's channel and the trends of competing channels. The trend analysis unit analyzes market trends. The content suggestion unit proposes new video content to creators. The SEO optimization unit provides strategies for SEO optimization of video content. The action plan provision unit provides action plans for the creator's future growth. [Effects of the Invention]

[0007] The system according to the embodiment enables YouTube creators to effectively analyze the performance of their own channels and video content and develop optimal strategies. [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 analysis tool according to an embodiment of the present invention is an AI-powered analysis tool specifically designed for YouTube creators. This analysis tool utilizes YouTube's vast dataset to thoroughly analyze the performance of creators' channels and video content. As a result, the analysis tool can provide specific data and insights for improving the performance of creators' channels and video content.

[0029] The analysis tool according to the embodiment includes a performance analysis unit, a positioning analysis unit, a trend analysis unit, a content suggestion unit, an SEO optimization unit, and an action plan provision unit. The performance analysis unit analyzes the performance of a creator's channel or video content. For example, it analyzes the number of views, viewing time, and engagement rate (comments, likes, shares, etc.) to identify which videos are most successful. The positioning analysis unit analyzes the positioning of the creator's channel and the trends of competing channels. For example, it compares the number of views and engagement rate of competing channels to clarify the position of the creator's channel. The trend analysis unit analyzes market trends. For example, it identifies which genres of videos are rapidly gaining popularity and which themes are popular with viewers. The content suggestion unit suggests new video content to creators. For example, it suggests what themes and formats of videos should be created based on viewer interests and market trends. The SEO optimization unit provides SEO optimization strategies for video content. For example, it suggests appropriate keyword selection and metadata optimization methods. The action plan providing unit provides an action plan for the creator's future growth. For example, it suggests what content strategy to adopt and what marketing activities to conduct. This enables the analysis tool according to the embodiment to comprehensively analyze and make suggestions to improve the performance of the YouTube creator's channel and video content.

[0030] The performance analysis unit can perform a detailed analysis of viewer viewing behavior patterns and identify at what parts of a video viewers drop off. For example, the performance analysis unit uses generative AI to analyze viewer viewing behavior data and identify at what parts of a video viewers drop off. For example, it can perform a detailed analysis of when viewers drop off during a video and identify the reasons for this. The performance analysis unit also analyzes viewer viewing behavior patterns and identifies which parts of a video are not appealing to viewers. For example, if viewers tend to drop off during a particular scene in a video, it can make suggestions to improve that scene. The performance analysis unit can also identify which parts of a video are interesting to viewers based on viewer viewing behavior data and, based on that information, make suggestions to improve the video. For example, if viewers drop off during a particular scene in a video, it can make specific suggestions to improve that scene. This makes it possible to identify viewer dropoff points and clarify areas for improvement in the video.

[0031] The performance analysis unit can evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, the performance analysis unit uses generation AI to analyze the click rates of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail designs and title keywords that are easy for viewers to click on. The performance analysis unit also uses generation AI to analyze viewer click behavior in order to evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail colors and title lengths that are easy for viewers to click on. The performance analysis unit also uses generation AI to evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail placements and title fonts that are easy for viewers to click on. In this way, by extracting effective features of thumbnails and titles, it is possible to improve viewer click rates.

[0032] The performance analysis unit can analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. The performance analysis unit, for example, uses a generative AI to analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. For example, it analyzes the voice tone and speaking style patterns that interest the viewer. The performance analysis unit also uses a generative AI to analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. For example, it identifies the characteristics of the voice tone and speaking style that interest the viewer. The performance analysis unit also uses a generative AI to analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. For example, it analyzes the voice tone and speaking style patterns that interest the viewer and suggests improvements to the video based on that information. In this way, the appeal of the video can be improved by identifying the voice tone and speaking style that elicit a viewer response.

[0033] The performance analysis unit can associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. The performance analysis unit, for example, uses a generation AI to associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. For example, the performance analysis unit analyzes viewer viewing behavior based on age and gender and suggests optimal content. The performance analysis unit also uses a generation AI to associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. For example, the performance analysis unit analyzes viewer viewing behavior based on region and interests and suggests optimal content. The performance analysis unit also uses a generation AI to associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. For example, the performance analysis unit suggests content that is optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by suggesting content that is optimal for a specific viewer segment.

[0034] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0035] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0036] The performance analysis unit can automatically generate video metadata and suggest optimal metadata. The performance analysis unit can, for example, use a generation AI to automatically generate video metadata and suggest optimal metadata. For example, it can automatically generate optimal titles and descriptions based on viewer viewing behavior data. The performance analysis unit also uses a generation AI to automatically generate video metadata and suggest optimal metadata. For example, it can automatically generate optimal tags and descriptions based on viewer viewing behavior data. The performance analysis unit can also use a generation AI to automatically generate video metadata and suggest optimal metadata. For example, it can automatically generate optimal titles and descriptions based on viewer viewing behavior data and suggest improvements to the video based on that information. In this way, by automatically generating metadata and suggesting optimal metadata, it is possible to improve video performance.

[0037] The performance analysis unit can try different combinations of metadata and identify the most effective metadata. The performance analysis unit, for example, uses a generation AI to try different combinations of metadata and identify the most effective metadata. For example, it tries optimal combinations of titles and tags based on viewer viewing behavior data. The performance analysis unit also uses a generation AI to try different combinations of metadata and identify the most effective metadata. For example, it tries optimal combinations of descriptions and tags based on viewer viewing behavior data. The performance analysis unit also uses a generation AI to try different combinations of metadata and identify the most effective metadata. For example, it tries optimal combinations of titles and descriptions based on viewer viewing behavior data, and suggests improvements to the video based on that information. In this way, by trying different combinations of metadata, it is possible to identify the most effective metadata and improve the performance of the video.

[0038] The positioning analysis unit can perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. The positioning analysis unit, for example, uses generation AI to perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. For example, it identifies overlaps or differences in the viewer demographics with competing channels based on viewer demographic data. The positioning analysis unit also uses generation AI to perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. For example, it identifies differences in the viewer demographics with competing channels based on viewer viewing behavior data. The positioning analysis unit also uses generation AI to perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. For example, it identifies overlaps or differences in the viewer demographics with competing channels based on viewer demographic data, and proposes areas for improvement for the channel based on that information. This allows for identifying overlaps or differences in the viewer demographics with competing channels, thereby enabling the development of an effective positioning strategy.

[0039] The positioning analysis unit can analyze the content strategies of competing channels and extract success factors. The positioning analysis unit, for example, uses generation AI to analyze the content strategies of competing channels and extract success factors. For example, it analyzes the number of views and engagement rates of competing channels to identify the characteristics of successful content. The positioning analysis unit also uses generation AI to analyze the content strategies of competing channels and extract success factors. For example, it identifies the elements of successful content based on viewing behavior data of competing channels. The positioning analysis unit also uses generation AI to analyze the content strategies of competing channels and extract success factors. For example, it identifies the characteristics of successful content based on the number of views and engagement rates of competing channels and suggests improvements to the channel based on that information. In this way, by extracting the success factors of competing channels, an effective content strategy can be developed.

[0040] The positioning analysis unit can analyze competing channels in different markets and regions and propose a global positioning strategy. The positioning analysis unit, for example, uses generative AI to analyze competing channels in different markets and regions and propose a global positioning strategy. For example, it identifies the characteristics of competing channels in different markets and regions based on viewer demographic data. The positioning analysis unit also uses generative AI to analyze competing channels in different markets and regions and propose a global positioning strategy. For example, it identifies the characteristics of competing channels in different markets and regions based on viewer viewing behavior data. The positioning analysis unit also uses generative AI to analyze competing channels in different markets and regions and propose a global positioning strategy. For example, it identifies the characteristics of competing channels in different markets and regions based on viewer demographic data and proposes a global positioning strategy based on that information. In this way, a global positioning strategy can be developed by analyzing competing channels in different markets and regions.

[0041] The positioning analysis unit can analyze past performance data of competing channels and predict future trends. The positioning analysis unit, for example, uses generation AI to analyze past performance data of competing channels and predict future trends. For example, it predicts future trends of competing channels based on data on the number of views and engagement rate. The positioning analysis unit also uses generation AI to analyze past performance data of competing channels and predict future trends. For example, it predicts future trends of competing channels based on data on the number of views and viewing time. The positioning analysis unit also uses generation AI to analyze past performance data of competing channels and predict future trends. For example, it predicts future trends of competing channels based on data on engagement rate and number of views, and suggests improvements to the channels based on that information. In this way, by analyzing past performance data of competing channels, future trends can be predicted and an effective positioning strategy can be developed.

[0042] The trend analysis unit can analyze viewers' search behavior in detail and identify trending keywords or topics. The trend analysis unit, for example, uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history, and based on that information, suggests areas for improving videos. In this way, by analyzing viewers' search behavior in detail, trending keywords and topics can be identified and an effective content strategy can be developed.

[0043] The trend analysis unit can analyze past trend data and build a model that predicts future trends. The trend analysis unit, for example, uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and search trends. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and viewing time. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on search trends and number of views, and improvements to videos are suggested based on that information. This makes it possible to develop an effective content strategy by predicting future trends.

[0044] The trend analysis unit can analyze trends in different markets and regions and propose a global trend strategy. The trend analysis unit, for example, uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer viewing behavior data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data and a global trend strategy is proposed based on that information. In this way, a global trend strategy can be developed by analyzing trends in different markets and regions.

[0045] The trend analysis unit can associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. The trend analysis unit, for example, uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on age and gender and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on region and interests and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it proposes trends that are optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by proposing trends that are optimal for a specific viewer segment.

[0046] The trend analysis unit can analyze viewers' search behavior in detail and identify trending keywords or topics. The trend analysis unit, for example, uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history, and based on that information, suggests areas for improving videos. In this way, by analyzing viewers' search behavior in detail, trending keywords and topics can be identified and an effective content strategy can be developed.

[0047] The trend analysis unit can analyze past trend data and build a model that predicts future trends. The trend analysis unit, for example, uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and search trends. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and viewing time. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on search trends and number of views, and improvements to videos are suggested based on that information. This makes it possible to develop an effective content strategy by predicting future trends.

[0048] The trend analysis unit can analyze trends in different markets and regions and propose a global trend strategy. The trend analysis unit, for example, uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer viewing behavior data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data and a global trend strategy is proposed based on that information. In this way, a global trend strategy can be developed by analyzing trends in different markets and regions.

[0049] The trend analysis unit can associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. The trend analysis unit, for example, uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on age and gender and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on region and interests and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it proposes trends that are optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by proposing trends that are optimal for a specific viewer segment.

[0050] The trend analysis unit can analyze viewers' search behavior in detail and identify trending keywords or topics. The trend analysis unit, for example, uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history, and based on that information, suggests areas for improving videos. In this way, by analyzing viewers' search behavior in detail, trending keywords and topics can be identified and an effective content strategy can be developed.

[0051] The trend analysis unit can analyze past trend data and build a model that predicts future trends. The trend analysis unit, for example, uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and search trends. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and viewing time. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on search trends and number of views, and improvements to videos are suggested based on that information. This makes it possible to develop an effective content strategy by predicting future trends.

[0052] The trend analysis unit can analyze trends in different markets and regions and propose a global trend strategy. The trend analysis unit, for example, uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer viewing behavior data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data and a global trend strategy is proposed based on that information. In this way, a global trend strategy can be developed by analyzing trends in different markets and regions.

[0053] The trend analysis unit can associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. The trend analysis unit, for example, uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on age and gender and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on region and interests and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it proposes trends that are optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by proposing trends that are optimal for a specific viewer segment.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The performance analysis unit can perform detailed analysis of viewer viewing behavior patterns and identify the parts of a video at which viewers drop off. For example, it can perform detailed analysis of when viewers drop off during a video and identify the reasons for this. It can also analyze viewer viewing behavior patterns and identify which parts of a video are not appealing to viewers. For example, if viewers tend to drop off at a particular scene in a video, it can make suggestions to improve that scene. It can also identify which parts of a video are interesting to viewers based on viewer viewing behavior data and, based on that information, make suggestions to improve the video. For example, if a viewer drops off at a particular scene in a video, it can make specific suggestions to improve that scene. This makes it possible to identify the points at which viewers drop off, thereby clarifying the areas in the video that need improvement.

[0056] The performance analysis unit can evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail designs and title keywords that are easy for viewers to click on. It also analyzes viewer click behavior to evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail colors and title lengths that are easy for viewers to click on. It also evaluates the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail placement and title fonts that are easy for viewers to click on. In this way, by extracting effective features of thumbnails and titles, it is possible to improve viewer click rates.

[0057] The performance analysis unit can analyze the audio data of a video and identify the voice tone and speaking style that elicit a viewer's response. For example, it can analyze the voice tone and speaking style patterns that interest viewers. It also uses generative AI to analyze the audio data of a video and identify the voice tone and speaking style that elicit a viewer's response. For example, it can identify the voice tone and speaking style characteristics that interest viewers. It also analyzes the audio data of a video and identify the voice tone and speaking style that elicit a viewer's response. For example, it can analyze the voice tone and speaking style patterns that interest viewers and suggest areas for improvement to the video based on that information. This makes it possible to increase the appeal of a video by identifying the voice tone and speaking style that elicits a viewer's response.

[0058] The performance analysis unit associates viewer demographic data with viewing behavior to suggest optimal content for a specific viewer segment. For example, it analyzes viewer viewing behavior based on age and gender to suggest optimal content. It also uses generative AI to associate viewer demographic data with viewing behavior to suggest optimal content for a specific viewer segment. For example, it analyzes viewer viewing behavior based on region and interests to suggest optimal content. It also associates viewer demographic data with viewing behavior to suggest optimal content for a specific viewer segment. For example, it suggests optimal content for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by suggesting optimal content for a specific viewer segment.

[0059] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, it automatically collects related background information and references it to understand the context. For example, it collects related news articles and academic papers. In addition, a topic model is used to understand the context when the generation AI creates a summary. For example, related keywords and phrases are extracted based on the topic model. In addition, when the generation AI creates a summary, it references related background information and topic models to build a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context.

[0060] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the generation AI analyzes the logical structure of an answer and generates a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. In addition, a system is constructed in which the development of arguments in an answer is analyzed and the generation AI generates a logical summary. For example, a summary is generated based on the importance and relevance of the arguments. In addition, an algorithm is developed in which the generation AI analyzes the logical structure of an answer and the development of the arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of the arguments. In this way, a logical summary can be generated by analyzing the logical structure of an answer and the development of the arguments.

[0061] The performance analysis unit can automatically generate video metadata and suggest optimal metadata. For example, it uses generation AI to automatically generate video metadata and suggest optimal metadata. For example, it automatically generates optimal titles and descriptions based on viewer viewing behavior data. In addition, it uses generation AI to automatically generate video metadata and suggest optimal metadata. For example, it automatically generates optimal tags and descriptions based on viewer viewing behavior data. In addition, it uses generation AI to automatically generate video metadata and suggest optimal metadata. For example, it automatically generates optimal titles and descriptions based on viewer viewing behavior data and suggests areas for improvement to the video based on that information. In this way, by automatically generating metadata and suggesting optimal metadata, it is possible to improve video performance.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The Performance Analytics department analyzes the performance of a creator's channel or video content, for example, by analyzing the number of views, watch time, and engagement rate (comments, likes, shares, etc.) to identify which videos are most successful. Step 2: The Positioning Analysis Department analyzes the positioning of the creator's channel and the trends of competing channels. For example, it compares the number of views and engagement rates of competing channels to clarify where the creator's channel is located. Step 3: The trend analysis department analyzes market trends, for example, identifying which genres of videos are rapidly gaining popularity and what themes are popular with viewers. Step 4: The content suggestion department suggests new video content to creators, for example, what themes and formats of videos should be created based on viewer interests and market trends. Step 5: The SEO optimization department provides strategies for optimizing the SEO of your video content, such as suggesting appropriate keyword selection and metadata optimization. Step 6: The Action Plan Providing Department provides an action plan for the creator's future growth, such as what content strategy should be adopted and what marketing activities should be carried out.

[0064] (Example 2) An analysis tool according to an embodiment of the present invention is an AI-powered analysis tool specifically designed for YouTube creators. This analysis tool utilizes YouTube's vast dataset to thoroughly analyze the performance of creators' channels and video content. As a result, the analysis tool can provide specific data and insights for improving the performance of creators' channels and video content.

[0065] The analysis tool according to the embodiment includes a performance analysis unit, a positioning analysis unit, a trend analysis unit, a content suggestion unit, an SEO optimization unit, and an action plan provision unit. The performance analysis unit analyzes the performance of a creator's channel or video content. For example, it analyzes the number of views, viewing time, and engagement rate (comments, likes, shares, etc.) to identify which videos are most successful. The positioning analysis unit analyzes the positioning of the creator's channel and the trends of competing channels. For example, it compares the number of views and engagement rate of competing channels to clarify the position of the creator's channel. The trend analysis unit analyzes market trends. For example, it identifies which genres of videos are rapidly gaining popularity and which themes are popular with viewers. The content suggestion unit suggests new video content to creators. For example, it suggests what themes and formats of videos should be created based on viewer interests and market trends. The SEO optimization unit provides SEO optimization strategies for video content. For example, it suggests appropriate keyword selection and metadata optimization methods. The action plan providing unit provides an action plan for the creator's future growth. For example, it suggests what content strategy to adopt and what marketing activities to conduct. This enables the analysis tool according to the embodiment to comprehensively analyze and make suggestions to improve the performance of the YouTube creator's channel and video content.

[0066] The performance analysis unit can perform a detailed analysis of viewer viewing behavior patterns and identify at what parts of a video viewers drop off. For example, the performance analysis unit uses generative AI to analyze viewer viewing behavior data and identify at what parts of a video viewers drop off. For example, it can perform a detailed analysis of when viewers drop off during a video and identify the reasons for this. The performance analysis unit also analyzes viewer viewing behavior patterns and identifies which parts of a video are not appealing to viewers. For example, if viewers tend to drop off during a particular scene in a video, it can make suggestions to improve that scene. The performance analysis unit can also identify which parts of a video are interesting to viewers based on viewer viewing behavior data and, based on that information, make suggestions to improve the video. For example, if viewers drop off during a particular scene in a video, it can make specific suggestions to improve that scene. This makes it possible to identify viewer dropoff points and clarify areas for improvement in the video.

[0067] The performance analysis unit can evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, the performance analysis unit uses generation AI to analyze the click rates of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail designs and title keywords that are easy for viewers to click on. The performance analysis unit also uses generation AI to analyze viewer click behavior in order to evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail colors and title lengths that are easy for viewers to click on. The performance analysis unit also uses generation AI to evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail placements and title fonts that are easy for viewers to click on. In this way, by extracting effective features of thumbnails and titles, it is possible to improve viewer click rates.

[0068] The performance analysis unit can use the emotion estimation function to analyze emotions from viewer comments and reactions and identify video elements that elicit positive emotions. The performance analysis unit, for example, uses the emotion estimation function to analyze emotions from viewer comments and reactions and identify video elements that elicit positive emotions. For example, the performance analysis unit analyzes emotion from viewer comments and reactions and identifies video scenes that elicit positive emotions. The performance analysis unit also uses the emotion estimation function to analyze emotions from viewer comments and reactions and identify video elements that elicit positive emotions. For example, the performance analysis unit analyzes emotion from viewer comments and reactions and identifies video elements that elicit positive emotions. For example, the performance analysis unit identifies video elements that elicit positive emotions based on the emotion scores of viewer comments. In this way, by analyzing viewer emotions, video elements that elicit positive emotions can be identified and the quality of the video can be improved.

[0069] The performance analysis unit can analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. The performance analysis unit, for example, uses a generative AI to analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. For example, it analyzes the voice tone and speaking style patterns that interest the viewer. The performance analysis unit also uses a generative AI to analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. For example, it identifies the characteristics of the voice tone and speaking style that interest the viewer. The performance analysis unit also uses a generative AI to analyze the audio data of the video and identify the voice tone and speaking style that elicit a viewer response. For example, it analyzes the voice tone and speaking style patterns that interest the viewer and suggests improvements to the video based on that information. In this way, the appeal of the video can be improved by identifying the voice tone and speaking style that elicit a viewer response.

[0070] The performance analysis unit can associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. The performance analysis unit, for example, uses a generation AI to associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. For example, the performance analysis unit analyzes viewer viewing behavior based on age and gender and suggests optimal content. The performance analysis unit also uses a generation AI to associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. For example, the performance analysis unit analyzes viewer viewing behavior based on region and interests and suggests optimal content. The performance analysis unit also uses a generation AI to associate viewer demographic data with viewing behavior and suggest content that is optimal for a specific viewer segment. For example, the performance analysis unit suggests content that is optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by suggesting content that is optimal for a specific viewer segment.

[0071] The performance analysis unit can use the emotion estimation function to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. The performance analysis unit, for example, uses the emotion estimation function to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. For example, the emotion score of the viewer reactions is analyzed and elements that elicit positive emotions are inserted into the video. The performance analysis unit also uses the emotion estimation function to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. For example, elements that elicit positive emotions are inserted into the video based on the emotion score of the viewer reactions. The performance analysis unit also uses the emotion estimation function to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. For example, elements that elicit positive emotions are inserted into the video based on the emotion score of the viewer reactions. In this way, by analyzing viewer reactions in real time and inserting elements that elicit emotions into the video, viewer engagement can be improved.

[0072] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0073] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0074] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0075] The performance analysis unit can automatically generate video metadata and suggest optimal metadata. The performance analysis unit can, for example, use a generation AI to automatically generate video metadata and suggest optimal metadata. For example, it can automatically generate optimal titles and descriptions based on viewer viewing behavior data. The performance analysis unit also uses a generation AI to automatically generate video metadata and suggest optimal metadata. For example, it can automatically generate optimal tags and descriptions based on viewer viewing behavior data. The performance analysis unit can also use a generation AI to automatically generate video metadata and suggest optimal metadata. For example, it can automatically generate optimal titles and descriptions based on viewer viewing behavior data and suggest improvements to the video based on that information. In this way, by automatically generating metadata and suggesting optimal metadata, it is possible to improve video performance.

[0076] The performance analysis unit can try different combinations of metadata and identify the most effective metadata. The performance analysis unit, for example, uses a generation AI to try different combinations of metadata and identify the most effective metadata. For example, it tries optimal combinations of titles and tags based on viewer viewing behavior data. The performance analysis unit also uses a generation AI to try different combinations of metadata and identify the most effective metadata. For example, it tries optimal combinations of descriptions and tags based on viewer viewing behavior data. The performance analysis unit also uses a generation AI to try different combinations of metadata and identify the most effective metadata. For example, it tries optimal combinations of titles and descriptions based on viewer viewing behavior data, and suggests improvements to the video based on that information. In this way, by trying different combinations of metadata, it is possible to identify the most effective metadata and improve the performance of the video.

[0077] The performance analysis unit can use the emotion estimation function to optimize metadata based on the emotional reactions of viewers. The performance analysis unit, for example, uses the emotion estimation function to optimize metadata based on the emotional reactions of viewers. For example, the emotion scores of viewers' comments are analyzed to optimize metadata that elicits positive emotions. The performance analysis unit also uses the emotion estimation function to optimize metadata based on viewers' emotional reactions. For example, the emotion scores of viewers' feedback are optimized to optimize metadata that elicits positive emotions. The performance analysis unit also uses the emotion estimation function to optimize metadata based on viewers' emotional reactions. For example, the emotion scores of viewers' comments are optimized to optimize metadata that elicits positive emotions. In this way, by optimizing metadata based on viewers' emotional reactions, it is possible to improve the performance of the video.

[0078] The positioning analysis unit can perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. The positioning analysis unit, for example, uses generation AI to perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. For example, it identifies overlaps or differences in the viewer demographics with competing channels based on viewer demographic data. The positioning analysis unit also uses generation AI to perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. For example, it identifies differences in the viewer demographics with competing channels based on viewer viewing behavior data. The positioning analysis unit also uses generation AI to perform a detailed analysis of the viewer demographics of competing channels and identify overlaps or differences in the viewer demographics. For example, it identifies overlaps or differences in the viewer demographics with competing channels based on viewer demographic data, and proposes areas for improvement for the channel based on that information. This allows for identifying overlaps or differences in the viewer demographics with competing channels, thereby enabling the development of an effective positioning strategy.

[0079] The positioning analysis unit can analyze the content strategies of competing channels and extract success factors. The positioning analysis unit, for example, uses generation AI to analyze the content strategies of competing channels and extract success factors. For example, it analyzes the number of views and engagement rates of competing channels to identify the characteristics of successful content. The positioning analysis unit also uses generation AI to analyze the content strategies of competing channels and extract success factors. For example, it identifies the elements of successful content based on viewing behavior data of competing channels. The positioning analysis unit also uses generation AI to analyze the content strategies of competing channels and extract success factors. For example, it identifies the characteristics of successful content based on the number of views and engagement rates of competing channels and suggests improvements to the channel based on that information. In this way, by extracting the success factors of competing channels, an effective content strategy can be developed.

[0080] The positioning analysis unit can use the emotion estimation function to analyze the emotional reactions of viewers of competing channels and identify elements that elicit positive emotions. The positioning analysis unit, for example, uses the emotion estimation function to analyze the emotional reactions of viewers of competing channels and identify elements that elicit positive emotions. For example, the positioning analysis unit analyzes the emotional reactions of viewers of competing channels and identifies elements that elicit positive emotions. For example, the positioning analysis unit analyzes the emotional reactions of viewers of competing channels and identifies elements that elicit positive emotions based on the emotion scores of viewers' reactions of competing channels. The positioning analysis unit also uses the emotion estimation function to analyze the emotional reactions of viewers of competing channels and identify elements that elicit positive emotions. For example, the positioning analysis unit identifies elements that elicit positive emotions based on the emotion scores of viewers' comments of competing channels and suggests improvements to the channel based on that information. In this way, by analyzing the emotional reactions of viewers of competing channels, elements that elicit positive emotions can be identified and an effective positioning strategy can be developed.

[0081] The positioning analysis unit can analyze competing channels in different markets and regions and propose a global positioning strategy. The positioning analysis unit, for example, uses generative AI to analyze competing channels in different markets and regions and propose a global positioning strategy. For example, it identifies the characteristics of competing channels in different markets and regions based on viewer demographic data. The positioning analysis unit also uses generative AI to analyze competing channels in different markets and regions and propose a global positioning strategy. For example, it identifies the characteristics of competing channels in different markets and regions based on viewer viewing behavior data. The positioning analysis unit also uses generative AI to analyze competing channels in different markets and regions and propose a global positioning strategy. For example, it identifies the characteristics of competing channels in different markets and regions based on viewer demographic data and proposes a global positioning strategy based on that information. In this way, a global positioning strategy can be developed by analyzing competing channels in different markets and regions.

[0082] The positioning analysis unit can analyze past performance data of competing channels and predict future trends. The positioning analysis unit, for example, uses generation AI to analyze past performance data of competing channels and predict future trends. For example, it predicts future trends of competing channels based on data on the number of views and engagement rate. The positioning analysis unit also uses generation AI to analyze past performance data of competing channels and predict future trends. For example, it predicts future trends of competing channels based on data on the number of views and viewing time. The positioning analysis unit also uses generation AI to analyze past performance data of competing channels and predict future trends. For example, it predicts future trends of competing channels based on data on engagement rate and number of views, and suggests improvements to the channels based on that information. In this way, by analyzing past performance data of competing channels, future trends can be predicted and an effective positioning strategy can be developed.

[0083] The positioning analysis unit can use the emotion estimation function to optimize the positioning of the creator's channel based on the emotional reactions of viewers of competing channels. The positioning analysis unit, for example, uses the emotion estimation function to optimize the positioning of the creator's channel based on the emotional reactions of viewers of competing channels. For example, it analyzes the emotion scores of comments from viewers of competing channels and proposes positioning that elicits positive emotions. The positioning analysis unit also uses the emotion estimation function to optimize the positioning of the creator's channel based on the emotional reactions of viewers of competing channels. For example, it proposes positioning that elicits positive emotions based on the emotion scores of reactions from viewers of competing channels. The positioning analysis unit also uses the emotion estimation function to optimize the positioning of the creator's channel based on the emotional reactions of viewers of competing channels. For example, it proposes positioning that elicits positive emotions based on the emotion scores of comments from viewers of competing channels and proposes channel improvements based on that information. In this way, by optimizing the positioning of the creator's channel based on the emotional reactions of viewers of competing channels, viewer engagement can be improved.

[0084] The trend analysis unit can analyze viewers' search behavior in detail and identify trending keywords or topics. The trend analysis unit, for example, uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history, and based on that information, suggests areas for improving videos. In this way, by analyzing viewers' search behavior in detail, trending keywords and topics can be identified and an effective content strategy can be developed.

[0085] The trend analysis unit can analyze past trend data and build a model that predicts future trends. The trend analysis unit, for example, uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and search trends. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and viewing time. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on search trends and number of views, and improvements to videos are suggested based on that information. This makes it possible to develop an effective content strategy by predicting future trends.

[0086] The trend analysis unit can use the emotion estimation function to analyze emotions from viewer comments and reactions and identify trends that evoke positive emotions. The trend analysis unit, for example, uses the emotion estimation function to analyze emotions from viewer comments and reactions and identify trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions based on the emotion scores of viewer reactions. The trend analysis unit also uses the emotion estimation function to analyze emotions from viewer comments and identify trends that evoke positive emotions. For example, the trend analysis unit identifies trends that evoke positive emotions based on the emotion scores of viewer comments and suggests improvements to the video based on that information. In this way, by analyzing viewer emotions, trends that evoke positive emotions can be identified and an effective content strategy can be developed.

[0087] The trend analysis unit can analyze trends in different markets and regions and propose a global trend strategy. The trend analysis unit, for example, uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer viewing behavior data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data and a global trend strategy is proposed based on that information. In this way, a global trend strategy can be developed by analyzing trends in different markets and regions.

[0088] The trend analysis unit can associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. The trend analysis unit, for example, uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on age and gender and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on region and interests and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it proposes trends that are optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by proposing trends that are optimal for a specific viewer segment.

[0089] The trend analysis unit can optimize trends based on the emotional responses of viewers using the emotion estimation function. The trend analysis unit, for example, uses the emotion estimation function to optimize trends based on the emotional responses of viewers. For example, it analyzes the emotion scores of viewers' comments and optimizes trends that elicit positive emotions. The trend analysis unit also uses the emotion estimation function to optimize trends based on viewers' emotional responses. For example, it optimizes trends that elicit positive emotions based on the emotion scores of viewers' feedback. The trend analysis unit also uses the emotion estimation function to optimize trends based on viewers' emotional responses. For example, it optimizes trends that elicit positive emotions based on the emotion scores of viewers' comments. In this way, by optimizing trends based on viewers' emotional responses, viewer satisfaction can be improved.

[0090] The trend analysis unit can analyze viewers' search behavior in detail and identify trending keywords or topics. The trend analysis unit, for example, uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history, and based on that information, suggests areas for improving videos. In this way, by analyzing viewers' search behavior in detail, trending keywords and topics can be identified and an effective content strategy can be developed.

[0091] The trend analysis unit can analyze past trend data and build a model that predicts future trends. The trend analysis unit, for example, uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and search trends. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and viewing time. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on search trends and number of views, and improvements to videos are suggested based on that information. This makes it possible to develop an effective content strategy by predicting future trends.

[0092] The trend analysis unit can use the emotion estimation function to analyze emotions from viewer comments and reactions and identify trends that evoke positive emotions. The trend analysis unit, for example, uses the emotion estimation function to analyze emotions from viewer comments and reactions and identify trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions based on the emotion scores of viewer reactions. The trend analysis unit also uses the emotion estimation function to analyze emotions from viewer comments and identify trends that evoke positive emotions. For example, the trend analysis unit identifies trends that evoke positive emotions based on the emotion scores of viewer comments and suggests improvements to the video based on that information. In this way, by analyzing viewer emotions, trends that evoke positive emotions can be identified and an effective content strategy can be developed.

[0093] The trend analysis unit can analyze trends in different markets and regions and propose a global trend strategy. The trend analysis unit, for example, uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer viewing behavior data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data and a global trend strategy is proposed based on that information. In this way, a global trend strategy can be developed by analyzing trends in different markets and regions.

[0094] The trend analysis unit can associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. The trend analysis unit, for example, uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on age and gender and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on region and interests and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it proposes trends that are optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by proposing trends that are optimal for a specific viewer segment.

[0095] The trend analysis unit can optimize trends based on the emotional responses of viewers using the emotion estimation function. The trend analysis unit, for example, uses the emotion estimation function to optimize trends based on the emotional responses of viewers. For example, it analyzes the emotion scores of viewers' comments and optimizes trends that elicit positive emotions. The trend analysis unit also uses the emotion estimation function to optimize trends based on viewers' emotional responses. For example, it optimizes trends that elicit positive emotions based on the emotion scores of viewers' feedback. The trend analysis unit also uses the emotion estimation function to optimize trends based on viewers' emotional responses. For example, it optimizes trends that elicit positive emotions based on the emotion scores of viewers' comments. In this way, by optimizing trends based on viewers' emotional responses, viewer satisfaction can be improved.

[0096] The trend analysis unit can analyze viewers' search behavior in detail and identify trending keywords or topics. The trend analysis unit, for example, uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history. The trend analysis unit also uses generation AI to analyze viewers' search behavior in detail and identify trending keywords or topics. For example, trending keywords or topics are identified based on viewers' search history, and based on that information, suggests areas for improving videos. In this way, by analyzing viewers' search behavior in detail, trending keywords and topics can be identified and an effective content strategy can be developed.

[0097] The trend analysis unit can analyze past trend data and build a model that predicts future trends. The trend analysis unit, for example, uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and search trends. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on the number of views and viewing time. The trend analysis unit also uses a generative AI to analyze past trend data and build a model that predicts future trends. For example, future trends are predicted based on data on search trends and number of views, and improvements to videos are suggested based on that information. This makes it possible to develop an effective content strategy by predicting future trends.

[0098] The trend analysis unit can use the emotion estimation function to analyze emotions from viewer comments and reactions and identify trends that evoke positive emotions. The trend analysis unit, for example, uses the emotion estimation function to analyze emotions from viewer comments and reactions and identify trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions. For example, the trend analysis unit analyzes emotion from viewer comments and reactions and identifies trends that evoke positive emotions based on the emotion scores of viewer reactions. The trend analysis unit also uses the emotion estimation function to analyze emotions from viewer comments and identify trends that evoke positive emotions. For example, the trend analysis unit identifies trends that evoke positive emotions based on the emotion scores of viewer comments and suggests improvements to the video based on that information. In this way, by analyzing viewer emotions, trends that evoke positive emotions can be identified and an effective content strategy can be developed.

[0099] The trend analysis unit can analyze trends in different markets and regions and propose a global trend strategy. The trend analysis unit, for example, uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer viewing behavior data. The trend analysis unit also uses generation AI to analyze trends in different markets and regions and propose a global trend strategy. For example, trends in different markets and regions are identified based on viewer demographic data and a global trend strategy is proposed based on that information. In this way, a global trend strategy can be developed by analyzing trends in different markets and regions.

[0100] The trend analysis unit can associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. The trend analysis unit, for example, uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on age and gender and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it analyzes viewer trends based on region and interests and proposes optimal trends. The trend analysis unit also uses generation AI to associate viewer demographic data with trends and propose trends that are optimal for a specific viewer segment. For example, it proposes trends that are optimal for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by proposing trends that are optimal for a specific viewer segment.

[0101] The trend analysis unit can optimize trends based on the emotional responses of viewers using the emotion estimation function. The trend analysis unit, for example, uses the emotion estimation function to optimize trends based on the emotional responses of viewers. For example, it analyzes the emotion scores of viewers' comments and optimizes trends that elicit positive emotions. The trend analysis unit also uses the emotion estimation function to optimize trends based on viewers' emotional responses. For example, it optimizes trends that elicit positive emotions based on the emotion scores of viewers' feedback. The trend analysis unit also uses the emotion estimation function to optimize trends based on viewers' emotional responses. For example, it optimizes trends that elicit positive emotions based on the emotion scores of viewers' comments. In this way, by optimizing trends based on viewers' emotional responses, viewer satisfaction can be improved.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The performance analysis unit can perform detailed analysis of viewer viewing behavior patterns and identify the parts of a video at which viewers drop off. For example, it can perform detailed analysis of when viewers drop off during a video and identify the reasons for this. It can also analyze viewer viewing behavior patterns and identify which parts of a video are not appealing to viewers. For example, if viewers tend to drop off at a particular scene in a video, it can make suggestions to improve that scene. It can also identify which parts of a video are interesting to viewers based on viewer viewing behavior data and, based on that information, make suggestions to improve the video. For example, if a viewer drops off at a particular scene in a video, it can make specific suggestions to improve that scene. This makes it possible to identify the points at which viewers drop off, thereby clarifying the areas in the video that need improvement.

[0104] The performance analysis unit can evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail designs and title keywords that are easy for viewers to click on. It also analyzes viewer click behavior to evaluate the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail colors and title lengths that are easy for viewers to click on. It also evaluates the effectiveness of video thumbnails and titles and extract the most effective features of thumbnails and titles. For example, it identifies thumbnail placement and title fonts that are easy for viewers to click on. In this way, by extracting effective features of thumbnails and titles, it is possible to improve viewer click rates.

[0105] The performance analysis unit can use the emotion estimation function to analyze emotions from viewer comments and reactions and identify video elements that elicit positive emotions. For example, the emotion estimation function can analyze the emotion scores of viewer comments and identify video scenes that elicit positive emotions. The emotion estimation function can also be used to analyze emotions from viewer comments and reactions and identify video elements that elicit positive emotions. For example, the emotion estimation function can analyze the emotion scores of viewer reactions and identify video elements that elicit positive emotions. The emotion estimation function can also be used to analyze emotions from viewer comments and reactions and identify video elements that elicit positive emotions. For example, the emotion estimation function can identify video elements that elicit positive emotions based on the emotion scores of viewer comments. In this way, by analyzing viewer emotions, it is possible to identify video elements that elicit positive emotions and improve the quality of videos.

[0106] The performance analysis unit can analyze the audio data of a video and identify the voice tone and speaking style that elicit a viewer's response. For example, it can analyze the voice tone and speaking style patterns that interest viewers. It also uses generative AI to analyze the audio data of a video and identify the voice tone and speaking style that elicit a viewer's response. For example, it can identify the voice tone and speaking style characteristics that interest viewers. It also analyzes the audio data of a video and identify the voice tone and speaking style that elicit a viewer's response. For example, it can analyze the voice tone and speaking style patterns that interest viewers and suggest areas for improvement to the video based on that information. This makes it possible to increase the appeal of a video by identifying the voice tone and speaking style that elicits a viewer's response.

[0107] The performance analysis unit associates viewer demographic data with viewing behavior to suggest optimal content for a specific viewer segment. For example, it analyzes viewer viewing behavior based on age and gender to suggest optimal content. It also uses generative AI to associate viewer demographic data with viewing behavior to suggest optimal content for a specific viewer segment. For example, it analyzes viewer viewing behavior based on region and interests to suggest optimal content. It also associates viewer demographic data with viewing behavior to suggest optimal content for a specific viewer segment. For example, it suggests optimal content for a specific viewer segment based on viewer demographic data. This makes it possible to improve viewer satisfaction by suggesting optimal content for a specific viewer segment.

[0108] The performance analysis unit can use the emotion estimation function to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. For example, the emotion score of the viewer reactions can be analyzed and elements that elicit positive emotions can be inserted into the video. The emotion estimation function can also be used to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. For example, elements that elicit positive emotions can be inserted into the video based on the emotion score of the viewer reactions. The performance analysis unit can use the emotion estimation function to analyze viewer reactions in real time and insert elements that elicit emotions in the middle of the video. For example, elements that elicit positive emotions can be inserted into the video based on the emotion score of the viewer reactions. In this way, by analyzing viewer reactions in real time and inserting elements that elicit emotions in the video, viewer engagement can be improved.

[0109] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, it automatically collects related background information and references it to understand the context. For example, it collects related news articles and academic papers. In addition, a topic model is used to understand the context when the generation AI creates a summary. For example, related keywords and phrases are extracted based on the topic model. In addition, when the generation AI creates a summary, it references related background information and topic models to build a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This allows for more accurate summaries by referring to background information and topic models to understand the context.

[0110] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the generation AI analyzes the logical structure of an answer and generates a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. In addition, a system is constructed in which the development of arguments in an answer is analyzed and the generation AI generates a logical summary. For example, a summary is generated based on the importance and relevance of the arguments. In addition, an algorithm is developed in which the generation AI analyzes the logical structure of an answer and the development of the arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of the arguments. In this way, a logical summary can be generated by analyzing the logical structure of an answer and the development of the arguments.

[0111] The summary generation unit uses the emotion estimation function to generate summaries that capture the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, it uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. In addition, using the emotion estimation function, a system is built in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, the evaluation is based on the emotion score. In addition, an algorithm is developed in which the generation AI uses the emotion estimation function to generate summaries that capture the emotional nuances of the answer. For example, a summary is generated based on the emotion score and reflected in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0112] The performance analysis unit can automatically generate video metadata and suggest optimal metadata. For example, it uses generation AI to automatically generate video metadata and suggest optimal metadata. For example, it automatically generates optimal titles and descriptions based on viewer viewing behavior data. In addition, it uses generation AI to automatically generate video metadata and suggest optimal metadata. For example, it automatically generates optimal tags and descriptions based on viewer viewing behavior data. In addition, it uses generation AI to automatically generate video metadata and suggest optimal metadata. For example, it automatically generates optimal titles and descriptions based on viewer viewing behavior data and suggests areas for improvement to the video based on that information. In this way, by automatically generating metadata and suggesting optimal metadata, it is possible to improve video performance.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The Performance Analytics department analyzes the performance of a creator's channel or video content, for example, by analyzing the number of views, watch time, and engagement rate (comments, likes, shares, etc.) to identify which videos are most successful. Step 2: The Positioning Analysis Department analyzes the positioning of the creator's channel and the trends of competing channels. For example, it compares the number of views and engagement rates of competing channels to clarify where the creator's channel is located. Step 3: The trend analysis department analyzes market trends, for example, identifying which genres of videos are rapidly gaining popularity and what themes are popular with viewers. Step 4: The content suggestion department suggests new video content to creators, for example, what themes and formats of videos should be created based on viewer interests and market trends. Step 5: The SEO optimization department provides strategies for optimizing the SEO of your video content, such as suggesting appropriate keyword selection and metadata optimization. Step 6: The Action Plan Providing Department provides an action plan for the creator's future growth, such as what content strategy should be adopted and what marketing activities should be carried out.

[0115] 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.

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

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

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

[0121] The 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.

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

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

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

[0125] Fig. 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.

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

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

[0128] In the 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0132] The data processing system 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0136] The 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.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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 AI 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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 AI 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0182] 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 system equipped with a generative AI, The generated AI is A performance analysis department that analyzes the performance of creators' channels or video content; a positioning analysis unit that analyzes the positioning of the creator's channel and trends of competing channels; A trend analysis department that analyzes market trends; a content suggestion unit that proposes new video content to the creator; an SEO optimization unit that provides a strategy for the SEO optimization of the video content; an action plan providing unit that provides an action plan for the creator's future growth; A system characterized by:

2. The performance analysis unit Analyze viewers' viewing patterns in detail and identify the part of the video at which the viewer leaves 2. The system of claim 1.

3. The performance analysis unit Evaluating the effectiveness of thumbnails or titles of videos and extracting the most effective features of the thumbnails or titles 2. The system of claim 1.

4. The performance analysis unit Analyze emotions from viewers' comments or reactions and identify video elements that elicit positive emotions 2. The system of claim 1.

5. The performance analysis unit Analyzing the audio data of a video to identify the tone or speaking style that elicits a response from the viewer 2. The system of claim 1.

6. The performance analysis unit Linking viewer demographic data with viewing behavior to suggest content best suited to specific audiences 2. The system of claim 1.

Images