System

Abstract

An object of a system according to an embodiment is to easily create an explanatory video.SOLUTION: A system according to an embodiment includes a reception unit, a data generation unit, and a moving image generation unit. The reception unit receives an input of moving image content from a user. The data generation unit analyzes the information received by the reception unit and generates necessary data and output. The animation generator aggregates the data and outputs generated by the data generator to generate the final narrative animation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring a great deal of time and effort to create videos, which is time-consuming and costly.

[0005] The system according to the embodiment aims to easily create explanatory videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a data generation unit, and a video generation unit. The reception unit receives input of video content from a user. The data generation unit analyzes the information received by the reception unit and generates necessary data and output. The video generation unit aggregates the data and output generated by the data generation unit and generates a final explanatory video. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily create explanatory videos. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The iPaaS solution according to an embodiment of the present invention is a system that uses video generation AI to easily create explanatory videos for business use. This system allows users to input the content of the video they want, and the video generation AI analyzes that content, generates the necessary data and output, and finally produces the explanatory video. For example, if a user inputs that they want to create a video introducing a new product, the video generation AI analyzes the information and generates diagrams, tables, and narration to explain the product's features. The generated data and output are then aggregated to create a visually appealing explanatory video. This revolutionizes PowerPoint creation and makes it commonplace to create documents such as proposals, business plans, and management meeting materials in video format. This allows the iPaaS solution to easily create compelling explanatory videos without requiring users to perform complex operations. For example, creating management meeting materials in video format enables visually appealing presentations and accelerates decision-making.

[0029] The iPaaS solution according to the embodiment includes a reception unit, a data generation unit, and a video generation unit. The reception unit receives input of video content from a user. For example, the user may input that they would like to create an introductory video for a new product. The data generation unit analyzes the information received by the reception unit and generates the necessary data and output. The data generation unit generates, for example, tables, diagrams, captions, narration, music, etc. The video generation unit aggregates the data and output generated by the data generation unit and generates a final explanatory video. For example, the video generation unit generates a video with narration while displaying the generated diagrams and tables. As a result, the iPaaS solution according to the embodiment can generate the necessary data and output based on the information input by the user and create the final explanatory video.

[0030] The data generation unit can generate tables and figures, subtitles, narration, and music. The data generation unit can generate, for example, bar graphs, pie charts, and tabular data. The data generation unit can also generate subtitles, captions, text overlays, and other subtitles. Furthermore, the data generation unit can generate narration using voice synthesis or recorded voice. The data generation unit can also generate music such as background music and sound effects. As a result, the data generation unit can generate a variety of data and outputs, thereby enriching the content of the explainer video.

[0031] The video generation unit can generate explanatory videos by combining the generated data and output. The video generation unit, for example, places the generated diagrams and tables on a timeline and sets the scene transition method. The video generation unit can also adjust the progress of the video by placing the generated captions and narration in appropriate positions. Furthermore, the video generation unit can adjust the timing of the generated music and sound effects to enhance the presentation of the video. In this way, by effectively combining the generated data and output, it is possible to create visually appealing explanatory videos.

[0032] The receiving unit allows the user to input specific explanatory content and necessary elements. For example, the receiving unit receives input of specific explanatory content such as features, usage methods, and background information of a new product from the user. The receiving unit also receives input of necessary elements such as text, images, and video clips from the user. This allows the user to input specific explanatory content and necessary elements, thereby enabling the creation of more accurate explanatory videos.

[0033] The data generation unit can generate the necessary data and output based on the input information. For example, the data generation unit can analyze text information input by the user and generate the necessary data and output. The data generation unit can also analyze audio information input by the user and generate the necessary data and output. Furthermore, the data generation unit can analyze image information input by the user and generate the necessary data and output. By generating the necessary data and output based on the input information, the content of the explanatory video can be tailored to the user's needs.

[0034] The video generation unit can generate visually effective explanatory videos. The video generation unit generates visually effective explanatory videos by, for example, selecting colors, devising layouts, and using animations. The video generation unit can also appropriately arrange the generated data and output to create visually appealing videos. In this way, generating visually appealing explanatory videos increases the effectiveness of communicating information.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays elements that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest elements that will be used in a specific time period based on the user's past input history. In this way, the optimal input method for the user can be suggested by analyzing the user's past input history.

[0036] When analyzing the input content, the reception unit can perform filtering based on the user's current project or field of interest. For example, the reception unit prioritizes analysis of information related to a project currently underway by the user. The reception unit can also prioritize analysis of highly relevant information based on the user's field of interest. Furthermore, the reception unit can also filter related information based on fields in which the user has previously shown interest. In this way, by filtering based on the user's current project or field of interest, highly relevant information can be prioritized for analysis.

[0037] When analyzing input content, the reception unit can select an appropriate analysis means depending on the user's input method. For example, when the user uses voice input, the reception unit performs analysis using voice recognition technology. When the user uses text input, the reception unit can also perform analysis using natural language processing technology. Furthermore, when the user uses image input, the reception unit can also perform analysis using image recognition technology. This improves the accuracy of analysis of input content by selecting the optimal analysis means depending on the user's input method.

[0038] When analyzing the input content, the reception unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, the reception unit prioritizes analysis of information related to the user's current location. The reception unit can also prioritize analysis of information related to places the user has visited in the past. Furthermore, the reception unit can also prioritize analysis of information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized in analysis by taking into account the user's geographical location information.

[0039] When analyzing the input content, the reception unit can analyze the user's social media activity and acquire related information. The reception unit can acquire related information based on, for example, information shared by the user on social media. The reception unit can also acquire related information by analyzing the content posted by the user on social media. Furthermore, the reception unit can also acquire related information by referring to the activity of the user's friends on social media. In this way, related information can be acquired efficiently by analyzing the user's social media activity.

[0040] When analyzing input content, the reception unit can customize the analysis method by reflecting the user's past feedback. The reception unit adjusts the analysis method based on, for example, feedback provided by the user in the past. The reception unit can also preferentially use a specific analysis method based on the user's past feedback. Furthermore, the reception unit can also reflect the user's feedback to improve the accuracy of the analysis results. In this way, the analysis method can be customized and the analysis accuracy improved by reflecting the user's past feedback.

[0041] The data generation unit can adjust the level of detail of the generated data based on the importance of the input information when generating the data. For example, the data generation unit generates detailed data for important information. The data generation unit can also generate concise data for general information. Furthermore, the data generation unit can adjust the level of detail in response to a user request. In this way, by adjusting the level of detail of the generated data based on the importance of the input information, it is possible to provide necessary information with an appropriate level of detail.

[0042] When generating data, the data generation unit can apply different generation algorithms depending on the category of information. For example, the data generation unit applies a natural language processing algorithm to text information. The data generation unit can also apply an image recognition algorithm to image information. Furthermore, the data generation unit can also apply a voice recognition algorithm to voice information. In this way, by applying different generation algorithms depending on the category of information, it is possible to generate data that is optimal for each category.

[0043] When generating data, the data generation unit can improve the accuracy of generation by referring to the user's past generation results. The data generation unit, for example, adjusts the generation algorithm based on data generated by the user in the past. The data generation unit can also suggest an optimal generation method based on the user's past generation results. Furthermore, the data generation unit can also improve the accuracy of the generation results by reflecting user feedback. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0044] When generating data, the data generation unit can determine the priority of the generated data based on the time of submission of the information. For example, the data generation unit generates data with priority for urgent information. The data generation unit can also generate data with normal priority for periodic information. Furthermore, the data generation unit can adjust the priority based on the time of submission in response to a user request. In this way, by determining the priority of the generated data based on the time of submission of the information, it is possible to generate information with high urgency with priority.

[0045] When generating data, the data generation unit can adjust the order of generated data based on the relevance of the information. For example, the data generation unit generates highly relevant information with priority. The data generation unit can also postpone less relevant information. Furthermore, the data generation unit can also adjust the order based on the relevance in response to a user request. In this way, by adjusting the order of generated data based on the relevance of the information, highly relevant information can be provided with priority.

[0046] When generating data, the data generation unit can adjust the use of technical terms in the generated data according to the user's level of expertise. For example, the data generation unit uses a lot of technical terms for users with high levels of expertise. The data generation unit can also use simpler words for users with low levels of expertise. Furthermore, the data generation unit can adjust the use of technical terms according to the user's request. In this way, by adjusting the use of technical terms according to the user's level of expertise, data that is easy for the user to understand can be provided.

[0047] The video generation unit can improve the accuracy of the video by taking into account the interrelationships of the generated data when generating the video. The video generation unit adjusts the configuration of the video by taking into account the interrelationships of the generated tables and figures, for example. The video generation unit can also adjust the progress of the video by taking into account the content of the generated captions and narration. Furthermore, the video generation unit can adjust the presentation of the video by taking into account the timing of the generated music and sound effects. In this way, the accuracy of the video is improved by taking into account the interrelationships of the generated data.

[0048] When generating a video, the video generation unit can generate the video taking into consideration the attribute information of the submitter of the generated data. The video generation unit generates a video with an appropriate tone and style depending on, for example, the submitter's occupation or position. The video generation unit can also generate a visually appealing video depending on the submitter's age and gender. Furthermore, the video generation unit can also generate an optimal video by referring to the submitter's past submission history. In this way, by taking into consideration the attribute information of the submitter of the generated data, it is possible to generate a video that is optimal for the submitter.

[0049] When generating a video, the video generation unit can weight the video based on the submission frequency of the generated data. For example, the video generation unit generates a detailed video for data that is frequently submitted. The video generation unit can also generate a concise video for data that is submitted only once. Furthermore, the video generation unit can adjust the weighting of the video according to the submission frequency. In this way, by weighting the video based on the submission frequency of the generated data, detailed videos can be generated for data that is frequently submitted.

[0050] When generating a video, the video generation unit can generate the video taking into account the geographic distribution of the generated data. For example, if the generated data is concentrated in a specific region, the video generation unit can generate a video that emphasizes information related to that region. Furthermore, if the generated data is distributed across multiple regions, the video generation unit can also generate a video that incorporates information from each region in a balanced manner. Furthermore, the video generation unit can generate a video that includes visually easy-to-understand maps and graphs based on the geographic distribution of the generated data. In this way, by taking into account the geographic distribution of the generated data, it is possible to generate a video that emphasizes information related to the region.

[0051] When generating a video, the video generation unit can improve the accuracy of the video by referring to literature related to the generated data. For example, the video generation unit refers to academic papers related to the generated data to generate a video containing highly reliable information. The video generation unit can also refer to industry reports related to the generated data to generate a video that reflects the latest trends. Furthermore, the video generation unit can also refer to patent documents related to the generated data to generate a video containing technical details. In this way, by referring to literature related to the generated data, it is possible to generate a video containing highly reliable information.

[0052] When generating a video, the video generation unit can generate the video taking into consideration the market value of the generated data. For example, if the generated data has high market value, the video generation unit generates a video that emphasizes that value. Also, if the generated data is related to a specific market segment, the video generation unit can generate a video that appeals to that segment. Furthermore, the video generation unit can generate a presentation video for investors based on the market value of the generated data. In this way, by taking the market value of the generated data into consideration, a video that emphasizes information with high market value can be generated.

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

[0054] The reception unit can retrieve information from relevant external databases based on the user's input. For example, if a user inputs that they want to create an introductory video for a new product, the reception unit can retrieve market data and competitive information related to the product from external databases and provide it to the data generation unit. Also, if a user inputs that they want to create an explanatory video about a specific technology, the reception unit can retrieve related academic papers and patent information and provide it to the data generation unit. Furthermore, if a user inputs that they want to create a video containing information about a specific region, the reception unit can retrieve statistical data and news articles about that region and provide it to the data generation unit. In this way, the reception unit can retrieve related information from external databases, thereby enriching the content of the explanatory video and improving its reliability.

[0055] The data generation unit can customize the generated data by taking into account the user's past behavior history. For example, it can generate data formats and styles that the user has frequently used in the past with priority. It can also generate data that reflects the colors and fonts that the user has favored in the past. It can also analyze trends in videos created by the user in the past and generate new data based on those trends. In this way, by taking the user's past behavior history into account, the data generation unit can generate data that is easy to use and familiar to the user.

[0056] The video generation unit can generate a video that meets the user's visual preferences based on the generated data. For example, if the user prefers simple and minimalist designs, the video generation unit can generate a video with a simple layout, eliminating unnecessary decorations. If the user prefers colorful and dynamic designs, the video generation unit can generate a video that incorporates a variety of colors and animations. Furthermore, if the user requires a design that follows specific brand guidelines, the video generation unit can generate a video based on those guidelines. In this way, the video generation unit can generate a video that meets the user's visual preferences, thereby providing an attractive video to the user.

[0057] The reception unit can provide relevant trend information based on the user's input. For example, if a user inputs that they want to create a promotional video for a new product, the reception unit can provide the latest market trends and consumer interests related to that product. If a user inputs that they want to create an explanatory video about a specific technology, the reception unit can provide the latest research results and industry trends related to that technology. Furthermore, if a user inputs that they want to create a video containing information about a specific region, the reception unit can provide the latest news and event information related to that region. In this way, the reception unit can provide relevant trend information, making the content of the explanatory video more timely.

[0058] The reception unit can provide relevant legal and regulatory information based on the user's input. For example, if a user inputs that they want to create a promotional video for a new product, the reception unit can provide legal and regulatory information related to that product. Also, if a user inputs that they want to create an explanatory video about a specific technology, the reception unit can provide legal and regulatory information related to that technology. Furthermore, if a user inputs that they want to create a video that includes information about a specific region, the reception unit can provide legal and regulatory information related to that region. In this way, the reception unit's provision of relevant legal and regulatory information ensures that the content of the explanatory video is legally appropriate.

[0059] The reception unit can provide relevant educational resources based on the user's input. For example, if a user inputs that they want to create an introductory video for a new product, the reception unit can provide educational resources related to that product. Also, if a user inputs that they want to create an explanatory video about a specific technology, the reception unit can provide educational resources related to that technology. Furthermore, if a user inputs that they want to create a video containing information about a specific region, the reception unit can provide educational resources related to that region. In this way, the reception unit's provision of relevant educational resources makes the content of the explanatory video more educational.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives input of the video content from the user. For example, the user may input that they want to create a promotional video for a new product. Step 2: The data generation unit analyzes the information received by the reception unit and generates the necessary data and output, such as tables, figures, subtitles, narration, and music. Step 3: The video generation unit aggregates the data and output generated by the data generation unit and generates the final explanatory video. For example, the video generation unit generates a video with narration while displaying the generated diagrams and tables.

[0062] (Example 2) The iPaaS solution according to an embodiment of the present invention is a system that uses video generation AI to easily create explanatory videos for business use. This system allows users to input the content of the video they want, and the video generation AI analyzes that content, generates the necessary data and output, and finally produces the explanatory video. For example, if a user inputs that they want to create a video introducing a new product, the video generation AI analyzes the information and generates diagrams, tables, and narration to explain the product's features. The generated data and output are then aggregated to create a visually appealing explanatory video. This revolutionizes PowerPoint creation and makes it commonplace to create documents such as proposals, business plans, and management meeting materials in video format. This allows the iPaaS solution to easily create compelling explanatory videos without requiring users to perform complex operations. For example, creating management meeting materials in video format enables visually appealing presentations and accelerates decision-making.

[0063] The iPaaS solution according to the embodiment includes a reception unit, a data generation unit, and a video generation unit. The reception unit receives input of video content from a user. For example, the user may input that they would like to create an introductory video for a new product. The data generation unit analyzes the information received by the reception unit and generates the necessary data and output. The data generation unit generates, for example, tables, diagrams, captions, narration, music, etc. The video generation unit aggregates the data and output generated by the data generation unit and generates a final explanatory video. For example, the video generation unit generates a video with narration while displaying the generated diagrams and tables. As a result, the iPaaS solution according to the embodiment can generate the necessary data and output based on the information input by the user and create the final explanatory video.

[0064] The data generation unit can generate tables and figures, subtitles, narration, and music. The data generation unit can generate, for example, bar graphs, pie charts, and tabular data. The data generation unit can also generate subtitles, captions, text overlays, and other subtitles. Furthermore, the data generation unit can generate narration using voice synthesis or recorded voice. The data generation unit can also generate music such as background music and sound effects. As a result, the data generation unit can generate a variety of data and outputs, thereby enriching the content of the explainer video.

[0065] The video generation unit can generate explanatory videos by combining the generated data and output. The video generation unit, for example, places the generated diagrams and tables on a timeline and sets the scene transition method. The video generation unit can also adjust the progress of the video by placing the generated captions and narration in appropriate positions. Furthermore, the video generation unit can adjust the timing of the generated music and sound effects to enhance the presentation of the video. In this way, by effectively combining the generated data and output, it is possible to create visually appealing explanatory videos.

[0066] The receiving unit allows the user to input specific explanatory content and necessary elements. For example, the receiving unit receives input of specific explanatory content such as features, usage methods, and background information of a new product from the user. The receiving unit also receives input of necessary elements such as text, images, and video clips from the user. This allows the user to input specific explanatory content and necessary elements, thereby enabling the creation of more accurate explanatory videos.

[0067] The data generation unit can generate the necessary data and output based on the input information. For example, the data generation unit can analyze text information input by the user and generate the necessary data and output. The data generation unit can also analyze audio information input by the user and generate the necessary data and output. Furthermore, the data generation unit can analyze image information input by the user and generate the necessary data and output. By generating the necessary data and output based on the input information, the content of the explanatory video can be tailored to the user's needs.

[0068] The video generation unit can generate visually effective explanatory videos. The video generation unit generates visually effective explanatory videos by, for example, selecting colors, devising layouts, and using animations. The video generation unit can also appropriately arrange the generated data and output to create visually appealing videos. In this way, generating visually appealing explanatory videos increases the effectiveness of communicating information.

[0069] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize input of important information and postpone input of detailed information. Furthermore, if the user is relaxed, the reception unit can also prioritize input of detailed information to organize the overall structure. Furthermore, if the user is in a hurry, the reception unit can prioritize input of the most important elements and allow additional information to be entered later. Thus, by prioritizing input content based on the user's emotions, optimal input according to the user's situation is possible. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays elements that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest elements that will be used in a specific time period based on the user's past input history. In this way, the optimal input method for the user can be suggested by analyzing the user's past input history.

[0071] When analyzing the input content, the reception unit can perform filtering based on the user's current project or field of interest. For example, the reception unit prioritizes analysis of information related to a project currently underway by the user. The reception unit can also prioritize analysis of highly relevant information based on the user's field of interest. Furthermore, the reception unit can also filter related information based on fields in which the user has previously shown interest. In this way, by filtering based on the user's current project or field of interest, highly relevant information can be prioritized for analysis.

[0072] When analyzing input content, the reception unit can select an appropriate analysis means depending on the user's input method. For example, when the user uses voice input, the reception unit performs analysis using voice recognition technology. When the user uses text input, the reception unit can also perform analysis using natural language processing technology. Furthermore, when the user uses image input, the reception unit can also perform analysis using image recognition technology. This improves the accuracy of analysis of input content by selecting the optimal analysis means depending on the user's input method.

[0073] The reception unit can estimate the user's emotions and adjust the display method of the input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple, highly visible display method. If the user is relaxed, the reception unit can also provide a display method that includes detailed information. If the user is in a hurry, the reception unit can also provide a display method that focuses on the main points. This allows the display method of the input content to be adjusted based on the user's emotions, making it easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] When analyzing the input content, the reception unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, the reception unit prioritizes analysis of information related to the user's current location. The reception unit can also prioritize analysis of information related to places the user has visited in the past. Furthermore, the reception unit can also prioritize analysis of information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized in analysis by taking into account the user's geographical location information.

[0075] When analyzing the input content, the reception unit can analyze the user's social media activity and acquire related information. The reception unit can acquire related information based on, for example, information shared by the user on social media. The reception unit can also acquire related information by analyzing the content posted by the user on social media. Furthermore, the reception unit can also acquire related information by referring to the activity of the user's friends on social media. In this way, related information can be acquired efficiently by analyzing the user's social media activity.

[0076] When analyzing input content, the reception unit can customize the analysis method by reflecting the user's past feedback. The reception unit adjusts the analysis method based on, for example, feedback provided by the user in the past. The reception unit can also preferentially use a specific analysis method based on the user's past feedback. Furthermore, the reception unit can also reflect the user's feedback to improve the accuracy of the analysis results. In this way, the analysis method can be customized and the analysis accuracy improved by reflecting the user's past feedback.

[0077] The data generation unit can estimate the user's emotions and determine the priority of data to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the data generation unit can prioritize generating important data. The data generation unit can also generate detailed data if the user is relaxed. Furthermore, if the user is in a hurry, the data generation unit can prioritize generating the most important data and generate additional data later. This enables optimal data generation according to the user's situation by determining the priority of data to be generated based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The data generation unit can adjust the level of detail of the generated data based on the importance of the input information when generating the data. For example, the data generation unit generates detailed data for important information. The data generation unit can also generate concise data for general information. Furthermore, the data generation unit can adjust the level of detail in response to a user request. In this way, by adjusting the level of detail of the generated data based on the importance of the input information, it is possible to provide necessary information with an appropriate level of detail.

[0079] When generating data, the data generation unit can apply different generation algorithms depending on the category of information. For example, the data generation unit applies a natural language processing algorithm to text information. The data generation unit can also apply an image recognition algorithm to image information. Furthermore, the data generation unit can also apply a voice recognition algorithm to voice information. In this way, by applying different generation algorithms depending on the category of information, it is possible to generate data that is optimal for each category.

[0080] When generating data, the data generation unit can improve the accuracy of generation by referring to the user's past generation results. The data generation unit, for example, adjusts the generation algorithm based on data generated by the user in the past. The data generation unit can also suggest an optimal generation method based on the user's past generation results. Furthermore, the data generation unit can also improve the accuracy of the generation results by reflecting user feedback. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0081] The data generation unit can estimate the user's emotions and adjust the display method of the generated data based on the estimated user emotions. For example, if the user is nervous, the data generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the data generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data generation unit can provide a display method that focuses on the main points. By adjusting the display method of the generated data based on the user's emotions, it is possible to provide a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] When generating data, the data generation unit can determine the priority of the generated data based on the time of submission of the information. For example, the data generation unit generates data with priority for urgent information. The data generation unit can also generate data with normal priority for periodic information. Furthermore, the data generation unit can adjust the priority based on the time of submission in response to a user request. In this way, by determining the priority of the generated data based on the time of submission of the information, it is possible to generate information with high urgency with priority.

[0083] When generating data, the data generation unit can adjust the order of generated data based on the relevance of the information. For example, the data generation unit generates highly relevant information with priority. The data generation unit can also postpone less relevant information. Furthermore, the data generation unit can also adjust the order based on the relevance in response to a user request. In this way, by adjusting the order of generated data based on the relevance of the information, highly relevant information can be provided with priority.

[0084] When generating data, the data generation unit can adjust the use of technical terms in the generated data according to the user's level of expertise. For example, the data generation unit uses a lot of technical terms for users with high levels of expertise. The data generation unit can also use simpler words for users with low levels of expertise. Furthermore, the data generation unit can adjust the use of technical terms according to the user's request. In this way, by adjusting the use of technical terms according to the user's level of expertise, data that is easy for the user to understand can be provided.

[0085] The video generation unit can estimate the user's emotions and adjust the video generation method based on the estimated user emotions. For example, if the user is relaxed, the video generation unit generates a video that progresses at a leisurely pace. If the user is in a hurry, the video generation unit can also generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the video generation unit can also generate a video that adds visually stimulating effects. By adjusting the video generation method based on the user's emotions, it is possible to generate an optimal video according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The video generation unit can improve the accuracy of the video by taking into account the interrelationships of the generated data when generating the video. The video generation unit adjusts the configuration of the video by taking into account the interrelationships of the generated tables and figures, for example. The video generation unit can also adjust the progress of the video by taking into account the content of the generated captions and narration. Furthermore, the video generation unit can adjust the presentation of the video by taking into account the timing of the generated music and sound effects. In this way, the accuracy of the video is improved by taking into account the interrelationships of the generated data.

[0087] When generating a video, the video generation unit can generate the video taking into consideration the attribute information of the submitter of the generated data. The video generation unit generates a video with an appropriate tone and style depending on, for example, the submitter's occupation or position. The video generation unit can also generate a visually appealing video depending on the submitter's age and gender. Furthermore, the video generation unit can also generate an optimal video by referring to the submitter's past submission history. In this way, by taking into consideration the attribute information of the submitter of the generated data, it is possible to generate a video that is optimal for the submitter.

[0088] When generating a video, the video generation unit can weight the video based on the submission frequency of the generated data. For example, the video generation unit generates a detailed video for data that is frequently submitted. The video generation unit can also generate a concise video for data that is submitted only once. Furthermore, the video generation unit can adjust the weighting of the video according to the submission frequency. In this way, by weighting the video based on the submission frequency of the generated data, detailed videos can be generated for data that is frequently submitted.

[0089] The video generation unit can estimate the user's emotions and adjust the video display method based on the estimated user emotions. For example, if the user is nervous, the video generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the video generation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the video generation unit can also provide a display method that focuses on the main points. In this way, by adjusting the video display method based on the user's emotions, a display that is easy for the user to view can be achieved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] When generating a video, the video generation unit can generate the video taking into account the geographic distribution of the generated data. For example, if the generated data is concentrated in a specific region, the video generation unit can generate a video that emphasizes information related to that region. Furthermore, if the generated data is distributed across multiple regions, the video generation unit can also generate a video that incorporates information from each region in a balanced manner. Furthermore, the video generation unit can generate a video that includes visually easy-to-understand maps and graphs based on the geographic distribution of the generated data. In this way, by taking into account the geographic distribution of the generated data, it is possible to generate a video that emphasizes information related to the region.

[0091] When generating a video, the video generation unit can improve the accuracy of the video by referring to literature related to the generated data. For example, the video generation unit refers to academic papers related to the generated data to generate a video containing highly reliable information. The video generation unit can also refer to industry reports related to the generated data to generate a video that reflects the latest trends. Furthermore, the video generation unit can also refer to patent documents related to the generated data to generate a video containing technical details. In this way, by referring to literature related to the generated data, it is possible to generate a video containing highly reliable information.

[0092] When generating a video, the video generation unit can generate the video taking into consideration the market value of the generated data. For example, if the generated data has high market value, the video generation unit generates a video that emphasizes that value. Also, if the generated data is related to a specific market segment, the video generation unit can generate a video that appeals to that segment. Furthermore, the video generation unit can generate a presentation video for investors based on the market value of the generated data. In this way, by taking the market value of the generated data into consideration, a video that emphasizes information with high market value can be generated. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, data generation unit, and video generation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows a user to input that they would like to create an introductory video for a new product. For example, the data generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit and generates necessary data and output. For example, the video generation unit is realized by the control unit 46A of the smart device 14, and aggregates the data and output generated by the data generation unit to generate a final explanatory video. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, data generation unit, and video generation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and a user can input that they would like to create an introduction video for a new product. For example, the data generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit and generates necessary data and output. For example, the video generation unit is realized by the control unit 46A of the smart glasses 214, and aggregates the data and output generated by the data generation unit to generate a final explanatory video. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, data generation unit, and video generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and allows a user to input that they would like to create an introductory video for a new product. For example, the data generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit and generates the necessary data and output. For example, the video generation unit is realized by the control unit 46A of the headset-type terminal 314, and aggregates the data and output generated by the data generation unit to generate the final explanatory video. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, data generation unit, and video generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and allows a user to input that they would like to create an introductory video for a new product. For example, the data generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit and generates the necessary data and output. For example, the video generation unit is realized by the control unit 46A of the robot 414, and aggregates the data and output generated by the data generation unit to generate the final explanatory video.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can retrieve information from relevant external databases based on the user's input. For example, if a user inputs that they want to create an introductory video for a new product, the reception unit can retrieve market data and competitive information related to the product from external databases and provide it to the data generation unit. Also, if a user inputs that they want to create an explanatory video about a specific technology, the reception unit can retrieve related academic papers and patent information and provide it to the data generation unit. Furthermore, if a user inputs that they want to create a video containing information about a specific region, the reception unit can retrieve statistical data and news articles about that region and provide it to the data generation unit. In this way, the reception unit can retrieve related information from external databases, thereby enriching the content of the explanatory video and improving its reliability.

[0095] The data generation unit can customize the generated data by taking into account the user's past behavior history. For example, it can generate data formats and styles that the user has frequently used in the past with priority. It can also generate data that reflects the colors and fonts that the user has favored in the past. It can also analyze trends in videos created by the user in the past and generate new data based on those trends. In this way, by taking the user's past behavior history into account, the data generation unit can generate data that is easy to use and familiar to the user.

[0096] The video generation unit can generate a video that meets the user's visual preferences based on the generated data. For example, if the user prefers simple and minimalist designs, the video generation unit can generate a video with a simple layout, eliminating unnecessary decorations. If the user prefers colorful and dynamic designs, the video generation unit can generate a video that incorporates a variety of colors and animations. Furthermore, if the user requires a design that follows specific brand guidelines, the video generation unit can generate a video based on those guidelines. In this way, the video generation unit can generate a video that meets the user's visual preferences, thereby providing an attractive video to the user.

[0097] The reception unit can provide relevant trend information based on the user's input. For example, if a user inputs that they want to create a promotional video for a new product, the reception unit can provide the latest market trends and consumer interests related to that product. If a user inputs that they want to create an explanatory video about a specific technology, the reception unit can provide the latest research results and industry trends related to that technology. Furthermore, if a user inputs that they want to create a video containing information about a specific region, the reception unit can provide the latest news and event information related to that region. In this way, the reception unit can provide relevant trend information, making the content of the explanatory video more timely.

[0098] The data generation unit can estimate the user's emotions from the data to be generated and adjust the tone and style of the data based on the estimated emotions. For example, if the user is feeling stressed, the data generation unit can generate simple and easy-to-understand data. If the user is relaxed, the data generation unit can generate data that is detailed and contains a wealth of information. Furthermore, if the user is excited, the data generation unit can generate visually stimulating data. In this way, the data generation unit can adjust the tone and style of the data based on the user's emotions, thereby providing optimal data for the user.

[0099] The video generation unit can estimate the user's emotions based on the generated data and adjust the video presentation based on the estimated emotions. For example, if the user is nervous, the video generation unit can create a simple and calm presentation. If the user is relaxed, the video generation unit can also create a relaxing atmosphere. Furthermore, if the user is excited, the video generation unit can also create a visually stimulating presentation. In this way, the video generation unit can adjust the video presentation based on the user's emotions, thereby providing the optimal video for the user.

[0100] The reception unit can provide relevant legal and regulatory information based on the user's input. For example, if a user inputs that they want to create a promotional video for a new product, the reception unit can provide legal and regulatory information related to that product. Also, if a user inputs that they want to create an explanatory video about a specific technology, the reception unit can provide legal and regulatory information related to that technology. Furthermore, if a user inputs that they want to create a video that includes information about a specific region, the reception unit can provide legal and regulatory information related to that region. In this way, the reception unit's provision of relevant legal and regulatory information ensures that the content of the explanatory video is legally appropriate.

[0101] The data generation unit can estimate the user's emotions for the data to be generated and determine the priority of the data based on the estimated emotions. For example, if the user is feeling stressed, the data generation unit can prioritize generating important data. Also, if the user is relaxed, the data generation unit can generate detailed data. Furthermore, if the user is in a hurry, the data generation unit can prioritize generating the most important data and generate additional data later. In this way, the data generation unit can prioritize data based on the user's emotions, thereby enabling optimal data generation according to the user's situation.

[0102] The video generation unit can estimate the user's emotions based on the generated data and adjust the video progression speed based on the estimated emotions. For example, if the user is nervous, the video generation unit can generate a video at a slow progression speed. Also, if the user is relaxed, the video generation unit can generate a video at a normal progression speed. Furthermore, if the user is in a hurry, the video generation unit can generate a video at a fast progression speed. In this way, the video generation unit can adjust the video progression speed based on the user's emotions, thereby providing an optimal video for the user.

[0103] The reception unit can provide relevant educational resources based on the user's input. For example, if a user inputs that they want to create an introductory video for a new product, the reception unit can provide educational resources related to that product. Also, if a user inputs that they want to create an explanatory video about a specific technology, the reception unit can provide educational resources related to that technology. Furthermore, if a user inputs that they want to create a video containing information about a specific region, the reception unit can provide educational resources related to that region. In this way, the reception unit's provision of relevant educational resources makes the content of the explanatory video more educational.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives input of the video content from the user. For example, the user may input that they want to create a promotional video for a new product. Step 2: The data generation unit analyzes the information received by the reception unit and generates the necessary data and output, such as tables, figures, subtitles, narration, and music. Step 3: The video generation unit aggregates the data and output generated by the data generation unit and generates the final explanatory video. For example, the video generation unit generates a video with narration while displaying the generated diagrams and tables.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, in order to avoid confusion and to 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of video content from a user; a data generation unit that analyzes the information received by the reception unit and generates necessary data and output; a video generation unit that aggregates the data and output generated by the data generation unit and generates a final explanatory video; A system characterized by:

2. The data generation unit Generate tables and figures, captions, narration, and music 2. The system of claim 1.

3. The video generation unit Combine the generated data and output to generate an explanatory video 2. The system of claim 1.

4. The reception unit The user enters specific details and required elements.

2. The system of claim 1.

5. The data generation unit Generate required data and output based on input information 2. The system of claim 1.

6. The video generation unit Generate visually effective explainer videos 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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