System

Abstract

An object of a system according to an embodiment is to generate a moving image based on character information so that a user can understand the moving image more quickly.SOLUTION: A system according to an embodiment includes a character information analysis unit, a moving image scenario generation unit, and a moving image generation unit. The character information analysis unit analyzes the character information. The animation scenario generation part generates an animation scenario on the basis of the character information analyzed by the character information analysis part. The moving image generation unit generates a moving image based on the moving image scenario generated by the moving image scenario generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that text-based instructions are difficult for users to understand.

[0005] The system according to the embodiment aims to generate video based on text information, allowing users to understand it more quickly. [Means for solving the problem]

[0006] The system according to the embodiment includes a character information analysis unit, a video scenario generation unit, and a video generation unit. The character information analysis unit analyzes character information. The video scenario generation unit generates a video scenario based on the character information analyzed by the character information analysis unit. The video generation unit generates a video based on the video scenario generated by the video scenario generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate video based on text information, allowing users to understand it more quickly. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 video generation system according to an embodiment of the present invention is a system in which a generation AI creates videos based on text information, allowing users to understand text-based instructions more quickly and intuitively.

[0029] The video generation system according to the embodiment includes a text information analysis unit, a video scenario generation unit, and a video generation unit. The text information analysis unit analyzes text information. For example, the text information analysis unit analyzes text data using natural language processing technology. The text information analysis unit can also analyze document files using text mining technology. The text information analysis unit can also analyze emails using keyword extraction technology. The video scenario generation unit generates a video scenario based on the text information analyzed by the text information analysis unit. For example, the video scenario generation unit generates a storyboard. The video scenario generation unit can also generate a scenario script. The video scenario generation unit can also generate a scene list. The video generation unit generates a video based on the video scenario generated by the video scenario generation unit. For example, the video generation unit generates a video by combining text, audio, images, and animation. The video generation unit can also use animation to show actual button positions and operation methods. The video generation unit can also display screen captures and operation procedures. As a result, the video generation system according to the embodiment generates a video based on text information, and can help the user understand the content more quickly and intuitively.

[0030] The text information analysis unit can perform individually customized analysis by referring to the user's past operation history and usage status. For example, the generation AI in the text information analysis unit refers to the user's past operation history and analyzes text information based on specific operation patterns and frequently used functions. For example, it prioritizes analysis of explanations related to functions that the user has frequently used in the past. The text information analysis unit can also perform individually customized analysis by referring to the user's usage status. For example, it can focus analysis on explanations related to functions that the user uses during a specific time period. The text information analysis unit can also perform analysis by combining the user's operation history and usage status. For example, it can analyze explanations related to functions that the user frequently uses after performing a specific operation. This enables more accurate analysis by customizing the analysis based on the user's past operation history and usage status.

[0031] The text information analysis unit can improve the accuracy of analysis by referring to the manuals of similar products or services and extracting similarities and differences. For example, the generation AI in the text information analysis unit refers to the manuals of similar products and extracts common operating procedures and points of caution. For example, it compares the manuals of multiple home appliances and performs an analysis based on the common operating procedures. The text information analysis unit can also refer to the manuals of similar services and extract similarities and differences. For example, it compares the manuals of multiple online services and performs an analysis based on common functions and points of caution. The text information analysis unit can also improve the accuracy of analysis by referring to the manuals of products or services and extracting similarities and differences. For example, it compares the operating procedures of a product and the usage procedures of a service and performs an analysis based on the common parts. In this way, by referring to the manuals of similar products or services, the accuracy of analysis is improved.

[0032] The text information analysis unit can also accept voice input and perform analysis by having the user verbally explain the contents of the instructions. For example, the text information analysis unit performs analysis by having the generation AI accept voice input and the user verbally explain the contents of the instructions. For example, the user verbally explains the operating procedures, and the analysis is performed based on that content. The text information analysis unit can also use voice recognition technology to convert voice input into text data and perform analysis. For example, voice recognition software automatically analyzes the voice and saves it as text. The text information analysis unit can also build a system that accepts voice input and performs analysis in real time. For example, text is displayed on a screen simultaneously with the voice input. The text information analysis unit can also perform analysis by having the user verbally explain the contents of the instructions using voice input. For example, the user verbally explains the contents of the instructions, and the analysis is performed based on that content. In this way, by accepting voice input, the user can verbally explain the contents of the instructions and perform analysis.

[0033] The text information analysis unit can simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages. For example, the text information analysis unit can simultaneously analyze instructions translated into other languages ​​by a generation AI and provide analysis results in multiple languages. For example, it can simultaneously analyze instructions in English, Japanese, and French. The text information analysis unit can also use machine translation technology to translate instructions into other languages ​​and perform analysis. For example, translation software can automatically translate and analyze instructions. The text information analysis unit can also use human translation to translate instructions into other languages ​​and perform analysis. For example, a translator can translate the instructions and perform analysis based on the content. The text information analysis unit can also analyze translated instructions and provide analysis results in multiple languages. For example, it can provide analysis results in multiple languages. This makes it possible to simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages.

[0034] The video scenario generation unit can generate a scenario that matches the user's learning style. For example, the video scenario generation unit uses a generation AI to generate a scenario that matches the user's learning style. For example, a scenario that makes extensive use of diagrams and illustrations is provided to a user with a visual learning style. The video scenario generation unit can also provide a scenario that makes extensive use of audio commentary to a user with an auditory learning style. The video scenario generation unit can also provide a scenario that makes extensive use of hands-on activities to a user with a tactile learning style. For example, it can make extensive use of scenes that show actual operating procedures. The video scenario generation unit can also build a system for generating scenarios that match the user's learning style. For example, the user's learning style can be collected in advance through a questionnaire, and a scenario can be generated based on the results. In this way, a scenario that matches the user's learning style can be generated, thereby aiding understanding.

[0035] The video scenario generation unit can refer to past user feedback and incorporate scenario configurations that were particularly highly rated. For example, the video scenario generation unit uses a generation AI to refer to past user feedback and incorporate scenario configurations that were particularly highly rated. For example, it generates a new scenario based on a scenario that was highly rated by users. The video scenario generation unit can also collect user feedback and generate a scenario based on the results. For example, it can collect feedback through surveys and reviews. The video scenario generation unit can also build a system for incorporating scenario configurations that were highly rated. For example, it generates a scenario based on feedback scores. In this way, it is possible to incorporate scenario configurations that were highly rated by referring to past user feedback.

[0036] The video scenario generation unit can incorporate interactive elements, allowing the scenario to change as the user selects an option. The video scenario generation unit, for example, uses a generation AI to generate a scenario that incorporates interactive elements. For example, the scenario can change as the user selects an option. The video scenario generation unit can also change the scenario according to the user's options. For example, it can determine the next scene based on the option selected by the user. The video scenario generation unit can also build a system that incorporates interactive elements, allowing the scenario to change as the user selects an option. For example, it can generate a scenario and display the next scene in real time as the user selects an option. In this way, by incorporating interactive elements, it is possible to create a scenario that changes as the user selects an option.

[0037] The video scenario generation unit can generate multiple different scenarios and allow the user to select from them. For example, the video scenario generation unit generates multiple different scenarios using a generation AI and allows the user to select from them. For example, multiple scenarios are presented and the user selects one. The video scenario generation unit can also change the scenario according to the user's selection. For example, the next scene is determined based on the scenario selected by the user. The video scenario generation unit can also build a system that generates multiple different scenarios and allows the user to select from them. For example, a scenario is generated in real time as the user selects an option and the next scene is displayed. In this way, multiple different scenarios can be generated and the user can select from them, thereby providing scenarios that suit the user's preferences.

[0038] The video generation unit can use a 3D model of an actual product to allow the user to rotate the product 360 degrees to check it. For example, the video generation unit uses a generation AI to use a 3D model of an actual product to allow the user to rotate the product 360 degrees to check it. For example, a 3D model of a home appliance can be used to allow the user to check each part of the product in detail. The video generation unit can also create a 3D model of the product using 3D modeling software and incorporate it into the video. For example, the product's exterior and internal structure can be shown in a 3D model. The video generation unit can also use an interactive viewer to allow the user to rotate the product 360 degrees to check it. For example, the user can rotate the product using a mouse or touch operation. This allows the user to rotate the product 360 degrees to check it by using a 3D model of the actual product.

[0039] The video generation unit can refer to the user's viewing history and incorporate the style and format of videos viewed in the past. For example, the video generation unit uses a generation AI to refer to the user's viewing history and incorporate the style and format of videos viewed in the past. For example, it generates a new video based on the style of videos that the user likes to watch. The video generation unit can also customize the content of the video based on the user's viewing history. For example, it generates a new video based on the content of videos that the user has viewed in the past. The video generation unit can also build a system for referencing the viewing history and incorporating the style and format of videos viewed in the past. For example, the viewing history can be stored in a database and a video can be generated based on that data. In this way, it is possible to incorporate the style and format of videos viewed in the past by referring to the user's viewing history.

[0040] The video generation unit can incorporate interactive elements that allow users to input questions within the video and provide answers in real time. For example, the video generation unit can incorporate interactive elements using a generative AI to allow users to input questions within the video. For example, a user can input a question while watching a video and receive an answer in real time. The video generation unit can also change the content of the video in response to a user's question. For example, the next scene can be determined based on the content of the user's question. The video generation unit can also incorporate interactive elements to allow users to input questions within the video and build a system for providing answers in real time. For example, as a user inputs a question, an answer can be generated in real time and the next scene can be displayed. In this way, by incorporating interactive elements that allow users to input questions within the video, answers can be provided in real time.

[0041] The video generation unit can simultaneously display images from different viewpoints, allowing the user to switch viewpoints. For example, the video generation unit uses a generation AI to simultaneously display images from different viewpoints, allowing the user to switch viewpoints. For example, the external appearance and internal structure of a product can be displayed simultaneously. The video generation unit can also simultaneously display images from different viewpoints using a multi-window display. For example, multiple viewpoints can be displayed using picture-in-picture. The video generation unit can also build a system that allows the user to switch viewpoints. For example, the user can switch viewpoints using a mouse or touch operation. This allows the user to simultaneously display images from different viewpoints, allowing the user to switch viewpoints, thereby providing more detailed information.

[0042] The video generation unit can automatically convert the generated video into a format optimized for the user's device, ensuring smooth playback. For example, the video generation unit can automatically convert the video generated by the generation AI into a format optimized for the user's device, ensuring smooth playback. For example, the video can be provided in a format optimized for smartphones and tablets. The video generation unit can also optimize the video using an encoding algorithm. For example, it can adjust the video to suit the device's performance. The video generation unit can also build a system to minimize buffering. For example, it can maintain the frame rate or compress the data. This ensures smooth playback by automatically converting the generated video into a format optimized for the user's device.

[0043] The video generation unit can customize the generated video based on the user's viewing history and feedback, and provide an individually optimized video. For example, the video generation unit customizes a video generated by a generation AI based on the user's viewing history and feedback, and provides an individually optimized video. For example, the video can be adjusted to match the user's preferred viewing style. The video generation unit can also change the content of the video based on user feedback. For example, the video can be improved based on user ratings and comments. The video generation unit can also build a system for customizing videos based on viewing history and feedback. For example, the viewing history can be stored in a database, and videos can be generated based on that data. In this way, the generated video can be customized based on the user's viewing history and feedback, and an individually optimized video can be provided, thereby providing a video that suits the user's preferences.

[0044] The video generation unit can automatically upload the generated videos to different platforms and provide them to a wide range of viewers. For example, the video generation unit can automatically upload videos generated by the generation AI to different platforms and provide them to a wide range of viewers. For example, it can automatically upload to YouTube or Vimeo. The video generation unit can also adjust the timing of uploading. For example, it can upload during times when there are many viewers. The video generation unit can also build a system for automatically uploading to different platforms. For example, it can automatically upload using an API. In this way, the generated videos can be automatically uploaded to different platforms and provided to a wide range of viewers.

[0045] The video generation unit can provide the generated video in a downloadable format so that the user can view it offline. For example, the video generation unit provides the video generated by the generation AI in a downloadable format so that the user can view it offline. For example, the video can be downloaded in MP4 format. The video generation unit can also provide multiple downloadable formats. For example, the video can be provided in AVI format or MKV format. The video generation unit can also build a system to support offline viewing. For example, a download link can be provided so that the user can easily download the video. This improves convenience by providing the generated video in a downloadable format so that the user can view it offline.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The character information analysis unit can perform individually customized analysis by referring to the user's past operation history and usage status. For example, it prioritizes analysis of explanations related to functions that the user has frequently used in the past. The character information analysis unit can also perform individually customized analysis by referring to the user's usage status. For example, it can focus analysis on explanations related to functions that the user uses during a specific time period. The character information analysis unit can also perform analysis by combining the user's operation history and usage status. For example, it can analyze explanations related to functions that the user frequently uses after performing a specific operation. This allows for more accurate analysis by customizing the analysis based on the user's past operation history and usage status.

[0048] The text information analysis unit can improve the accuracy of analysis by referring to the manuals of similar products or services and extracting commonalities and differences. For example, it can compare the manuals of multiple home appliances and perform an analysis based on the common operating procedures. The text information analysis unit can also refer to the manuals of similar services and extract commonalities and differences. For example, it can compare the manuals of multiple online services and perform an analysis based on common functions and points to note. The text information analysis unit can also improve the accuracy of analysis by referring to the manuals of products or services and extracting commonalities and differences. For example, it can compare the operating procedures of a product and the usage procedures of a service and perform an analysis based on the commonalities. In this way, by referring to the manuals of similar products or services, the accuracy of analysis is improved.

[0049] The text information analysis unit can also accept voice input and perform analysis by having the user verbally explain the contents of the instructions. For example, the user explains the operating procedures verbally, and the analysis is performed based on that content. The text information analysis unit can also use voice recognition technology to convert the voice input into text data and perform analysis. For example, voice recognition software automatically analyzes the voice and saves it as text. The text information analysis unit can also build a system that accepts voice input in real time and performs analysis. For example, text is displayed on the screen simultaneously with the voice input. The text information analysis unit can also use voice input to perform analysis by having the user verbally explain the contents of the instructions. For example, the user explains the contents of the instructions verbally, and the analysis is performed based on that content. In this way, by accepting voice input, the user can verbally explain the contents of the instructions and perform analysis.

[0050] The text information analysis unit can simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages. For example, it can simultaneously analyze instructions in English, Japanese, and French. The text information analysis unit can also use machine translation technology to translate instructions into other languages ​​and perform analysis. For example, translation software can automatically translate the instructions and perform analysis. The text information analysis unit can also use human translation to translate the instructions into other languages ​​and perform analysis. For example, a translator can translate the instructions and perform analysis based on the content. The text information analysis unit can also analyze translated instructions and provide analysis results in multiple languages. For example, it can provide analysis results in multiple languages. This allows it to simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages.

[0051] The video scenario generation unit can generate a scenario that matches the user's learning style. For example, it can provide a scenario that makes extensive use of diagrams and illustrations to a user with a visual learning style. The video scenario generation unit can also provide a scenario that makes extensive use of audio commentary to a user with an auditory learning style. The video scenario generation unit can also provide a scenario that makes extensive use of hands-on activities to a user with a tactile learning style. For example, it can make extensive use of scenes that show actual operating procedures. The video scenario generation unit can also build a system for generating a scenario that matches the user's learning style. For example, it can collect information about the user's learning style in advance through a questionnaire and generate a scenario based on the results. In this way, generating a scenario that matches the user's learning style helps understanding.

[0052] The video scenario generation unit can refer to past user feedback and incorporate scenario configurations that were particularly highly rated. For example, a new scenario can be generated based on a scenario that was highly rated by users. The video scenario generation unit can also collect user feedback and generate a scenario based on the results. For example, feedback can be collected through surveys or reviews. The video scenario generation unit can also build a system for incorporating scenario configurations that were highly rated. For example, a scenario can be generated based on feedback scores. In this way, by referring to past user feedback, it is possible to incorporate scenario configurations that were highly rated.

[0053] The video scenario generation unit can incorporate interactive elements to change the scenario as the user selects an option. For example, the scenario can change as the user selects an option. The video scenario generation unit can also change the scenario according to the user's options. For example, the next scene is determined based on the option selected by the user. The video scenario generation unit can also build a system that incorporates interactive elements to change the scenario as the user selects an option. For example, a scenario is generated in real time as the user selects an option, and the next scene is displayed. In this way, by incorporating interactive elements, the scenario can change as the user selects an option.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The character information analysis unit analyzes character information. For example, the character information analysis unit analyzes text data using natural language processing technology. It can also analyze document files using text mining technology. It can also analyze emails using keyword extraction technology. Step 2: The video scenario generation unit generates a video scenario based on the text information analyzed by the text information analysis unit. For example, the video scenario generation unit can generate a storyboard, a scenario script, and a scene list. Step 3: The video generation unit generates a video based on the video scenario generated by the video scenario generation unit. For example, the video generation unit generates a video by combining text, audio, images, and animation. It can also animate the actual button positions and operation methods. It can also display screen captures and operation procedures.

[0056] (Example 2) The video generation system according to an embodiment of the present invention is a system in which a generation AI creates videos based on text information, allowing users to understand text-based instructions more quickly and intuitively.

[0057] The video generation system according to the embodiment includes a text information analysis unit, a video scenario generation unit, and a video generation unit. The text information analysis unit analyzes text information. For example, the text information analysis unit analyzes text data using natural language processing technology. The text information analysis unit can also analyze document files using text mining technology. The text information analysis unit can also analyze emails using keyword extraction technology. The video scenario generation unit generates a video scenario based on the text information analyzed by the text information analysis unit. For example, the video scenario generation unit generates a storyboard. The video scenario generation unit can also generate a scenario script. The video scenario generation unit can also generate a scene list. The video generation unit generates a video based on the video scenario generated by the video scenario generation unit. For example, the video generation unit generates a video by combining text, audio, images, and animation. The video generation unit can also use animation to show actual button positions and operation methods. The video generation unit can also display screen captures and operation procedures. As a result, the video generation system according to the embodiment generates a video based on text information, and can help the user understand the content more quickly and intuitively.

[0058] The text information analysis unit can perform individually customized analysis by referring to the user's past operation history and usage status. For example, the generation AI in the text information analysis unit refers to the user's past operation history and analyzes text information based on specific operation patterns and frequently used functions. For example, it prioritizes analysis of explanations related to functions that the user has frequently used in the past. The text information analysis unit can also perform individually customized analysis by referring to the user's usage status. For example, it can focus analysis on explanations related to functions that the user uses during a specific time period. The text information analysis unit can also perform analysis by combining the user's operation history and usage status. For example, it can analyze explanations related to functions that the user frequently uses after performing a specific operation. This enables more accurate analysis by customizing the analysis based on the user's past operation history and usage status.

[0059] The text information analysis unit can improve the accuracy of analysis by referring to the manuals of similar products or services and extracting similarities and differences. For example, the generation AI in the text information analysis unit refers to the manuals of similar products and extracts common operating procedures and points of caution. For example, it compares the manuals of multiple home appliances and performs an analysis based on the common operating procedures. The text information analysis unit can also refer to the manuals of similar services and extract similarities and differences. For example, it compares the manuals of multiple online services and performs an analysis based on common functions and points of caution. The text information analysis unit can also improve the accuracy of analysis by referring to the manuals of products or services and extracting similarities and differences. For example, it compares the operating procedures of a product and the usage procedures of a service and performs an analysis based on the common parts. In this way, by referring to the manuals of similar products or services, the accuracy of analysis is improved.

[0060] The text information analysis unit uses the emotion estimation function to estimate the user's emotions in real time while reading the manual, and can identify and focus on analyzing parts that are difficult to understand. For example, the text information analysis unit uses the emotion estimation function to analyze the user's emotions in real time while reading the manual, and can identify parts that are difficult to understand. For example, the text information analysis unit can focus on analyzing parts where the user felt confused or irritated. The text information analysis unit can also capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression. The text information analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The text information analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. By estimating the user's emotions in real time, the text information analysis unit can identify and focus on analyzing parts that are difficult to understand.

[0061] The text information analysis unit can also accept voice input and perform analysis by having the user verbally explain the contents of the instructions. For example, the text information analysis unit performs analysis by having the generation AI accept voice input and the user verbally explain the contents of the instructions. For example, the user verbally explains the operating procedures, and the analysis is performed based on that content. The text information analysis unit can also use voice recognition technology to convert voice input into text data and perform analysis. For example, voice recognition software automatically analyzes the voice and saves it as text. The text information analysis unit can also build a system that accepts voice input and performs analysis in real time. For example, text is displayed on a screen simultaneously with the voice input. The text information analysis unit can also perform analysis by having the user verbally explain the contents of the instructions using voice input. For example, the user verbally explains the contents of the instructions, and the analysis is performed based on that content. In this way, by accepting voice input, the user can verbally explain the contents of the instructions and perform analysis.

[0062] The text information analysis unit can simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages. For example, the text information analysis unit can simultaneously analyze instructions translated into other languages ​​by a generation AI and provide analysis results in multiple languages. For example, it can simultaneously analyze instructions in English, Japanese, and French. The text information analysis unit can also use machine translation technology to translate instructions into other languages ​​and perform analysis. For example, translation software can automatically translate and analyze instructions. The text information analysis unit can also use human translation to translate instructions into other languages ​​and perform analysis. For example, a translator can translate the instructions and perform analysis based on the content. The text information analysis unit can also analyze translated instructions and provide analysis results in multiple languages. For example, it can provide analysis results in multiple languages. This makes it possible to simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages.

[0063] The text information analysis unit can use the emotion estimation function to detect in real time the stress or confusion the user feels while reading the manual and focus on analyzing that part. For example, the text information analysis unit can use the emotion estimation function to detect in real time the stress or confusion the user feels while reading the manual and focus on analyzing that part. For example, it can identify the part where the user felt confused and analyze that part in detail. The text information analysis unit can also capture the user's facial expression with a camera and analyze the emotion in real time using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expression to detect stress or confusion. The text information analysis unit can also record the user's voice and estimate the emotion in real time using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score and detect stress or confusion. The text information analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion in real time using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate to detect stress or confusion. This allows the system to detect users' stress and confusion in real time and analyze it in a focused manner to help them understand.

[0064] The video scenario generation unit can generate a scenario that matches the user's learning style. For example, the video scenario generation unit uses a generation AI to generate a scenario that matches the user's learning style. For example, a scenario that makes extensive use of diagrams and illustrations is provided to a user with a visual learning style. The video scenario generation unit can also provide a scenario that makes extensive use of audio commentary to a user with an auditory learning style. The video scenario generation unit can also provide a scenario that makes extensive use of hands-on activities to a user with a tactile learning style. For example, it can make extensive use of scenes that show actual operating procedures. The video scenario generation unit can also build a system for generating scenarios that match the user's learning style. For example, the user's learning style can be collected in advance through a questionnaire, and a scenario can be generated based on the results. In this way, a scenario that matches the user's learning style can be generated, thereby aiding understanding.

[0065] The video scenario generation unit can refer to past user feedback and incorporate scenario configurations that were particularly highly rated. For example, the video scenario generation unit uses a generation AI to refer to past user feedback and incorporate scenario configurations that were particularly highly rated. For example, it generates a new scenario based on a scenario that was highly rated by users. The video scenario generation unit can also collect user feedback and generate a scenario based on the results. For example, it can collect feedback through surveys and reviews. The video scenario generation unit can also build a system for incorporating scenario configurations that were highly rated. For example, it generates a scenario based on feedback scores. In this way, it is possible to incorporate scenario configurations that were highly rated by referring to past user feedback.

[0066] The video scenario generation unit can use the emotion estimation function to generate a scenario that is likely to interest the user and adjust the scenario by predicting the emotional reaction during viewing. The video scenario generation unit, for example, uses the emotion estimation function to generate a scenario that is likely to interest the user. For example, it provides a scenario that incorporates elements that interest the user. The video scenario generation unit can also predict the emotional reaction during viewing and adjust the scenario. For example, it changes the content of the scenario based on the user's emotional reaction. The video scenario generation unit can also use the emotion estimation function to build a system for generating a scenario that is likely to interest the user and predicting the emotional reaction during viewing to adjust the scenario. For example, it can generate a scenario based on the user's past viewing history and feedback, monitor the emotional reaction during viewing in real time, and adjust the scenario. In this way, the viewing experience is improved by generating a scenario that is likely to interest the user and adjusting the scenario by predicting the emotional reaction during viewing.

[0067] The video scenario generation unit can incorporate interactive elements, allowing the scenario to change as the user selects an option. The video scenario generation unit, for example, uses a generation AI to generate a scenario that incorporates interactive elements. For example, the scenario can change as the user selects an option. The video scenario generation unit can also change the scenario according to the user's options. For example, it can determine the next scene based on the option selected by the user. The video scenario generation unit can also build a system that incorporates interactive elements, allowing the scenario to change as the user selects an option. For example, it can generate a scenario and display the next scene in real time as the user selects an option. In this way, by incorporating interactive elements, it is possible to create a scenario that changes as the user selects an option.

[0068] The video scenario generation unit can generate multiple different scenarios and allow the user to select from them. For example, the video scenario generation unit generates multiple different scenarios using a generation AI and allows the user to select from them. For example, multiple scenarios are presented and the user selects one. The video scenario generation unit can also change the scenario according to the user's selection. For example, the next scene is determined based on the scenario selected by the user. The video scenario generation unit can also build a system that generates multiple different scenarios and allows the user to select from them. For example, a scenario is generated in real time as the user selects an option and the next scene is displayed. In this way, multiple different scenarios can be generated and the user can select from them, thereby providing scenarios that suit the user's preferences.

[0069] The video scenario generation unit can use the emotion estimation function to monitor the emotional reactions of a user when watching a scenario in real time and automatically select an optimal scenario. The video scenario generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of a user when watching a scenario in real time and automatically select an optimal scenario. For example, it selects a scenario based on the user's emotional reactions. The video scenario generation unit can also monitor the user's emotional reactions in real time and adjust a scenario. For example, it changes the content of the scenario based on the user's emotional reactions. The video scenario generation unit can also use the emotion estimation function to build a system for monitoring the user's emotional reactions in real time and automatically selecting an optimal scenario. For example, it generates a scenario based on the user's past viewing history and feedback, monitors the emotional reactions during viewing in real time, and adjusts the scenario. In this way, the viewing experience is improved by monitoring the user's emotional reactions in real time and automatically selecting an optimal scenario.

[0070] The video generation unit can use a 3D model of an actual product to allow the user to rotate the product 360 degrees to check it. For example, the video generation unit uses a generation AI to use a 3D model of an actual product to allow the user to rotate the product 360 degrees to check it. For example, a 3D model of a home appliance can be used to allow the user to check each part of the product in detail. The video generation unit can also create a 3D model of the product using 3D modeling software and incorporate it into the video. For example, the product's exterior and internal structure can be shown in a 3D model. The video generation unit can also use an interactive viewer to allow the user to rotate the product 360 degrees to check it. For example, the user can rotate the product using a mouse or touch operation. This allows the user to rotate the product 360 degrees to check it by using a 3D model of the actual product.

[0071] The video generation unit can refer to the user's viewing history and incorporate the style and format of videos viewed in the past. For example, the video generation unit uses a generation AI to refer to the user's viewing history and incorporate the style and format of videos viewed in the past. For example, it generates a new video based on the style of videos that the user likes to watch. The video generation unit can also customize the content of the video based on the user's viewing history. For example, it generates a new video based on the content of videos that the user has viewed in the past. The video generation unit can also build a system for referencing the viewing history and incorporating the style and format of videos viewed in the past. For example, the viewing history can be stored in a database and a video can be generated based on that data. In this way, it is possible to incorporate the style and format of videos viewed in the past by referring to the user's viewing history.

[0072] The video generation unit can use the emotion estimation function to monitor the emotional response of a user when watching a video in real time and adjust the video content so that the user's emotion becomes positive while watching. The video generation unit, for example, uses the emotion estimation function to monitor the emotional response of a user when watching a video in real time and adjust the video content so that the user's emotion becomes positive while watching. For example, the video generation unit changes the content of the video based on the user's emotional response. The video generation unit can also monitor the user's emotional response in real time and adjust the content of the video. For example, the video generation unit can use the emotion estimation function to build a system for monitoring the user's emotional response in real time and adjusting the video content so that the user's emotion becomes positive while watching. For example, the video generation unit generates a video based on the user's past viewing history and feedback, monitors the user's emotional response during viewing in real time, and adjusts the video content so that the user's emotion becomes positive while watching. In this way, the viewing experience is improved by monitoring the user's emotional response in real time and adjusting the video content so that the user's emotion becomes positive while watching.

[0073] The video generation unit can incorporate interactive elements that allow users to input questions within the video and provide answers in real time. For example, the video generation unit can incorporate interactive elements using a generative AI to allow users to input questions within the video. For example, a user can input a question while watching a video and receive an answer in real time. The video generation unit can also change the content of the video in response to a user's question. For example, the next scene can be determined based on the content of the user's question. The video generation unit can also incorporate interactive elements to allow users to input questions within the video and build a system for providing answers in real time. For example, as a user inputs a question, an answer can be generated in real time and the next scene can be displayed. In this way, by incorporating interactive elements that allow users to input questions within the video, answers can be provided in real time.

[0074] The video generation unit can simultaneously display images from different viewpoints, allowing the user to switch viewpoints. For example, the video generation unit uses a generation AI to simultaneously display images from different viewpoints, allowing the user to switch viewpoints. For example, the external appearance and internal structure of a product can be displayed simultaneously. The video generation unit can also simultaneously display images from different viewpoints using a multi-window display. For example, multiple viewpoints can be displayed using picture-in-picture. The video generation unit can also build a system that allows the user to switch viewpoints. For example, the user can switch viewpoints using a mouse or touch operation. This allows the user to simultaneously display images from different viewpoints, allowing the user to switch viewpoints, thereby providing more detailed information.

[0075] The video generation unit can automatically convert the generated video into a format optimized for the user's device, ensuring smooth playback. For example, the video generation unit can automatically convert the video generated by the generation AI into a format optimized for the user's device, ensuring smooth playback. For example, the video can be provided in a format optimized for smartphones and tablets. The video generation unit can also optimize the video using an encoding algorithm. For example, it can adjust the video to suit the device's performance. The video generation unit can also build a system to minimize buffering. For example, it can maintain the frame rate or compress the data. This ensures smooth playback by automatically converting the generated video into a format optimized for the user's device.

[0076] The video generation unit can customize the generated video based on the user's viewing history and feedback, and provide an individually optimized video. For example, the video generation unit customizes a video generated by a generation AI based on the user's viewing history and feedback, and provides an individually optimized video. For example, the video can be adjusted to match the user's preferred viewing style. The video generation unit can also change the content of the video based on user feedback. For example, the video can be improved based on user ratings and comments. The video generation unit can also build a system for customizing videos based on viewing history and feedback. For example, the viewing history can be stored in a database, and videos can be generated based on that data. In this way, the generated video can be customized based on the user's viewing history and feedback, and an individually optimized video can be provided, thereby providing a video that suits the user's preferences.

[0077] The video generation unit can use the emotion estimation function to monitor the emotional response of a user when watching a video in real time and adjust the video content so that the user feels positive after watching. The video generation unit, for example, uses the emotion estimation function to monitor the emotional response of a user when watching a video in real time and adjust the video content so that the user feels positive after watching. For example, the video generation unit changes the content of the video based on the user's emotional response. The video generation unit can also monitor the user's emotional response in real time and adjust the content of the video. For example, the video generation unit can change the scenes based on the user's emotional response. The video generation unit can also use the emotion estimation function to build a system for monitoring the user's emotional response in real time and adjusting the video content so that the user feels positive after watching. For example, the video generation unit generates a video based on the user's past viewing history and feedback, monitors the user's emotional response during viewing in real time, and adjusts the video content. In this way, the viewing experience is improved by monitoring the user's emotional response in real time and adjusting the video content so that the user feels positive after watching.

[0078] The video generation unit can automatically upload the generated videos to different platforms and provide them to a wide range of viewers. For example, the video generation unit can automatically upload videos generated by the generation AI to different platforms and provide them to a wide range of viewers. For example, it can automatically upload to YouTube or Vimeo. The video generation unit can also adjust the timing of uploading. For example, it can upload during times when there are many viewers. The video generation unit can also build a system for automatically uploading to different platforms. For example, it can automatically upload using an API. In this way, the generated videos can be automatically uploaded to different platforms and provided to a wide range of viewers.

[0079] The video generation unit can provide the generated video in a downloadable format so that the user can view it offline. For example, the video generation unit provides the video generated by the generation AI in a downloadable format so that the user can view it offline. For example, the video can be downloaded in MP4 format. The video generation unit can also provide multiple downloadable formats. For example, the video can be provided in AVI format or MKV format. The video generation unit can also build a system to support offline viewing. For example, a download link can be provided so that the user can easily download the video. This improves convenience by providing the generated video in a downloadable format so that the user can view it offline.

[0080] The video generation unit can use the emotion estimation function to monitor the emotional response of a user when watching a video in real time and adjust the video content so that the user feels positive after watching. The video generation unit, for example, uses the emotion estimation function to monitor the emotional response of a user when watching a video in real time and adjust the video content so that the user feels positive after watching. For example, the video generation unit changes the content of the video based on the user's emotional response. The video generation unit can also monitor the user's emotional response in real time and adjust the content of the video. For example, the video generation unit can change the scenes based on the user's emotional response. The video generation unit can also use the emotion estimation function to build a system for monitoring the user's emotional response in real time and adjusting the video content so that the user feels positive after watching. For example, the video generation unit generates a video based on the user's past viewing history and feedback, monitors the user's emotional response during viewing in real time, and adjusts the video content. In this way, the viewing experience is improved by monitoring the user's emotional response in real time and adjusting the video content so that the user feels positive after watching.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The character information analysis unit can perform individually customized analysis by referring to the user's past operation history and usage status. For example, it prioritizes analysis of explanations related to functions that the user has frequently used in the past. The character information analysis unit can also perform individually customized analysis by referring to the user's usage status. For example, it can focus analysis on explanations related to functions that the user uses during a specific time period. The character information analysis unit can also perform analysis by combining the user's operation history and usage status. For example, it can analyze explanations related to functions that the user frequently uses after performing a specific operation. This allows for more accurate analysis by customizing the analysis based on the user's past operation history and usage status.

[0083] The text information analysis unit can improve the accuracy of analysis by referring to the manuals of similar products or services and extracting commonalities and differences. For example, it can compare the manuals of multiple home appliances and perform an analysis based on the common operating procedures. The text information analysis unit can also refer to the manuals of similar services and extract commonalities and differences. For example, it can compare the manuals of multiple online services and perform an analysis based on common functions and points to note. The text information analysis unit can also improve the accuracy of analysis by referring to the manuals of products or services and extracting commonalities and differences. For example, it can compare the operating procedures of a product and the usage procedures of a service and perform an analysis based on the commonalities. In this way, by referring to the manuals of similar products or services, the accuracy of analysis is improved.

[0084] The text information analysis unit uses an emotion estimation function to estimate the user's emotions in real time while reading instructions, and can identify and focus on analyzing parts that are difficult to understand. For example, it can focus on analyzing parts that the user feels confused or irritated. The text information analysis unit can also capture the user's facial expressions with a camera and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions. The text information analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The text information analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows the user's emotions to be estimated in real time, and it can identify and focus on analyzing parts that are difficult to understand.

[0085] The text information analysis unit can also accept voice input and perform analysis by having the user verbally explain the contents of the instructions. For example, the user explains the operating procedures verbally, and the analysis is performed based on that content. The text information analysis unit can also use voice recognition technology to convert the voice input into text data and perform analysis. For example, voice recognition software automatically analyzes the voice and saves it as text. The text information analysis unit can also build a system that accepts voice input in real time and performs analysis. For example, text is displayed on the screen simultaneously with the voice input. The text information analysis unit can also use voice input to perform analysis by having the user verbally explain the contents of the instructions. For example, the user explains the contents of the instructions verbally, and the analysis is performed based on that content. In this way, by accepting voice input, the user can verbally explain the contents of the instructions and perform analysis.

[0086] The text information analysis unit can simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages. For example, it can simultaneously analyze instructions in English, Japanese, and French. The text information analysis unit can also use machine translation technology to translate instructions into other languages ​​and perform analysis. For example, translation software can automatically translate the instructions and perform analysis. The text information analysis unit can also use human translation to translate the instructions into other languages ​​and perform analysis. For example, a translator can translate the instructions and perform analysis based on the content. The text information analysis unit can also analyze translated instructions and provide analysis results in multiple languages. For example, it can provide analysis results in multiple languages. This allows it to simultaneously analyze instructions translated into other languages ​​and provide analysis results in multiple languages.

[0087] The video scenario generation unit can generate a scenario that matches the user's learning style. For example, it can provide a scenario that makes extensive use of diagrams and illustrations to a user with a visual learning style. The video scenario generation unit can also provide a scenario that makes extensive use of audio commentary to a user with an auditory learning style. The video scenario generation unit can also provide a scenario that makes extensive use of hands-on activities to a user with a tactile learning style. For example, it can make extensive use of scenes that show actual operating procedures. The video scenario generation unit can also build a system for generating a scenario that matches the user's learning style. For example, it can collect information about the user's learning style in advance through a questionnaire and generate a scenario based on the results. In this way, generating a scenario that matches the user's learning style helps understanding.

[0088] The video scenario generation unit can refer to past user feedback and incorporate scenario configurations that were particularly highly rated. For example, a new scenario can be generated based on a scenario that was highly rated by users. The video scenario generation unit can also collect user feedback and generate a scenario based on the results. For example, feedback can be collected through surveys or reviews. The video scenario generation unit can also build a system for incorporating scenario configurations that were highly rated. For example, a scenario can be generated based on feedback scores. In this way, by referring to past user feedback, it is possible to incorporate scenario configurations that were highly rated.

[0089] The video scenario generation unit can use the emotion estimation function to generate a scenario that is most likely to interest the user and adjust the scenario by predicting the user's emotional reaction while watching. For example, it provides a scenario that incorporates elements that will interest the user. The video scenario generation unit can also predict the user's emotional reaction while watching and adjust the scenario. For example, it changes the content of the scenario based on the user's emotional reaction. The video scenario generation unit can also use the emotion estimation function to build a system for generating a scenario that will interest the user and predicting the user's emotional reaction while watching to adjust the scenario. For example, it can generate a scenario based on the user's past viewing history and feedback, monitor the user's emotional reaction while watching in real time, and adjust the scenario. In this way, the viewing experience is improved by generating a scenario that will interest the user and adjusting the scenario by predicting the user's emotional reaction while watching.

[0090] The video scenario generation unit can incorporate interactive elements to change the scenario as the user selects an option. For example, the scenario can change as the user selects an option. The video scenario generation unit can also change the scenario according to the user's options. For example, the next scene is determined based on the option selected by the user. The video scenario generation unit can also build a system that incorporates interactive elements to change the scenario as the user selects an option. For example, a scenario is generated in real time as the user selects an option, and the next scene is displayed. In this way, by incorporating interactive elements, the scenario can change as the user selects an option.

[0091] The video scenario generation unit can use the emotion estimation function to monitor the emotional reactions of a user when watching a scenario in real time and automatically select an optimal scenario. For example, the scenario can be selected based on the user's emotional reactions. The video scenario generation unit can also monitor the user's emotional reactions in real time and adjust the scenario. For example, the content of the scenario can be changed based on the user's emotional reactions. The video scenario generation unit can also use the emotion estimation function to build a system for monitoring the user's emotional reactions in real time and automatically selecting an optimal scenario. For example, the video scenario generation unit can generate a scenario based on the user's past viewing history and feedback, monitor the emotional reactions during viewing in real time, and adjust the scenario. In this way, the viewing experience can be improved by monitoring the user's emotional reactions in real time and automatically selecting the optimal scenario.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The character information analysis unit analyzes character information. For example, the character information analysis unit analyzes text data using natural language processing technology. It can also analyze document files using text mining technology. It can also analyze emails using keyword extraction technology. Step 2: The video scenario generation unit generates a video scenario based on the text information analyzed by the text information analysis unit. For example, the video scenario generation unit can generate a storyboard, a scenario script, and a scene list. Step 3: The video generation unit generates a video based on the video scenario generated by the video scenario generation unit. For example, the video generation unit generates a video by combining text, audio, images, and animation. It can also animate the actual button positions and operation methods. It can also display screen captures and operation procedures.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 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. The present invention comprises a character information analysis unit that analyzes character information, a motion picture scenario generation unit that generates a motion picture scenario based on the character information analyzed by the character information analysis unit, and a motion picture generation unit that generates a motion picture based on the motion picture scenario generated by the motion picture scenario generation unit. A system characterized by:

2. The character information analysis unit also accepts voice input and performs analysis by having the user verbally explain the contents of the instruction manual.

2. The system of claim 1.

3. The video scenario generation unit generates a scenario that matches the user's learning style.

2. The system of claim 1.

4. The video generator uses the 3D model of the actual product to allow the user to rotate the product 360 degrees and view it.

2. The system of claim 1.

5. The character information analysis unit estimates the user's emotions in real time while reading the manual, and identifies and focuses on analyzing parts that are difficult to understand.

2. The system of claim 1.

Images