system

Abstract

The system according to the embodiment aims to efficiently digitize the contents of reference books and provide them as videos. [Solution] A system according to an embodiment includes an import unit, an analysis unit, a scenario creation unit, a generation unit, and a provision unit. The import unit scans the contents of a reference book and imports them as digital data. The analysis unit analyzes the digital data imported by the import unit and understands the contents. The scenario creation unit creates a video scenario based on the content analyzed by the analysis unit. The generation unit generates a video based on the scenario created by the scenario creation unit. The provision unit provides the video generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently digitize the contents of reference books and provide them as videos.

[0005] The system according to the embodiment aims to efficiently digitize the contents of reference books and provide them as videos. [Means for solving the problem]

[0006] The system according to the embodiment includes an import unit, an analysis unit, a scenario creation unit, a generation unit, and a provision unit. The import unit scans the contents of a reference book and imports them as digital data. The analysis unit analyzes the digital data imported by the import unit and understands the contents. The scenario creation unit creates a video scenario based on the content analyzed by the analysis unit. The generation unit generates a video based on the scenario created by the scenario creation unit. The provision unit provides the video generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently digitize the contents of a reference book and provide it as a video. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An embodiment of the video generation system of the present invention is a system that generates videos based on the contents of a reference book. This video generation system scans the contents of the reference book and imports them as digital data. A generation AI analyzes the digital data and understands the content. The generation AI creates a video scenario based on the analyzed content and generates a video based on the scenario. This video explains the contents of the reference book in a visually easy-to-understand manner, making it easier for learners to understand. For example, the video generation system scans each page of the reference book with a scanner and saves it as text data. This digital data is input into a generation AI. The generation AI then analyzes the input digital data. The generation AI analyzes the text data and understands the contents of the reference book. For example, the generation AI analyzes the text data using natural language processing technology to grasp the content of each chapter or section. The generation AI then creates a video scenario based on the analyzed content. For example, the generation AI constructs a video scenario based on the content of each chapter or section. This scenario includes the main points and important points of each chapter or section. The generation AI then generates a video based on the scenario. For example, the generation AI generates a video using animations and illustrations based on the scenario. These videos explain the contents of the reference book in a visually easy-to-understand way, making it easier for learners to understand. This system generates videos based on the contents of the reference book, allowing learners to study more effectively. For example, it generates videos that explain difficult mathematical concepts using animations, or videos that explain historical events using diagrams. This allows learners to gain a deeper understanding of the contents of the reference book. In this way, the video generation system can make it easier for learners to visually understand the contents of the reference book.

[0029] The video generation system according to the embodiment includes an import unit, an analysis unit, a scenario creation unit, a generation unit, and a provision unit. The import unit scans the contents of a reference book and imports them as digital data. For example, the import unit can scan each page of the reference book with a scanner and save it as text data. The import unit can adjust the scan resolution and import speed. The analysis unit analyzes the digital data imported by the import unit and understands the content. For example, the analysis unit analyzes the text data using natural language processing technology to understand the content of each chapter or section. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. The scenario creation unit creates a video scenario based on the content analyzed by the analysis unit. For example, the scenario creation unit constructs a video scenario based on the content of each chapter or section. This scenario includes the main points and important points of each chapter or section. The generation unit generates a video based on the scenario created by the scenario creation unit. For example, the generation unit generates a video using animations or illustrations based on the scenario. The generation unit can select the software to be used and the format of the video to be generated. The providing unit provides the video generated by the generating unit. For example, the providing unit stores the generated video and provides it to the learner. The providing unit can adjust the platform and timing of providing the video. In this way, the video generation system according to the embodiment can make it easier for the learner to visually understand the contents of the reference book.

[0030] The scenario creation unit includes an editing unit that edits the video scenario based on the content analyzed by the analysis unit. The editing unit edits the video scenario based on the content analyzed by the analysis unit. For example, the editing unit can edit the video scenario based on the content of each chapter or section. The editing unit can select the content to be edited and the editing tools. The editing unit can adjust, for example, the components of the scenario or the length of the scenario. This makes it possible to edit the scenario and generate a more effective video. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the editing unit can instruct the generation AI to edit the scenario, and the generation AI can edit the scenario. This allows the editing unit to edit the scenario efficiently.

[0031] The generation unit includes a storage unit that stores the generated video. The storage unit stores the generated video. For example, the storage unit can store the generated video in a specific format. The storage unit can select the storage format and storage location. The storage unit can store the generated video in high resolution, for example. The storage unit can also compress and store the generated video. By storing the generated video, it becomes possible to reuse it later. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can instruct the generation AI to store the video, and the generation AI can store the video. This allows the storage unit to store the video efficiently.

[0032] The capture unit can scan each page of a reference book with a scanner and save it as text data. For example, the capture unit can scan each page of a reference book with a scanner and save it as text data. For example, a high-resolution scanner can be used for scanning. The capture unit can adjust the scan resolution and scan speed. For example, the capture unit can set the scan resolution to 300 dpi and the scan speed to 10 pages per minute. The capture unit can also convert the scanned data into text data using OCR technology. For example, the capture unit can convert scanned image data into text data using OCR technology and save it. This allows the contents of the reference book to be captured as digital data. Some or all of the above-mentioned processing in the capture unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the capture unit can instruct the generation AI to set scan settings, and the generation AI can set the scan settings. This allows the capture unit to set the scan settings efficiently.

[0033] The analysis unit can analyze the text data using natural language processing technology to understand the content of each chapter or section. The analysis unit can analyze the text data using natural language processing technology to understand the content of each chapter or section. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can, for example, use morphological analysis to divide the text data into words and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the text data and understand the meaning of each sentence. The analysis unit can also use semantic analysis to analyze the meaning of the text data and understand the content of each chapter or section. This allows the content of a reference book to be accurately analyzed using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can instruct the generation AI to analyze the text data, and the generation AI can analyze the text data. This allows the analysis unit to analyze the text data efficiently.

[0034] The generation unit can generate a video using animations and illustrations based on a scenario. The generation unit, for example, generates a video using animations and illustrations based on a scenario. The generation unit can select software to use and the format of the video to be generated. For example, the generation unit can generate an animation based on a scenario using animation software. The generation unit can also generate illustrations based on the scenario using illustration software. The generation unit can also edit the generated animations and illustrations using video editing software to generate a final video. This makes it possible to generate a visually easy-to-understand video by using animations and illustrations. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can instruct the generation AI to generate animations and illustrations, and the generation AI can generate the animations and illustrations. This allows the generation unit to efficiently generate animations and illustrations.

[0035] The capture unit can detect the paper quality and printing condition of a reference book and automatically adjust optimal scan settings. The capture unit, for example, detects the paper quality and printing condition of a reference book and automatically adjusts optimal scan settings. To detect paper quality and printing condition, for example, a sensor that measures paper thickness and print density is used. For example, if the paper is thin, the capture unit can lower the scan resolution to prevent transparency. Furthermore, if the print is dark, the capture unit can adjust the contrast to make it easier to read. Furthermore, if the paper is old and yellowed, the capture unit can perform color correction to scan clearly. This enables optimal scanning by adjusting scan settings according to the paper quality and printing condition. Some or all of the above-described processing in the capture unit may be performed using, or without, a generation AI. For example, the capture unit can instruct the generation AI to detect the paper quality and printing condition, and the generation AI can adjust the scan settings. This allows the capture unit to efficiently adjust scan settings.

[0036] The capture unit can prioritize scanning important pages based on the contents of the reference book during capture. For example, the capture unit prioritizes scanning important pages based on the contents of the reference book during capture. Important pages can be selected using, for example, frequently occurring keywords or chapter titles. For example, the capture unit can prioritize scanning the table of contents page to grasp the overall structure. The capture unit can also prioritize scanning the first page of each chapter to obtain an overview of each chapter. Furthermore, the capture unit can prioritize scanning the last page of the reference book to obtain a summary or conclusion. This prioritizes scanning important pages, enabling efficient data capture. Some or all of the above-described processing in the capture unit may be performed using, or without, a generation AI. For example, the capture unit can instruct the generation AI to select important pages, and the generation AI can determine the scanning priority. This allows the capture unit to efficiently scan important pages.

[0037] The capture unit can detect the physical condition of the reference book during capture and make corrections. The capture unit can, for example, detect the physical condition of the reference book (e.g., tears or stains) during capture and make corrections. For example, image analysis technology is used to detect the physical condition. For example, the capture unit can detect torn pages and make digital corrections. The capture unit can also detect dirty pages and remove the stains. Furthermore, the capture unit can detect bent pages and make corrections to make them flat. This enables accurate data capture by detecting the physical condition and making corrections. Some or all of the above-mentioned processing in the capture unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the capture unit can instruct the generation AI to detect the physical condition, and the generation AI can make the corrections. This allows the capture unit to efficiently correct the physical condition.

[0038] The capture unit can automatically acquire metadata such as the author and publication year of the reference book during capture. The capture unit automatically acquires metadata such as the author and publication year of the reference book during capture, for example. For example, OCR technology or a barcode scanner is used to acquire the metadata. For example, the capture unit can scan the cover of the reference book to acquire the author's name. The capture unit can also scan the colophon of the reference book to acquire the publication year. Furthermore, the capture unit can scan the ISBN code of the reference book to acquire book information. This automatic acquisition of metadata facilitates data management. Some or all of the above-described processing in the capture unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the capture unit can instruct the generation AI to acquire metadata, and the generation AI can acquire the metadata. This allows the capture unit to efficiently acquire metadata.

[0039] The analysis unit can analyze the contents of a reference book in multiple languages ​​during analysis and provide a translation function. The analysis unit can, for example, analyze the contents of a reference book in multiple languages ​​during analysis and provide a translation function. For example, machine translation technology is used to analyze multiple languages. For example, the analysis unit can translate an English reference book into Japanese and analyze it. The analysis unit can also translate a Japanese reference book into English and analyze it. Furthermore, the analysis unit can translate a French reference book into English and analyze it. This allows analysis in multiple languages, making it possible for the system to be used internationally. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can instruct the generation AI to translate, and the generation AI can perform the translation. This allows the analysis unit to perform translation efficiently.

[0040] During analysis, the analysis unit can compare the contents of a reference book with other reference books and extract differences and similarities. For example, during analysis, the analysis unit can compare the contents of a reference book with other reference books and extract differences and similarities. To extract differences and similarities, for example, text mining technology or comparison algorithms are used. For example, the analysis unit can compare a reference book with other reference books on the same topic and extract differences. The analysis unit can also compare a reference book with other reference books by the same author and extract similarities. Furthermore, the analysis unit can compare a reference book with reference books from different eras and extract changes. This allows for a deeper understanding of the content by comparing with other reference books. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can instruct the generation AI to make the comparison, and the generation AI can perform the comparison. This allows the analysis unit to perform the comparison efficiently.

[0041] The analysis unit may analyze the contents of the reference book as audio data and use audio recognition technology during analysis. For example, the analysis unit may analyze the contents of the reference book as audio data and use audio recognition technology during analysis. Audio recognition technology includes, for example, audio analysis algorithms and audio data preprocessing methods. For example, the analysis unit may import and analyze the contents of the reference book as audio data. The analysis unit may also convert the audio data into text data using audio recognition technology. Furthermore, the analysis unit may analyze the audio data and extract important points. By analyzing the audio data, audio information can be utilized. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may instruct the generation AI to analyze the audio data, and the generation AI may analyze the audio data. This allows the analysis unit to efficiently analyze the audio data.

[0042] The analysis unit may analyze the contents of the reference book as image data and use image recognition technology during analysis. For example, the analysis unit may analyze the contents of the reference book as image data and use image recognition technology during analysis. Image recognition technology includes, for example, image analysis algorithms and image data preprocessing methods. For example, the analysis unit may import and analyze charts and figures in the reference book as image data. The analysis unit may also use image recognition technology to convert the image data into text data. Furthermore, the analysis unit may analyze the image data and extract important points. This enables the use of visual information by analyzing the image data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may instruct the generation AI to analyze the image data, and the generation AI may analyze the image data. This allows the analysis unit to efficiently analyze the image data.

[0043] The scenario creation unit can generate multiple scenario variations based on the contents of a reference book when creating a scenario. For example, the scenario creation unit generates multiple scenario variations based on the contents of a reference book when creating a scenario. The generated scenario variations include, for example, scenarios from different perspectives and scenarios of different lengths. For example, the scenario creation unit can generate a different scenario for each chapter. The scenario creation unit can also generate a different scenario for each section. Furthermore, the scenario creation unit can generate a different scenario for each theme. This allows for flexible video creation by generating multiple scenario variations. Some or all of the above-described processing in the scenario creation unit may be performed using, or without, a generation AI. For example, the scenario creation unit can instruct the generation AI to generate scenario variations, and the generation AI can generate the scenario variations. This allows the scenario creation unit to efficiently generate scenario variations.

[0044] The scenario creation unit can create an interactive scenario based on the contents of a reference book when creating a scenario. For example, the scenario creation unit creates an interactive scenario based on the contents of a reference book when creating a scenario. The creation of an interactive scenario includes, for example, scenario branching and interactive elements according to user choices. For example, the scenario creation unit can create a scenario whose progress changes when the user selects an option. The scenario creation unit can also create a scenario whose progress changes when the user answers questions. Furthermore, the scenario creation unit can create a scenario whose progress changes when the user operates the device. By creating an interactive scenario, it is possible to generate a video that promotes user participation. Some or all of the above-described processing in the scenario creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scenario creation unit can instruct the generation AI to create an interactive scenario, and the generation AI can create the interactive scenario. This allows the scenario creation unit to efficiently create an interactive scenario.

[0045] The scenario creation unit can determine the priority of scenarios based on the contents of a reference book when creating a scenario. For example, the scenario creation unit determines the priority of scenarios based on the contents of a reference book when creating a scenario. The determination of the priority of scenarios includes, for example, determining the importance of content and content with a high learning effect. For example, the scenario creation unit can prioritize creating scenarios for important chapters. The scenario creation unit can also prioritize creating scenarios for important sections. Furthermore, the scenario creation unit can prioritize creating scenarios for important themes. In this way, by determining the priority of scenarios, important content can be animated preferentially. Some or all of the above-described processing in the scenario creation unit may be performed using, or without, a generation AI. For example, the scenario creation unit can instruct the generation AI to determine the priority of scenarios, and the generation AI can determine the priority of scenarios. This allows the scenario creation unit to efficiently determine the priority of scenarios.

[0046] The scenario creation unit can adjust the order of the scenario based on the contents of the reference book when creating a scenario. For example, the scenario creation unit adjusts the order of the scenario based on the contents of the reference book when creating a scenario. Adjusting the order of the scenario includes, for example, an order according to the progress of learning or an order based on the relevance of the content. For example, the scenario creation unit can create a scenario by adjusting the order of chapters. Also, the scenario creation unit can create a scenario by adjusting the order of sections. Furthermore, the scenario creation unit can create a scenario by adjusting the order of themes. In this way, by adjusting the order of the scenario, an effective video can be generated. Some or all of the above-mentioned processing in the scenario creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scenario creation unit can instruct the generation AI to adjust the order of the scenarios, and the generation AI can adjust the order of the scenarios. In this way, the scenario creation unit can efficiently adjust the order of the scenarios.

[0047] The generation unit can generate multiple video variations based on the contents of the reference book during generation. The generation unit, for example, generates multiple video variations based on the contents of the reference book during generation. The generation of video variations includes, for example, videos from different perspectives and videos of different lengths. For example, the generation unit can generate different videos for each chapter. The generation unit can also generate different videos for each section. Furthermore, the generation unit can generate different videos for each theme. This enables flexible video creation by generating multiple video variations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to generate video variations, and the generation AI can generate the video variations. This allows the generation unit to efficiently generate video variations.

[0048] The generation unit can generate an interactive video based on the contents of the reference book at the time of generation. The generation unit, for example, generates an interactive video based on the contents of the reference book at the time of generation. The generation of the interactive video includes, for example, video branching and interactive elements according to user choices. For example, the generation unit can generate a video whose progress changes when the user selects an option. The generation unit can also generate a video whose progress changes when the user answers a question. Furthermore, the generation unit can generate a video whose progress changes when the user operates the device. In this way, by generating an interactive video, it is possible to generate a video that promotes user participation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to generate an interactive video, and the generation AI can generate the interactive video. In this way, the generation unit can efficiently generate an interactive video.

[0049] The generation unit can determine the priority of videos based on the contents of the reference book at the time of generation. The generation unit, for example, determines the priority of videos based on the contents of the reference book at the time of generation. The determination of the priority of videos includes, for example, content of high importance and content with high learning effect. For example, the generation unit can prioritize animation of important chapters. The generation unit can also prioritize animation of important sections. Furthermore, the generation unit can prioritize animation of important themes. In this way, by determining the priority of videos, important content can be prioritized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to determine the priority of videos, and the generation AI can determine the priority of videos. In this way, the generation unit can efficiently determine the priority of videos.

[0050] The generation unit can adjust the order of the videos based on the contents of the reference book during generation. For example, the generation unit adjusts the order of the videos based on the contents of the reference book during generation. Adjusting the order of the videos includes, for example, an order according to the progress of learning or an order based on the relevance of the content. For example, the generation unit can generate videos by adjusting the order of chapters. The generation unit can also generate videos by adjusting the order of sections. Furthermore, the generation unit can generate videos by adjusting the order of themes. In this way, by adjusting the order of the videos, effective videos can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to adjust the order of the videos, and the generation AI can adjust the order of the videos. In this way, the generation unit can efficiently adjust the order of the videos.

[0051] The providing unit can recommend the most appropriate video by referring to the user's learning history when providing the video. For example, the providing unit can recommend the most appropriate video by referring to the user's learning history when providing the video. The reference to the learning history includes, for example, past learning content and learning progress. For example, the providing unit can recommend related videos based on the content the user has learned in the past. The providing unit can also recommend the next video to be studied based on the user's learning progress. Furthermore, the providing unit can extract a topic of interest from the user's learning history and recommend related videos. This enables effective learning by recommending the most appropriate video based on the user's learning history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to refer to the learning history, and the generation AI can refer to the learning history. This allows the providing unit to efficiently refer to the learning history.

[0052] The providing unit can select the optimal video format by taking into account the user's device information when providing the video. For example, the providing unit selects the optimal video format by taking into account the user's device information when providing the video. Consideration of device information includes, for example, the device type and OS version. For example, if the user is using a smartphone, the providing unit can provide a video format optimized for the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a video format optimized for a large screen. Furthermore, if the user is using a personal computer, the providing unit can provide a high-resolution video format. This enables effective video provision by selecting the optimal video format based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to consider the device information, and the generation AI can consider the device information. This allows the providing unit to efficiently consider the device information.

[0053] The providing unit can provide highly relevant videos by taking into account the user's geographical location information when providing the videos. For example, the providing unit can provide highly relevant videos by taking into account the user's geographical location information when providing the videos. Examples of consideration of geographical location information include GPS data and IP addresses. For example, if the user is in a specific area, the providing unit can provide videos related to that area. Furthermore, if the user is traveling, the providing unit can provide videos related to the travel destination. Furthermore, if the user is in a specific facility, the providing unit can provide videos related to the facility. This enables effective learning by providing highly relevant videos based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to take into account the geographical location information, and the generation AI can take into account the geographical location information. This allows the providing unit to efficiently take into account the geographical location information.

[0054] The providing unit can analyze the user's social media activity and provide related videos at the time of providing. The providing unit, for example, analyzes the user's social media activity and provides related videos at the time of providing. Analysis of social media activity includes, for example, the content of posts and the number of likes. For example, the providing unit can provide videos related to topics in which the user has shown interest on social media. The providing unit can also provide related videos based on the content of posts from accounts the user follows. Furthermore, the providing unit can provide related videos based on the activity of groups the user participates in. This enables effective learning by providing related videos based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to analyze the social media activity, and the generation AI can analyze the social media activity. This allows the providing unit to efficiently analyze social media activity.

[0055] The editing department can generate multiple editing variations based on the contents of the reference book during editing. For example, the editing department generates multiple editing variations based on the contents of the reference book during editing. Generating editing variations includes, for example, editing from different perspectives and editing of different lengths. For example, the editing department can generate different editing variations for each chapter. The editing department can also generate different editing variations for each section. Furthermore, the editing department can generate different editing variations for each theme. This allows for flexible video creation by generating multiple editing variations. Some or all of the above-mentioned processing in the editing department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the editing department can instruct the generation AI to generate editing variations, and the generation AI can generate the editing variations. This allows the editing department to efficiently generate editing variations.

[0056] The editing department can adjust the editing order based on the contents of the reference book during editing. For example, the editing department can adjust the editing order based on the contents of the reference book during editing. Adjusting the editing order includes, for example, an order according to the progress of learning or an order based on the relevance of the content. For example, the editing department can adjust the order of chapters during editing. Also, the editing department can adjust the order of sections during editing. Furthermore, the editing department can adjust the order of themes during editing. In this way, by adjusting the editing order, an effective video can be generated. Some or all of the above-mentioned processing in the editing department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the editing department can instruct the generation AI to adjust the editing order, and the generation AI can adjust the editing order. In this way, the editing department can efficiently adjust the editing order.

[0057] The storage unit can select the optimal storage method by taking into account the user's storage status when saving. For example, the storage unit selects the optimal storage method by taking into account the user's storage status when saving. Consideration of the storage status includes, for example, available space and storage type. For example, the storage unit can compress and save videos when the user's storage is low. Furthermore, the storage unit can save videos in high image quality when the user's storage is sufficient. Furthermore, the storage unit can save videos in standard image quality when the user's storage is medium. This enables effective video storage by selecting the optimal storage method according to the user's storage status. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit can instruct the generation AI to consider the storage status, and the generation AI can consider the storage status. This allows the storage unit to efficiently consider the storage status.

[0058] The storage unit can select the optimal storage format by taking into account the user's device information when saving. For example, the storage unit selects the optimal storage format by taking into account the user's device information when saving. Consideration of device information includes, for example, the device type and OS version. For example, if the user is using a smartphone, the storage unit can save in a format optimal for the smartphone. Furthermore, if the user is using a tablet, the storage unit can save in a format optimal for the tablet. Furthermore, if the user is using a PC, the storage unit can save in a format optimal for the PC. This enables effective video saving by selecting the optimal storage format based on the user's device information. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit instructs the generation AI to consider the device information, and the generation AI can consider the device information. This allows the storage unit to efficiently consider the device information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When analyzing the contents of a reference book, the analysis unit can automatically search for other related reference books and materials and reflect them in the analysis results. For example, the analysis unit can search the Internet for other reference books on the same topic and add their contents to the analysis. The analysis unit can also search for reliable materials such as academic papers and specialized books and reflect them in the analysis results. Furthermore, the analysis unit can search for related video and audio data and add their contents to the analysis. This allows the analysis unit to provide more comprehensive and in-depth analysis results.

[0061] The scenario creation unit can create a scenario that suits the user's learning style based on the content analyzed by the analysis unit. For example, the scenario creation unit can create a scenario that makes extensive use of illustrations and animations for users who prefer visual learning. The scenario creation unit can also create a scenario that emphasizes audio commentary for users who prefer auditory learning. Furthermore, the scenario creation unit can create a scenario that incorporates interactive elements for users who prefer practical learning. This allows the scenario creation unit to provide an effective scenario that suits the user's learning style.

[0062] The generation unit can regenerate a generated video by reflecting user feedback. For example, if a user leaves a comment on a specific part of a video, the generation unit can modify the video based on the comment. Furthermore, if a user adjusts the playback speed of the video, the generation unit can regenerate the video to match the speed. Furthermore, if a user asks a question about the content of the video, the generation unit can regenerate a video including an answer to the question. This enables the generation unit to flexibly generate videos that reflect user feedback.

[0063] When providing the generated video, the providing unit can recommend the next video to be viewed based on the user's learning history and progress. For example, the providing unit can analyze the content of videos the user has previously viewed and recommend related videos. The providing unit can also grasp the user's learning progress and recommend videos containing content that should be studied next. Furthermore, the providing unit can recommend videos that will help the user achieve their learning goals based on the user's learning goals. This allows the providing unit to effectively support the user's learning.

[0064] When providing the video, the providing unit can recommend the most suitable video by referring to the user's learning history. For example, the providing unit can recommend related videos based on the content the user has studied in the past. The providing unit can also recommend the next video to study based on the user's learning progress. Furthermore, the providing unit can extract themes of interest from the user's learning history and recommend related videos. This enables effective learning by recommending the most suitable video based on the user's learning history.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The capture unit scans the contents of the reference book and captures them as digital data. For example, the capture unit can scan each page of the reference book with a scanner and save it as text data. The capture unit can adjust the scanning resolution and capture speed. Step 2: The analysis unit analyzes the digital data captured by the capture unit and understands its content. For example, the analysis unit analyzes the text data using natural language processing technology to understand the content of each chapter or section. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Step 3: The scenario creation unit creates a video scenario based on the content analyzed by the analysis unit. For example, the scenario creation unit constructs a video scenario based on the content of each chapter or section. This scenario includes the main points and important points of each chapter or section. Step 4: The generation unit generates a video based on the scenario created by the scenario creation unit. For example, the generation unit generates a video using animation or illustrations based on the scenario. The generation unit can select the software to be used and the format of the video to be generated. Step 5: The providing unit provides the video generated by the generating unit. For example, the providing unit stores the generated video and provides it to the learner. The providing unit can adjust the platform and timing of providing the video.

[0067] (Example 2) An embodiment of the video generation system of the present invention is a system that generates videos based on the contents of a reference book. This video generation system scans the contents of the reference book and imports them as digital data. A generation AI analyzes the digital data and understands the content. The generation AI creates a video scenario based on the analyzed content and generates a video based on the scenario. This video explains the contents of the reference book in a visually easy-to-understand manner, making it easier for learners to understand. For example, the video generation system scans each page of the reference book with a scanner and saves it as text data. This digital data is input into a generation AI. The generation AI then analyzes the input digital data. The generation AI analyzes the text data and understands the contents of the reference book. For example, the generation AI analyzes the text data using natural language processing technology to grasp the content of each chapter or section. The generation AI then creates a video scenario based on the analyzed content. For example, the generation AI constructs a video scenario based on the content of each chapter or section. This scenario includes the main points and important points of each chapter or section. The generation AI then generates a video based on the scenario. For example, the generation AI generates a video using animations and illustrations based on the scenario. These videos explain the contents of the reference book in a visually easy-to-understand way, making it easier for learners to understand. This system generates videos based on the contents of the reference book, allowing learners to study more effectively. For example, it generates videos that explain difficult mathematical concepts using animations, or videos that explain historical events using diagrams. This allows learners to gain a deeper understanding of the contents of the reference book. In this way, the video generation system can make it easier for learners to visually understand the contents of the reference book.

[0068] The video generation system according to the embodiment includes an import unit, an analysis unit, a scenario creation unit, a generation unit, and a provision unit. The import unit scans the contents of a reference book and imports them as digital data. For example, the import unit can scan each page of the reference book with a scanner and save it as text data. The import unit can adjust the scan resolution and import speed. The analysis unit analyzes the digital data imported by the import unit and understands the content. For example, the analysis unit analyzes the text data using natural language processing technology to understand the content of each chapter or section. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, and the like. The scenario creation unit creates a video scenario based on the content analyzed by the analysis unit. For example, the scenario creation unit constructs a video scenario based on the content of each chapter or section. This scenario includes the main points and important points of each chapter or section. The generation unit generates a video based on the scenario created by the scenario creation unit. For example, the generation unit generates a video using animations or illustrations based on the scenario. The generation unit can select the software to be used and the format of the video to be generated. The providing unit provides the video generated by the generating unit. For example, the providing unit stores the generated video and provides it to the learner. The providing unit can adjust the platform and timing of providing the video. In this way, the video generation system according to the embodiment can make it easier for the learner to visually understand the contents of the reference book.

[0069] The scenario creation unit includes an editing unit that edits the video scenario based on the content analyzed by the analysis unit. The editing unit edits the video scenario based on the content analyzed by the analysis unit. For example, the editing unit can edit the video scenario based on the content of each chapter or section. The editing unit can select the content to be edited and the editing tools. The editing unit can adjust, for example, the components of the scenario or the length of the scenario. This makes it possible to edit the scenario and generate a more effective video. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the editing unit can instruct the generation AI to edit the scenario, and the generation AI can edit the scenario. This allows the editing unit to edit the scenario efficiently.

[0070] The generation unit includes a storage unit that stores the generated video. The storage unit stores the generated video. For example, the storage unit can store the generated video in a specific format. The storage unit can select the storage format and storage location. The storage unit can store the generated video in high resolution, for example. The storage unit can also compress and store the generated video. By storing the generated video, it becomes possible to reuse it later. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit can instruct the generation AI to store the video, and the generation AI can store the video. This allows the storage unit to store the video efficiently.

[0071] The capture unit can scan each page of a reference book with a scanner and save it as text data. For example, the capture unit can scan each page of a reference book with a scanner and save it as text data. For example, a high-resolution scanner can be used for scanning. The capture unit can adjust the scan resolution and scan speed. For example, the capture unit can set the scan resolution to 300 dpi and the scan speed to 10 pages per minute. The capture unit can also convert the scanned data into text data using OCR technology. For example, the capture unit can convert scanned image data into text data using OCR technology and save it. This allows the contents of the reference book to be captured as digital data. Some or all of the above-mentioned processing in the capture unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the capture unit can instruct the generation AI to set scan settings, and the generation AI can set the scan settings. This allows the capture unit to set the scan settings efficiently.

[0072] The analysis unit can analyze the text data using natural language processing technology to understand the content of each chapter or section. The analysis unit can analyze the text data using natural language processing technology to understand the content of each chapter or section. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can, for example, use morphological analysis to divide the text data into words and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the text data and understand the meaning of each sentence. The analysis unit can also use semantic analysis to analyze the meaning of the text data and understand the content of each chapter or section. This allows the content of a reference book to be accurately analyzed using natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can instruct the generation AI to analyze the text data, and the generation AI can analyze the text data. This allows the analysis unit to analyze the text data efficiently.

[0073] The generation unit can generate a video using animations and illustrations based on a scenario. The generation unit, for example, generates a video using animations and illustrations based on a scenario. The generation unit can select software to use and the format of the video to be generated. For example, the generation unit can generate an animation based on a scenario using animation software. The generation unit can also generate illustrations based on the scenario using illustration software. The generation unit can also edit the generated animations and illustrations using video editing software to generate a final video. This makes it possible to generate a visually easy-to-understand video by using animations and illustrations. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can instruct the generation AI to generate animations and illustrations, and the generation AI can generate the animations and illustrations. This allows the generation unit to efficiently generate animations and illustrations.

[0074] The capture unit can estimate the user's emotion and adjust the timing of scanning based on the estimated user emotion. The capture unit, for example, estimates the user's emotion and adjusts the timing of scanning based on the estimated user emotion. Emotion estimation can use, for example, facial expression recognition technology or voice analysis technology. For example, when the user is concentrating, the capture unit can speed up the timing of scanning to capture data efficiently. Furthermore, when the user is tired, the capture unit can slow down the timing of scanning to allow for a break. Furthermore, when the user is relaxed, the capture unit can maintain a constant timing of scanning to capture data stably. This enables efficient capture by adjusting the timing of scanning according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the capture unit can be performed using, for example, the generation AI, or without the generation AI. For example, the capture unit can instruct the generation AI to estimate emotions, and the generation AI can then estimate the emotions. This allows the capture unit to efficiently estimate emotions.

[0075] The capture unit can detect the paper quality and printing condition of a reference book and automatically adjust optimal scan settings. The capture unit, for example, detects the paper quality and printing condition of a reference book and automatically adjusts optimal scan settings. To detect paper quality and printing condition, for example, a sensor that measures paper thickness and print density is used. For example, if the paper is thin, the capture unit can lower the scan resolution to prevent transparency. Furthermore, if the print is dark, the capture unit can adjust the contrast to make it easier to read. Furthermore, if the paper is old and yellowed, the capture unit can perform color correction to scan clearly. This enables optimal scanning by adjusting scan settings according to the paper quality and printing condition. Some or all of the above-described processing in the capture unit may be performed using, or without, a generation AI. For example, the capture unit can instruct the generation AI to detect the paper quality and printing condition, and the generation AI can adjust the scan settings. This allows the capture unit to efficiently adjust scan settings.

[0076] The capture unit can prioritize scanning important pages based on the contents of the reference book during capture. For example, the capture unit prioritizes scanning important pages based on the contents of the reference book during capture. Important pages can be selected using, for example, frequently occurring keywords or chapter titles. For example, the capture unit can prioritize scanning the table of contents page to grasp the overall structure. The capture unit can also prioritize scanning the first page of each chapter to obtain an overview of each chapter. Furthermore, the capture unit can prioritize scanning the last page of the reference book to obtain a summary or conclusion. This prioritizes scanning important pages, enabling efficient data capture. Some or all of the above-described processing in the capture unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the capture unit can instruct the generation AI to select important pages, and the generation AI can determine the scanning priority. This allows the capture unit to efficiently scan important pages.

[0077] The capture unit can estimate the user's emotions and determine the priority of pages to scan based on the estimated user emotions. The capture unit, for example, estimates the user's emotions and determines the priority of pages to scan based on the estimated user emotions. Emotion estimation can use, for example, facial expression recognition technology or voice analysis technology. For example, if the user is excited, the capture unit can prioritize scanning chapters that interest the user. Furthermore, if the user is tired, the capture unit can prioritize scanning short chapters. Furthermore, if the user is relaxed, the capture unit can scan pages in order. This enables efficient data capture by determining the priority of pages to scan based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the capture unit can be performed using, for example, the generation AI. For example, the capture unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the capture unit to efficiently estimate emotions.

[0078] The capture unit can detect the physical condition of the reference book during capture and make corrections. The capture unit can, for example, detect the physical condition of the reference book (e.g., tears or stains) during capture and make corrections. For example, image analysis technology is used to detect the physical condition. For example, the capture unit can detect torn pages and make digital corrections. The capture unit can also detect dirty pages and remove the stains. Furthermore, the capture unit can detect bent pages and make corrections to make them flat. This enables accurate data capture by detecting the physical condition and making corrections. Some or all of the above-mentioned processing in the capture unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the capture unit can instruct the generation AI to detect the physical condition, and the generation AI can make the corrections. This allows the capture unit to efficiently correct the physical condition.

[0079] The capture unit can automatically acquire metadata such as the author and publication year of the reference book during capture. The capture unit automatically acquires metadata such as the author and publication year of the reference book during capture, for example. For example, OCR technology or a barcode scanner is used to acquire the metadata. For example, the capture unit can scan the cover of the reference book to acquire the author's name. The capture unit can also scan the colophon of the reference book to acquire the publication year. Furthermore, the capture unit can scan the ISBN code of the reference book to acquire book information. This automatic acquisition of metadata facilitates data management. Some or all of the above-described processing in the capture unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the capture unit can instruct the generation AI to acquire metadata, and the generation AI can acquire the metadata. This allows the capture unit to efficiently acquire metadata.

[0080] The analysis unit can estimate the user's emotion and adjust the depth of analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the depth of analysis based on the estimated user's emotion. For example, facial expression recognition technology or voice analysis technology is used to estimate the emotion. For example, the analysis unit can perform a detailed analysis when the user is concentrating. Furthermore, the analysis unit can perform a simplified analysis when the user is tired. Furthermore, the analysis unit can perform a normal analysis when the user is relaxed. This enables efficient analysis by adjusting the depth of analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can instruct the generation AI to estimate the emotion, and the generation AI can perform the emotion estimation. This allows the analysis unit to efficiently estimate emotions.

[0081] The analysis unit can analyze the contents of a reference book in multiple languages ​​during analysis and provide a translation function. The analysis unit can, for example, analyze the contents of a reference book in multiple languages ​​during analysis and provide a translation function. For example, machine translation technology is used to analyze multiple languages. For example, the analysis unit can translate an English reference book into Japanese and analyze it. The analysis unit can also translate a Japanese reference book into English and analyze it. Furthermore, the analysis unit can translate a French reference book into English and analyze it. This allows analysis in multiple languages, making it possible for the system to be used internationally. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can instruct the generation AI to translate, and the generation AI can perform the translation. This allows the analysis unit to perform translation efficiently.

[0082] During analysis, the analysis unit can compare the contents of a reference book with other reference books and extract differences and similarities. For example, during analysis, the analysis unit can compare the contents of a reference book with other reference books and extract differences and similarities. To extract differences and similarities, for example, text mining technology or comparison algorithms are used. For example, the analysis unit can compare a reference book with other reference books on the same topic and extract differences. The analysis unit can also compare a reference book with other reference books by the same author and extract similarities. Furthermore, the analysis unit can compare a reference book with reference books from different eras and extract changes. This allows for a deeper understanding of the content by comparing with other reference books. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can instruct the generation AI to make the comparison, and the generation AI can perform the comparison. This allows the analysis unit to perform the comparison efficiently.

[0083] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. For example, facial expression recognition technology or voice analysis technology is used to estimate the emotion. For example, the analysis unit can provide a simple display method when the user is nervous. Furthermore, the analysis unit can provide a detailed display method when the user is relaxed. Furthermore, the analysis unit can provide a display method that focuses on the main points when the user is in a hurry. This enables efficient information provision by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can instruct the generation AI to estimate the emotion, and the generation AI can perform the emotion estimation. This allows the analysis unit to efficiently estimate emotions.

[0084] The analysis unit may analyze the contents of the reference book as audio data and use audio recognition technology during analysis. For example, the analysis unit may analyze the contents of the reference book as audio data and use audio recognition technology during analysis. Audio recognition technology includes, for example, audio analysis algorithms and audio data preprocessing methods. For example, the analysis unit may import and analyze the contents of the reference book as audio data. The analysis unit may also convert the audio data into text data using audio recognition technology. Furthermore, the analysis unit may analyze the audio data and extract important points. By analyzing the audio data, audio information can be utilized. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may instruct the generation AI to analyze the audio data, and the generation AI may analyze the audio data. This allows the analysis unit to efficiently analyze the audio data.

[0085] The analysis unit may analyze the contents of the reference book as image data and use image recognition technology during analysis. For example, the analysis unit may analyze the contents of the reference book as image data and use image recognition technology during analysis. Image recognition technology includes, for example, image analysis algorithms and image data preprocessing methods. For example, the analysis unit may import and analyze charts and figures in the reference book as image data. The analysis unit may also use image recognition technology to convert the image data into text data. Furthermore, the analysis unit may analyze the image data and extract important points. This enables the use of visual information by analyzing the image data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may instruct the generation AI to analyze the image data, and the generation AI may analyze the image data. This allows the analysis unit to efficiently analyze the image data.

[0086] The scenario creation unit can estimate the user's emotions and adjust the way the scenario is presented based on the estimated user emotions. The scenario creation unit, for example, estimates the user's emotions and adjusts the way the scenario is presented based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, the scenario creation unit can create a visually stimulating scenario when the user is excited. Furthermore, the scenario creation unit can create a calm scenario when the user is relaxed. Furthermore, the scenario creation unit can create a detailed scenario when the user is focused. This allows for the generation of effective videos by adjusting the way the scenario is presented based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the scenario creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scenario creation unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the scenario creation unit to efficiently estimate emotions.

[0087] The scenario creation unit can generate multiple scenario variations based on the contents of a reference book when creating a scenario. For example, the scenario creation unit generates multiple scenario variations based on the contents of a reference book when creating a scenario. The generated scenario variations include, for example, scenarios from different perspectives and scenarios of different lengths. For example, the scenario creation unit can generate a different scenario for each chapter. The scenario creation unit can also generate a different scenario for each section. Furthermore, the scenario creation unit can generate a different scenario for each theme. This allows for flexible video creation by generating multiple scenario variations. Some or all of the above-described processing in the scenario creation unit may be performed using, or without, a generation AI. For example, the scenario creation unit can instruct the generation AI to generate scenario variations, and the generation AI can generate the scenario variations. This allows the scenario creation unit to efficiently generate scenario variations.

[0088] The scenario creation unit can create an interactive scenario based on the contents of a reference book when creating a scenario. For example, the scenario creation unit creates an interactive scenario based on the contents of a reference book when creating a scenario. The creation of an interactive scenario includes, for example, scenario branching and interactive elements according to user choices. For example, the scenario creation unit can create a scenario whose progress changes when the user selects an option. The scenario creation unit can also create a scenario whose progress changes when the user answers questions. Furthermore, the scenario creation unit can create a scenario whose progress changes when the user operates the device. By creating an interactive scenario, it is possible to generate a video that promotes user participation. Some or all of the above-described processing in the scenario creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scenario creation unit can instruct the generation AI to create an interactive scenario, and the generation AI can create the interactive scenario. This allows the scenario creation unit to efficiently create an interactive scenario.

[0089] The scenario creation unit can estimate the user's emotions and adjust the length of the scenario based on the estimated user emotions. The scenario creation unit, for example, estimates the user's emotions and adjusts the length of the scenario based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, the scenario creation unit can create a short scenario when the user is in a hurry. Furthermore, the scenario creation unit can create a long scenario when the user is relaxed. Furthermore, the scenario creation unit can create a detailed scenario when the user is concentrating. This allows for the creation of effective videos by adjusting the length of the scenario according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the scenario creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the scenario creation unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the scenario creation unit to efficiently estimate emotions.

[0090] The scenario creation unit can determine the priority of scenarios based on the contents of a reference book when creating a scenario. For example, the scenario creation unit determines the priority of scenarios based on the contents of a reference book when creating a scenario. The determination of the priority of scenarios includes, for example, determining the importance of content and content with a high learning effect. For example, the scenario creation unit can prioritize creating scenarios for important chapters. The scenario creation unit can also prioritize creating scenarios for important sections. Furthermore, the scenario creation unit can prioritize creating scenarios for important themes. In this way, by determining the priority of scenarios, important content can be animated preferentially. Some or all of the above-described processing in the scenario creation unit may be performed using, or without, a generation AI. For example, the scenario creation unit can instruct the generation AI to determine the priority of scenarios, and the generation AI can determine the priority of scenarios. This allows the scenario creation unit to efficiently determine the priority of scenarios.

[0091] The scenario creation unit can adjust the order of the scenario based on the contents of the reference book when creating a scenario. For example, the scenario creation unit adjusts the order of the scenario based on the contents of the reference book when creating a scenario. Adjusting the order of the scenario includes, for example, an order according to the progress of learning or an order based on the relevance of the content. For example, the scenario creation unit can create a scenario by adjusting the order of chapters. Also, the scenario creation unit can create a scenario by adjusting the order of sections. Furthermore, the scenario creation unit can create a scenario by adjusting the order of themes. In this way, by adjusting the order of the scenario, an effective video can be generated. Some or all of the above-mentioned processing in the scenario creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the scenario creation unit can instruct the generation AI to adjust the order of the scenarios, and the generation AI can adjust the order of the scenarios. In this way, the scenario creation unit can efficiently adjust the order of the scenarios.

[0092] The generation unit can estimate the user's emotion and adjust the style of the video based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the style of the video based on the estimated user's emotion. The emotion estimation can use, for example, facial expression recognition technology or voice analysis technology. For example, the generation unit can generate a video with a calm style when the user is relaxed. Furthermore, the generation unit can generate a video with a visually stimulating style when the user is excited. Furthermore, the generation unit can generate a video with a detailed style when the user is focused. This allows for the generation of effective videos by adjusting the style of the video according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can instruct the generation AI to estimate the emotion, and the generation AI can perform the emotion estimation. This allows the generation unit to efficiently estimate emotions.

[0093] The generation unit can generate multiple video variations based on the contents of the reference book during generation. The generation unit, for example, generates multiple video variations based on the contents of the reference book during generation. The generation of video variations includes, for example, videos from different perspectives and videos of different lengths. For example, the generation unit can generate different videos for each chapter. The generation unit can also generate different videos for each section. Furthermore, the generation unit can generate different videos for each theme. This enables flexible video creation by generating multiple video variations. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to generate video variations, and the generation AI can generate the video variations. This allows the generation unit to efficiently generate video variations.

[0094] The generation unit can generate an interactive video based on the contents of the reference book at the time of generation. The generation unit, for example, generates an interactive video based on the contents of the reference book at the time of generation. The generation of the interactive video includes, for example, video branching and interactive elements according to user choices. For example, the generation unit can generate a video whose progress changes when the user selects an option. The generation unit can also generate a video whose progress changes when the user answers a question. Furthermore, the generation unit can generate a video whose progress changes when the user operates the device. In this way, by generating an interactive video, it is possible to generate a video that promotes user participation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to generate an interactive video, and the generation AI can generate the interactive video. In this way, the generation unit can efficiently generate an interactive video.

[0095] The generation unit can estimate the user's emotion and adjust the length of the video based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the video based on the estimated user emotion. For example, facial expression recognition technology or voice analysis technology is used to estimate the emotion. For example, the generation unit can generate a short video when the user is in a hurry. Furthermore, the generation unit can generate a long video when the user is relaxed. Furthermore, the generation unit can generate a detailed video when the user is concentrating. This allows for the generation of effective videos by adjusting the length of the video according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can instruct the generation AI to estimate the emotion, and the generation AI can perform the emotion estimation. This allows the generation unit to efficiently estimate the emotion.

[0096] The generation unit can determine the priority of videos based on the contents of the reference book at the time of generation. The generation unit, for example, determines the priority of videos based on the contents of the reference book at the time of generation. The determination of the priority of videos includes, for example, content of high importance and content with high learning effect. For example, the generation unit can prioritize animation of important chapters. The generation unit can also prioritize animation of important sections. Furthermore, the generation unit can prioritize animation of important themes. In this way, by determining the priority of videos, important content can be prioritized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to determine the priority of videos, and the generation AI can determine the priority of videos. In this way, the generation unit can efficiently determine the priority of videos.

[0097] The generation unit can adjust the order of the videos based on the contents of the reference book during generation. For example, the generation unit adjusts the order of the videos based on the contents of the reference book during generation. Adjusting the order of the videos includes, for example, an order according to the progress of learning or an order based on the relevance of the content. For example, the generation unit can generate videos by adjusting the order of chapters. The generation unit can also generate videos by adjusting the order of sections. Furthermore, the generation unit can generate videos by adjusting the order of themes. In this way, by adjusting the order of the videos, effective videos can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can instruct the generation AI to adjust the order of the videos, and the generation AI can adjust the order of the videos. In this way, the generation unit can efficiently adjust the order of the videos.

[0098] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the video presentation method based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, if the user is relaxed, the providing unit can present the video at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can present a shortened version of the video. Furthermore, if the user is concentrating, the providing unit can present a detailed video. This enables effective video presentation by adjusting the video presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the providing unit to efficiently estimate emotions.

[0099] The providing unit can recommend the most appropriate video by referring to the user's learning history when providing the video. For example, the providing unit can recommend the most appropriate video by referring to the user's learning history when providing the video. The reference to the learning history includes, for example, past learning content and learning progress. For example, the providing unit can recommend related videos based on the content the user has learned in the past. The providing unit can also recommend the next video to be studied based on the user's learning progress. Furthermore, the providing unit can extract a topic of interest from the user's learning history and recommend related videos. This enables effective learning by recommending the most appropriate video based on the user's learning history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to refer to the learning history, and the generation AI can refer to the learning history. This allows the providing unit to efficiently refer to the learning history.

[0100] The providing unit can select the optimal video format by taking into account the user's device information when providing the video. For example, the providing unit selects the optimal video format by taking into account the user's device information when providing the video. Consideration of device information includes, for example, the device type and OS version. For example, if the user is using a smartphone, the providing unit can provide a video format optimized for the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a video format optimized for a large screen. Furthermore, if the user is using a personal computer, the providing unit can provide a high-resolution video format. This enables effective video provision by selecting the optimal video format based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to consider the device information, and the generation AI can consider the device information. This allows the providing unit to efficiently consider the device information.

[0101] The providing unit can estimate the user's emotions and adjust the timing of video provision based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the timing of video provision based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, the providing unit can provide videos in between study breaks when the user is relaxed. Furthermore, the providing unit can provide videos that can be viewed in a short time when the user is in a hurry. Furthermore, the providing unit can provide videos continuously when the user is concentrating. This enables effective video provision by adjusting the timing of video provision according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI. For example, the providing unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the providing unit to efficiently estimate emotions.

[0102] The providing unit can provide highly relevant videos by taking into account the user's geographical location information when providing the videos. For example, the providing unit can provide highly relevant videos by taking into account the user's geographical location information when providing the videos. Examples of consideration of geographical location information include GPS data and IP addresses. For example, if the user is in a specific area, the providing unit can provide videos related to that area. Furthermore, if the user is traveling, the providing unit can provide videos related to the travel destination. Furthermore, if the user is in a specific facility, the providing unit can provide videos related to the facility. This enables effective learning by providing highly relevant videos based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to take into account the geographical location information, and the generation AI can take into account the geographical location information. This allows the providing unit to efficiently take into account the geographical location information.

[0103] The providing unit can analyze the user's social media activity and provide related videos at the time of providing. The providing unit, for example, analyzes the user's social media activity and provides related videos at the time of providing. Analysis of social media activity includes, for example, the content of posts and the number of likes. For example, the providing unit can provide videos related to topics in which the user has shown interest on social media. The providing unit can also provide related videos based on the content of posts from accounts the user follows. Furthermore, the providing unit can provide related videos based on the activity of groups the user participates in. This enables effective learning by providing related videos based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can instruct the generation AI to analyze the social media activity, and the generation AI can analyze the social media activity. This allows the providing unit to efficiently analyze social media activity.

[0104] The editing unit can estimate the user's emotions and adjust the editing method of the scenario based on the estimated user emotions. The editing unit, for example, estimates the user's emotions and adjusts the editing method of the scenario based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, the editing unit can perform detailed editing when the user is relaxed. Furthermore, the editing unit can perform simple editing when the user is in a hurry. Furthermore, the editing unit can perform precise editing when the user is concentrating. This allows for the generation of effective videos by adjusting the editing method of the scenario according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the editing unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the editorial department to efficiently estimate emotions.

[0105] The editing department can generate multiple editing variations based on the contents of the reference book during editing. For example, the editing department generates multiple editing variations based on the contents of the reference book during editing. Generating editing variations includes, for example, editing from different perspectives and editing of different lengths. For example, the editing department can generate different editing variations for each chapter. The editing department can also generate different editing variations for each section. Furthermore, the editing department can generate different editing variations for each theme. This allows for flexible video creation by generating multiple editing variations. Some or all of the above-mentioned processing in the editing department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the editing department can instruct the generation AI to generate editing variations, and the generation AI can generate the editing variations. This allows the editing department to efficiently generate editing variations.

[0106] The editing unit can estimate the user's emotions and determine editing priorities based on the estimated user emotions. The editing unit, for example, estimates the user's emotions and determines editing priorities based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, if the user is excited, the editing unit can prioritize editing chapters that the user is interested in. Furthermore, if the user is tired, the editing unit can prioritize editing short chapters. Furthermore, if the user is relaxed, the editing unit can edit in order. This allows for determining editing priorities based on the user's emotions, thereby generating effective videos. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the editing unit may be performed using, for example, the generation AI. For example, the editing unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the editorial department to efficiently estimate emotions.

[0107] The editing department can adjust the editing order based on the contents of the reference book during editing. For example, the editing department can adjust the editing order based on the contents of the reference book during editing. Adjusting the editing order includes, for example, an order according to the progress of learning or an order based on the relevance of the content. For example, the editing department can adjust the order of chapters during editing. Also, the editing department can adjust the order of sections during editing. Furthermore, the editing department can adjust the order of themes during editing. In this way, by adjusting the editing order, an effective video can be generated. Some or all of the above-mentioned processing in the editing department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the editing department can instruct the generation AI to adjust the editing order, and the generation AI can adjust the editing order. In this way, the editing department can efficiently adjust the editing order.

[0108] The storage unit can estimate the user's emotion and adjust the format of the video to be saved based on the estimated user emotion. The storage unit, for example, estimates the user's emotion and adjusts the format of the video to be saved based on the estimated user emotion. Emotion estimation can use, for example, facial expression recognition technology or voice analysis technology. For example, the storage unit can save the video in a high-quality format when the user is relaxed. Furthermore, the storage unit can save the video in a low-quality format when the user is in a hurry. Furthermore, the storage unit can save the video in a standard-quality format when the user is concentrating. This enables effective video saving by adjusting the format of the video to be saved according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or without the generation AI. For example, the storage unit can instruct the generation AI to estimate emotions, and the generation AI can estimate emotions. This allows the storage unit to efficiently estimate emotions.

[0109] The storage unit can select the optimal storage method by taking into account the user's storage status when saving. For example, the storage unit selects the optimal storage method by taking into account the user's storage status when saving. Consideration of the storage status includes, for example, available space and storage type. For example, the storage unit can compress and save videos when the user's storage is low. Furthermore, the storage unit can save videos in high image quality when the user's storage is sufficient. Furthermore, the storage unit can save videos in standard image quality when the user's storage is medium. This enables effective video storage by selecting the optimal storage method according to the user's storage status. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit can instruct the generation AI to consider the storage status, and the generation AI can consider the storage status. This allows the storage unit to efficiently consider the storage status.

[0110] The storage unit can estimate the user's emotions and determine the priority of videos to be saved based on the estimated user emotions. The storage unit, for example, estimates the user's emotions and determines the priority of videos to be saved based on the estimated user emotions. For example, facial expression recognition technology or voice analysis technology is used to estimate emotions. For example, when the user is excited, the storage unit can prioritize saving videos that interest the user. Furthermore, when the user is tired, the storage unit can prioritize saving short videos. Furthermore, when the user is relaxed, the storage unit can save videos in order. This enables effective video saving by determining the priority of videos to be saved according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, the generation AI. For example, the storage unit can instruct the generation AI to estimate emotions, and the generation AI can perform the emotion estimation. This allows the storage unit to efficiently estimate emotions.

[0111] The storage unit can select the optimal storage format by taking into account the user's device information when saving. For example, the storage unit selects the optimal storage format by taking into account the user's device information when saving. Consideration of device information includes, for example, the device type and OS version. For example, if the user is using a smartphone, the storage unit can save in a format optimal for the smartphone. Furthermore, if the user is using a tablet, the storage unit can save in a format optimal for the tablet. Furthermore, if the user is using a PC, the storage unit can save in a format optimal for the PC. This enables effective video saving by selecting the optimal storage format based on the user's device information. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit instructs the generation AI to consider the device information, and the generation AI can consider the device information. This allows the storage unit to efficiently consider the device information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned capture unit, analysis unit, scenario creation unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the capture unit can scan the contents of a reference book using the camera 42 of the smart device 14 and capture the contents as digital data. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the captured digital data and understands the content. The scenario creation unit, realized by the specific processing unit 290 of the data processing device 12, creates a video scenario based on the analyzed content. The generation unit, realized by the specific processing unit 290 of the data processing device 12, generates a video based on the scenario. The provision unit provides the generated video to the learner using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned capture unit, analysis unit, scenario creation unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the capture unit can scan the contents of a reference book using the camera 42 of the smart glasses 214 and capture the contents as digital data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured digital data to understand the contents. The scenario creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a video scenario based on the analyzed content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the scenario. The provision unit provides the generated video to the learner using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, scenario creation unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the capture unit can scan the contents of a reference book using the camera 42 of the headset-type terminal 314 and capture the contents as digital data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured digital data to understand the contents. The scenario creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a video scenario based on the analyzed content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the scenario. The provision unit provides the generated video to the learner using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, scenario creation unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the capture unit can scan the contents of a reference book using the camera 42 of the robot 414 and capture the contents as digital data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured digital data to understand the contents. The scenario creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a video scenario based on the analyzed content. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a video based on the scenario. The provision unit provides the generated video to the learner using the speaker 240 of the robot 414.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] When analyzing the contents of a reference book, the analysis unit can automatically search for other related reference books and materials and reflect them in the analysis results. For example, the analysis unit can search the Internet for other reference books on the same topic and add their contents to the analysis. The analysis unit can also search for reliable materials such as academic papers and specialized books and reflect them in the analysis results. Furthermore, the analysis unit can search for related video and audio data and add their contents to the analysis. This allows the analysis unit to provide more comprehensive and in-depth analysis results.

[0114] The scenario creation unit can create a scenario that suits the user's learning style based on the content analyzed by the analysis unit. For example, the scenario creation unit can create a scenario that makes extensive use of illustrations and animations for users who prefer visual learning. The scenario creation unit can also create a scenario that emphasizes audio commentary for users who prefer auditory learning. Furthermore, the scenario creation unit can create a scenario that incorporates interactive elements for users who prefer practical learning. This allows the scenario creation unit to provide an effective scenario that suits the user's learning style.

[0115] The generation unit can regenerate a generated video by reflecting user feedback. For example, if a user leaves a comment on a specific part of a video, the generation unit can modify the video based on the comment. Furthermore, if a user adjusts the playback speed of the video, the generation unit can regenerate the video to match the speed. Furthermore, if a user asks a question about the content of the video, the generation unit can regenerate a video including an answer to the question. This enables the generation unit to flexibly generate videos that reflect user feedback.

[0116] When providing the generated video, the providing unit can recommend the next video to be viewed based on the user's learning history and progress. For example, the providing unit can analyze the content of videos the user has previously viewed and recommend related videos. The providing unit can also grasp the user's learning progress and recommend videos containing content that should be studied next. Furthermore, the providing unit can recommend videos that will help the user achieve their learning goals based on the user's learning goals. This allows the providing unit to effectively support the user's learning.

[0117] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple display method. If the user is relaxed, the analysis unit can provide a detailed display method. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to provide information efficiently by adjusting the display method according to the user's emotions.

[0118] The scenario creation unit can estimate the user's emotions and adjust the way the scenario is expressed based on the estimated user's emotions. For example, if the user is excited, the scenario creation unit can create a visually stimulating scenario. If the user is relaxed, the scenario creation unit can create a calm scenario. Furthermore, if the user is focused, the scenario creation unit can create a detailed scenario. In this way, by adjusting the way the scenario is expressed according to the user's emotions, it is possible to generate effective videos.

[0119] The generation unit can estimate the user's emotion and adjust the style of the video based on the estimated user's emotion. For example, the generation unit can generate a video with a calm style when the user is relaxed. Furthermore, the generation unit can generate a video with a visually stimulating style when the user is excited. Furthermore, the generation unit can generate a video with a detailed style when the user is focused. In this way, by adjusting the style of the video according to the user's emotion, it is possible to generate an effective video.

[0120] The providing unit can estimate the user's emotions and adjust the video providing method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the video at a leisurely pace. If the user is in a hurry, the providing unit can provide a shortened version of the video. Furthermore, if the user is concentrating, the providing unit can provide a detailed video. This allows for effective video provision by adjusting the video providing method according to the user's emotions.

[0121] The storage unit can estimate the user's emotion and adjust the format of the video to be stored based on the estimated user's emotion. For example, the storage unit can store the video in a high-quality format when the user is relaxed. The storage unit can store the video in a low-quality format when the user is in a hurry. The storage unit can store the video in a standard-quality format when the user is concentrating. This allows for effective video storage by adjusting the format of the video to be stored according to the user's emotion.

[0122] When providing the video, the providing unit can recommend the most suitable video by referring to the user's learning history. For example, the providing unit can recommend related videos based on the content the user has studied in the past. The providing unit can also recommend the next video to study based on the user's learning progress. Furthermore, the providing unit can extract themes of interest from the user's learning history and recommend related videos. This enables effective learning by recommending the most suitable video based on the user's learning history.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The capture unit scans the contents of the reference book and captures them as digital data. For example, the capture unit can scan each page of the reference book with a scanner and save it as text data. The capture unit can adjust the scanning resolution and capture speed. Step 2: The analysis unit analyzes the digital data captured by the capture unit and understands its content. For example, the analysis unit analyzes the text data using natural language processing technology to understand the content of each chapter or section. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. Step 3: The scenario creation unit creates a video scenario based on the content analyzed by the analysis unit. For example, the scenario creation unit constructs a video scenario based on the content of each chapter or section. This scenario includes the main points and important points of each chapter or section. Step 4: The generation unit generates a video based on the scenario created by the scenario creation unit. For example, the generation unit generates a video using animation or illustrations based on the scenario. The generation unit can select the software to be used and the format of the video to be generated. Step 5: The providing unit provides the video generated by the generating unit. For example, the providing unit stores the generated video and provides it to the learner. The providing unit can adjust the platform and timing of providing the video.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] 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.

[0174] 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.

[0175] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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. An import section that scans the contents of reference books and imports them as digital data, an analysis unit that analyzes the digital data captured by the capture unit and understands the content; a scenario creation unit that creates a scenario for a video based on the content analyzed by the analysis unit; a generation unit that generates a video based on the scenario created by the scenario creation unit; a providing unit that provides the video generated by the generating unit. A system characterized by:

2. The scenario creation unit An editing unit that edits a scenario of a video based on the content analyzed by the analyzing unit 2. The system of claim 1.

3. The generation unit Equipped with a storage unit to save the generated video 2. The system of claim 1.

4. The capture unit is Scan each page of the reference book with a scanner and save it as text data.

2. The system of claim 1.

5. The analysis unit Analyze text data using natural language processing technology to understand the content of each chapter and section 2. The system of claim 1.

6. The generation unit Generate animations and illustrations based on a scenario 2. The system of claim 1.

7. The capture unit is Describe the specific technical details of estimating the user's emotions and adjusting the timing of scans based on the estimated user emotions.

2. The system of claim 1.

8. The capture unit is Detects the paper quality and printing condition of the reference book and automatically adjusts the appropriate scan settings 2. The system of claim 1.

Images