System

Abstract

An object of the system according to the embodiment is to analyze the contents of a technical paper and generate an explanatory video.SOLUTION: A system includes a technical paper analysis part, a scenario generation part, a moving image generation part, and a provision part. The technical paper analysis part analyzes the contents of the technical paper. The scenario generation unit generates a scenario of the commentary video on the basis of the content analyzed by the technical paper analysis unit. The moving image generation unit generates an explanatory moving image based on the scenario generated by the scenario generation unit. The provision unit provides the user with the explanatory video generated by the video generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that technical papers lack explanations to deepen understanding, making it difficult for readers to fully understand the content.

[0005] The system according to the embodiment aims to analyze the contents of technical papers and generate explanatory videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a technical paper analysis unit, a scenario generation unit, a video generation unit, and a providing unit. The technical paper analysis unit analyzes the content of the technical paper. The scenario generation unit generates a scenario for an explanatory video based on the content analyzed by the technical paper analysis unit. The video generation unit generates an explanatory video based on the scenario generated by the scenario generation unit. The providing unit provides the explanatory video generated by the video generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the contents of technical papers and generate explanatory videos. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The explanatory video generation system according to an embodiment of the present invention is a system in which the content of a technical paper is analyzed, and a generation AI generates explanatory videos that are then provided to users. As a result, the explanatory video generation system can automatically generate videos that explain the content of the technical paper in an easy-to-understand manner and provide them to users.

[0029] An explanatory video generation system according to an embodiment includes a technical paper analysis unit, a scenario generation unit, a video generation unit, and a provision unit. The technical paper analysis unit analyzes the content of a technical paper. For example, the technical paper analysis unit receives text data of a technical paper as input and analyzes the content. The technical paper analysis unit can also understand the structure and technical terminology of the paper and extract key points. The technical paper analysis unit can also organize the purpose, method, and results of the paper. For example, the technical paper analysis unit can clarify the purpose of the paper, analyze the method used, and summarize the results. The scenario generation unit generates a scenario for the explanatory video based on the content analyzed by the technical paper analysis unit. For example, the scenario generation unit generates a scenario including explanations of the main points of the paper, important figures and diagrams, and explanations of technical terminology based on the analysis results. The scenario generation unit can also generate a scenario that explains the advantages of a new algorithm or method in the paper. The scenario generation unit can also generate a scenario including animations and figures to visually represent the content of the paper. For example, the scenario generation unit generates a scenario that highlights important parts and dynamically displays figures and tables to emphasize the main points of the paper. The video generation unit generates an explanatory video based on the scenario generated by the scenario generation unit. For example, the video generation unit generates an explanatory video that includes audio commentary, animation, and the display of figures and tables based on the scenario. The video generation unit can also explain technical terms using visual animation. The video generation unit can also generate an explanatory video that highlights important parts and dynamically displays figures and tables to emphasize the main points of the paper. For example, the video generation unit automatically generates animations and figures and tables to visually represent the contents of the paper. The providing unit provides the explanatory video generated by the video generation unit to a user. For example, the providing unit provides the generated explanatory video to a user through an online platform or a streaming service. The providing unit can also provide the explanatory video in a downloadable format. The providing unit can also provide the explanatory video in multiple languages ​​and generate a multilingual video. For example, the providing unit provides an explanatory video that includes audio commentary in English, Japanese, Chinese, etc.As a result, the explanatory video generation system according to the embodiment can automatically generate videos that explain the contents of technical papers in an easy-to-understand manner and provide them to users. For example, when researchers or students read a new technical paper, they can refer to the explanatory videos to efficiently understand the contents of the paper.

[0030] When analyzing the content of a technical paper, the technical paper analysis unit can infer the intentions and background information of the paper's author and reflect this in the analysis results. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit refers to the author's past papers and research themes to understand the consistency and purpose of the author's research in order to infer the author's intentions. In addition, in order for the generation AI to infer the background information of a technical paper, the technical paper analysis unit analyzes the literature and related research cited by the paper to clarify the paper's positioning and background. Furthermore, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit infers the author's intentions and background information and focuses on analyzing the introduction and conclusion of the paper in order to reflect this in the analysis results. This allows the author's intentions and background information to be reflected in the analysis results, enabling a deeper understanding.

[0031] When analyzing the content of a technical paper, the technical paper analysis unit compares it with other related papers and patent documents to clarify the positioning of the technology. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit automatically searches for other related papers and analyzes citation relationships and technical differences to clarify the positioning of the technology. The generation AI also analyzes patent documents and compares them with the content of the technical paper to clarify the novelty and uniqueness of the technology. For example, the technical paper analysis unit identifies related patents by referencing a patent database. When the generation AI analyzes the content of a technical paper, the technical paper analysis unit compares it with other related papers and patent documents to analyze technological trends and developments to clarify the positioning of the technology. This makes it possible to clarify the positioning of the technology by comparing it with other related papers and patent documents.

[0032] When analyzing the content of a technical paper, the technical paper analysis unit simultaneously analyzes technical papers from different fields, making it possible to discover technical relationships between different fields. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit simultaneously analyzes technical papers from different fields and extracts keywords and topics from those fields to discover technical relationships between different fields. In addition, when the generation AI analyzes technical papers from different fields, the technical paper analysis unit refers to research results and technical application examples from those fields to discover technical relationships. For example, it cross-references papers from different fields. In addition, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit simultaneously analyzes technical papers from different fields and analyzes technological trends and developments in those fields to discover technical relationships between those fields. In this way, by analyzing technical papers from different fields, it is possible to discover technical relationships between different fields.

[0033] When analyzing the content of a technical paper, the technical paper analysis unit can automatically generate infographics to visually represent the content of the paper. For example, the technical paper analysis unit uses a generation AI to analyze the content of the technical paper and automatically generate infographics to visually represent that content. For example, it displays key points and data in graphs and charts. The technical paper analysis unit also analyzes the paper's charts and illustrations and extracts visual elements so that the generation AI can analyze the content of the technical paper and automatically generate infographics to visually represent that content. The technical paper analysis unit also visualizes the structure of the paper and the key points of each section so that the generation AI can analyze the content of the technical paper and automatically generate infographics to visually represent that content. This allows for a deeper understanding by automatically generating infographics to visually represent the content of the paper.

[0034] When generating a scenario for an explanatory video, the scenario generation unit can structure the content of the paper in a storytelling format to attract the viewer's interest. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit structures the content of the paper in a storytelling format, starting with the background and problem statement of the paper and explaining the solution and results in a systematic manner to attract the viewer's interest. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit structures the content in a storytelling format, explaining the content using specific examples and experimental results to attract the viewer's interest. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit structures the content in a storytelling format, emphasizing the important points of the paper as a climax to attract the viewer's interest. In this way, structuring the content in a storytelling format makes it easier to attract the viewer's interest.

[0035] The scenario generation unit can automatically generate animations and diagrams to visually express the contents of a paper when generating a scenario for an explanatory video. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit automatically generates animations to visually express the contents of the paper. For example, it shows the operation of an algorithm using animation. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit automatically generates diagrams to visually express the contents of the paper. For example, it shows changes in data using graphs and charts. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit analyzes the diagrams and illustrations in the paper and extracts visual elements to automatically generate animations and diagrams to visually express the content. This automatically generates animations and diagrams to visually express the content, thereby deepening the viewer's understanding.

[0036] When generating a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields, thereby showing the technological relevance between different fields. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields and extracts keywords and topics from those fields to show the technological relevance between those fields. The scenario generation unit also analyzes technical papers from different fields and references research results and technical application examples from those fields to show the technological relevance. For example, it cross-references papers from different fields. Furthermore, when the generation AI generates a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields and analyzes technological trends and developments in those fields to show the technological relevance between those fields. In this way, by taking up technical papers from different fields, it is possible to show the technological relevance between those fields.

[0037] When generating a scenario for an explanatory video, the scenario generation unit can generate multiple scenarios that explain the content of a paper from different perspectives. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit generates multiple scenarios, such as from a researcher's perspective, a practitioner's perspective, and a student's perspective, in order to explain the content of the paper from different perspectives. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit incorporates different approaches, such as technical details, application examples, and historical background, in order to explain the content from different perspectives. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit generates a scenario that combines knowledge from different fields of expertise in order to explain the content of the paper from different perspectives. In this way, by generating multiple scenarios that explain from different perspectives, the viewer's understanding is deepened.

[0038] The video generation unit can automatically generate animations and diagrams that are visually easy to understand when generating explanatory videos. For example, when the generation AI generates explanatory videos, the video generation unit automatically generates animations that are visually easy to understand. For example, it shows the operation of an algorithm using animation. Furthermore, when the generation AI generates explanatory videos, the video generation unit automatically generates diagrams that are visually easy to understand. For example, it shows changes in data using graphs and charts. Furthermore, when the generation AI generates explanatory videos, the video generation unit analyzes diagrams and illustrations in papers and extracts visual elements to automatically generate animations and diagrams that are visually easy to understand. This automatically generates animations and diagrams that are visually easy to understand, thereby deepening the viewer's understanding.

[0039] When generating an explanatory video, the video generation unit can provide audio commentary in multiple languages ​​and generate multilingual videos. For example, when the generation AI generates an explanatory video, the video generation unit provides audio commentary in multiple languages. For example, audio commentary in English, Japanese, Chinese, etc. is automatically generated. Furthermore, when the generation AI generates an explanatory video, the video generation unit analyzes the technical terms and expressions in each language and generates unified audio commentary to generate a multilingual video. Furthermore, when the generation AI generates an explanatory video, the video generation unit provides audio commentary in multiple languages ​​and analyzes audio data in each language to reproduce natural pronunciation and intonation to generate a multilingual video. As a result, audio commentary can be provided in multiple languages, thereby generating a multilingual video.

[0040] When generating explanatory videos, the video generation unit simultaneously takes up technical papers from different fields, thereby showing the technological relevance between different fields. For example, when the generation AI generates an explanatory video, the video generation unit simultaneously takes up technical papers from different fields and extracts keywords and topics from those fields to show the technological relevance between those fields. The video generation unit also analyzes technical papers from different fields and references research results and technical application examples from those fields to show the technological relevance. For example, it cross-references papers from different fields. Furthermore, when the generation AI generates explanatory videos, the video generation unit simultaneously takes up technical papers from different fields and analyzes technological trends and developments in those fields to show the technological relevance between those fields. In this way, by taking up technical papers from different fields, it is possible to show the technological relevance between those fields.

[0041] When generating explanatory videos, the video generation unit can generate multiple videos that explain the contents of a paper from different perspectives. For example, when the generation AI generates an explanatory video, the video generation unit generates multiple videos from the perspective of a researcher, a practitioner, a student, etc., to explain the contents of the paper from different perspectives. In addition, when the generation AI generates an explanatory video, the video generation unit incorporates different approaches, such as technical details, application examples, and historical background, to explain the contents from different perspectives. In addition, when the generation AI generates an explanatory video, the video generation unit generates a video that combines knowledge from different fields of expertise to explain the contents of the paper from different perspectives. In this way, by generating multiple videos that explain from different perspectives, the viewer's understanding is deepened.

[0042] The providing unit can include visually easy-to-understand animations and diagrams when providing the generated explanatory video to the user. For example, when the generation AI generates the explanatory video, the providing unit automatically generates visually easy-to-understand animations. For example, the operation of an algorithm is shown using animation. Furthermore, when the generation AI generates the explanatory video, the providing unit automatically generates visually easy-to-understand diagrams and charts. For example, changes in data are shown using graphs and charts. Furthermore, when the generation AI generates the explanatory video, the providing unit analyzes diagrams and illustrations in the paper and extracts visual elements to automatically generate visually easy-to-understand animations and diagrams. This deepens the user's understanding by including visually easy-to-understand animations and diagrams.

[0043] When providing the generated explanatory video to a user, the providing unit can provide audio commentary in multiple languages ​​and generate a multilingual video. For example, when the generation AI generates an explanatory video, the providing unit provides audio commentary in multiple languages. For example, audio commentary in English, Japanese, Chinese, etc. is automatically generated. Furthermore, when the generation AI generates an explanatory video, the providing unit analyzes the technical terms and expressions in each language and generates a unified audio commentary to generate a multilingual video. Furthermore, when the generation AI generates an explanatory video, the providing unit provides audio commentary in multiple languages ​​and analyzes audio data in each language to reproduce natural pronunciation and intonation to generate a multilingual video. In this way, by providing audio commentary in multiple languages, a multilingual video can be generated.

[0044] When providing the generated explanatory video to the user, the providing unit can simultaneously take up technical papers from different fields and demonstrate the technical relevance between different fields. For example, when the generation AI generates the explanatory video, the providing unit simultaneously takes up technical papers from different fields and extracts keywords and topics from those fields to demonstrate the technical relevance between those fields. The providing unit also analyzes technical papers from different fields and references research results and technical application examples from those fields to demonstrate the technical relevance. For example, the providing unit cross-references papers from those fields. Furthermore, when the generation AI generates the explanatory video, the providing unit simultaneously takes up technical papers from different fields and analyzes technological trends and developments in those fields to demonstrate the technical relevance between those fields. In this way, by taking up technical papers from different fields, the technical relevance between those fields can be demonstrated.

[0045] When providing the generated explanatory video to a user, the providing unit can generate multiple videos that explain the content of the paper from different perspectives. For example, when the generation AI generates an explanatory video, the providing unit generates multiple videos from the perspective of a researcher, a practitioner, a student, etc., to explain the content of the paper from different perspectives. In addition, when the generation AI generates an explanatory video, the providing unit incorporates different approaches, such as technical details, application examples, and historical background, to explain the content from different perspectives. In addition, when the generation AI generates an explanatory video, the providing unit generates a video that combines knowledge from different fields of expertise to explain the content of the paper from different perspectives. In this way, by generating multiple videos that explain from different perspectives, the viewer's understanding is deepened.

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

[0047] When analyzing the contents of technical papers, the technical paper analysis unit can estimate the intentions and background information of the paper's author and reflect this in the analysis results. For example, the technical paper analysis unit refers to the author's past papers and research themes to understand the consistency and purpose of the author's research. The technical paper analysis unit also analyzes the literature and related research cited by the paper to clarify the paper's positioning and background. Furthermore, by focusing on analyzing the introduction and conclusion of the paper, the technical paper analysis unit can reflect the author's intentions and background information in the analysis results. This allows for a deeper understanding by reflecting the author's intentions and background information in the analysis results.

[0048] When analyzing the contents of a technical paper, the technical paper analysis unit can compare it with other related papers and patent documents to clarify the positioning of the technology. For example, the technical paper analysis unit automatically searches for other related papers and analyzes citation relationships and technical differences to clarify the positioning of the technology. The technical paper analysis unit also analyzes patent documents and compares them with the contents of the technical paper to clarify the novelty and uniqueness of the technology. For example, it identifies related patents by referencing a patent database. The technical paper analysis unit can also clarify the positioning of the technology by analyzing technological trends and progress. This makes it possible to clarify the positioning of the technology by comparing it with other related papers and patent documents.

[0049] When analyzing the contents of technical papers, the technical paper analysis unit simultaneously analyzes technical papers from different fields, making it possible to discover technical relationships between different fields. For example, the technical paper analysis unit simultaneously analyzes technical papers from different fields and extracts keywords and topics from different fields. The technical paper analysis unit also discovers technical relationships by referring to research results and technical application examples from different fields. Furthermore, the technical paper analysis unit can discover technical relationships between different fields by analyzing technological trends and developments in different fields. In this way, by analyzing technical papers from different fields, it is possible to discover technical relationships between different fields.

[0050] When analyzing the contents of technical papers, the technical paper analysis unit can automatically generate infographics to visually represent the contents of the paper. For example, the technical paper analysis unit automatically generates infographics that display key points and data in graphs and charts. The technical paper analysis unit also automatically generates infographics by analyzing the charts and illustrations in the paper and extracting visual elements. Furthermore, the technical paper analysis unit can automatically generate infographics that visualize the structure of the paper and the main points of each section. This allows for a deeper understanding by automatically generating infographics to visually represent the contents of the paper.

[0051] When generating a scenario for an explanatory video, the scenario generation unit can structure the content of the paper in a storytelling format to attract the viewer's interest. For example, the scenario generation unit can start with the background and problem statement of the paper, and then explain the solution and results in a systematic manner. The scenario generation unit can also attract the viewer's interest by explaining the paper while incorporating specific examples and experimental results. Furthermore, the scenario generation unit can more easily attract the viewer's interest by emphasizing the important points of the paper as the climax. In this way, structuring the video in a storytelling format makes it easier to attract the viewer's interest.

[0052] When generating a scenario for an explanatory video, the scenario generation unit can automatically generate animations and diagrams to visually express the contents of a paper. For example, the scenario generation unit automatically generates animations that animate the operation of an algorithm. The scenario generation unit also automatically generates diagrams that show changes in data in graphs and charts. Furthermore, the scenario generation unit can automatically generate animations and diagrams by analyzing the diagrams and illustrations in the paper and extracting visual elements. This automatically generates animations and diagrams for visual expression, deepening the viewer's understanding.

[0053] When generating a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields, making it possible to show the technological relevance between different fields. For example, the scenario generation unit shows the technological relevance between different fields by extracting keywords and topics from different fields. The scenario generation unit also shows the technological relevance by referencing research results and technical application examples from different fields. Furthermore, the scenario generation unit can show the technological relevance between different fields by analyzing technological trends and developments in different fields. In this way, by taking up technical papers from different fields, it is possible to show the technological relevance between different fields.

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

[0055] Step 1: The technical paper analysis unit analyzes the content of the technical paper. For example, the technical paper analysis unit receives the text data of a technical paper as input and analyzes its content. The technical paper analysis unit can also understand the structure and terminology of the paper and extract its main points. Furthermore, it organizes the purpose, method, and results of the paper, clarifies the purpose, analyzes the methods used, and summarizes the results. Step 2: The scenario generation unit generates a scenario for the explanatory video based on the content analyzed by the technical paper analysis unit. For example, based on the analysis results, it generates a scenario that includes explanations of the paper's main points, important figures and diagrams, and technical terminology. It also generates a scenario that explains the advantages of new algorithms and methods, and a scenario that includes animations and figures to visually represent the content of the paper. Step 3: The video generation unit generates explanatory videos based on the scenario generated by the scenario generation unit. For example, based on the scenario, explanatory videos including audio commentary, animation, and the display of figures and tables are generated. Also, explanatory videos are generated that use visual animations to explain technical terms, highlight important parts, and dynamically display figures and tables to emphasize the main points of the paper. Step 4: The providing unit provides the explanatory video generated by the video generating unit to the user. For example, the generated explanatory video is provided to the user through an online platform or streaming service. The explanatory video may also be provided in a downloadable format and in multiple languages. For example, the explanatory video may include audio commentary in English, Japanese, Chinese, etc.

[0056] (Example 2) The explanatory video generation system according to an embodiment of the present invention is a system in which the content of a technical paper is analyzed, and a generation AI generates explanatory videos that are then provided to users. As a result, the explanatory video generation system can automatically generate videos that explain the content of the technical paper in an easy-to-understand manner and provide them to users.

[0057] An explanatory video generation system according to an embodiment includes a technical paper analysis unit, a scenario generation unit, a video generation unit, and a provision unit. The technical paper analysis unit analyzes the content of a technical paper. For example, the technical paper analysis unit receives text data of a technical paper as input and analyzes the content. The technical paper analysis unit can also understand the structure and technical terminology of the paper and extract key points. The technical paper analysis unit can also organize the purpose, method, and results of the paper. For example, the technical paper analysis unit can clarify the purpose of the paper, analyze the method used, and summarize the results. The scenario generation unit generates a scenario for the explanatory video based on the content analyzed by the technical paper analysis unit. For example, the scenario generation unit generates a scenario including explanations of the main points of the paper, important figures and diagrams, and explanations of technical terminology based on the analysis results. The scenario generation unit can also generate a scenario that explains the advantages of a new algorithm or method in the paper. The scenario generation unit can also generate a scenario including animations and figures to visually represent the content of the paper. For example, the scenario generation unit generates a scenario that highlights important parts and dynamically displays figures and tables to emphasize the main points of the paper. The video generation unit generates an explanatory video based on the scenario generated by the scenario generation unit. For example, the video generation unit generates an explanatory video that includes audio commentary, animation, and the display of figures and tables based on the scenario. The video generation unit can also explain technical terms using visual animation. The video generation unit can also generate an explanatory video that highlights important parts and dynamically displays figures and tables to emphasize the main points of the paper. For example, the video generation unit automatically generates animations and figures and tables to visually represent the contents of the paper. The providing unit provides the explanatory video generated by the video generation unit to a user. For example, the providing unit provides the generated explanatory video to a user through an online platform or a streaming service. The providing unit can also provide the explanatory video in a downloadable format. The providing unit can also provide the explanatory video in multiple languages ​​and generate a multilingual video. For example, the providing unit provides an explanatory video that includes audio commentary in English, Japanese, Chinese, etc.As a result, the explanatory video generation system according to the embodiment can automatically generate videos that explain the contents of technical papers in an easy-to-understand manner and provide them to users. For example, when researchers or students read a new technical paper, they can refer to the explanatory videos to efficiently understand the contents of the paper.

[0058] When analyzing the content of a technical paper, the technical paper analysis unit can infer the intentions and background information of the paper's author and reflect this in the analysis results. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit refers to the author's past papers and research themes to understand the consistency and purpose of the author's research in order to infer the author's intentions. In addition, in order for the generation AI to infer the background information of a technical paper, the technical paper analysis unit analyzes the literature and related research cited by the paper to clarify the paper's positioning and background. Furthermore, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit infers the author's intentions and background information and focuses on analyzing the introduction and conclusion of the paper in order to reflect this in the analysis results. This allows the author's intentions and background information to be reflected in the analysis results, enabling a deeper understanding.

[0059] When analyzing the content of a technical paper, the technical paper analysis unit compares it with other related papers and patent documents to clarify the positioning of the technology. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit automatically searches for other related papers and analyzes citation relationships and technical differences to clarify the positioning of the technology. The generation AI also analyzes patent documents and compares them with the content of the technical paper to clarify the novelty and uniqueness of the technology. For example, the technical paper analysis unit identifies related patents by referencing a patent database. When the generation AI analyzes the content of a technical paper, the technical paper analysis unit compares it with other related papers and patent documents to analyze technological trends and developments to clarify the positioning of the technology. This makes it possible to clarify the positioning of the technology by comparing it with other related papers and patent documents.

[0060] The technical paper analysis unit uses the emotion estimation function to analyze the reader's emotional response to the content of the paper and identify easy-to-understand and difficult parts. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit uses the emotion estimation function to analyze the reader's emotional response and identify easy-to-understand and difficult parts. For example, it analyzes the reader's facial expressions and voice. The technical paper analysis unit also uses the emotion estimation function to analyze the reader's emotional response to each section of the technical paper and identify easy-to-understand and difficult parts. For example, it collects reader feedback. When the generation AI analyzes the content of the technical paper, the technical paper analysis unit also uses the emotion estimation function to analyze the reader's emotional response and calculates the reader's emotional score to identify easy-to-understand and difficult parts. In this way, by analyzing the reader's emotional response, it is possible to identify easy-to-understand and difficult parts.

[0061] When analyzing the content of a technical paper, the technical paper analysis unit simultaneously analyzes technical papers from different fields, making it possible to discover technical relationships between different fields. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit simultaneously analyzes technical papers from different fields and extracts keywords and topics from those fields to discover technical relationships between different fields. In addition, when the generation AI analyzes technical papers from different fields, the technical paper analysis unit refers to research results and technical application examples from those fields to discover technical relationships. For example, it cross-references papers from different fields. In addition, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit simultaneously analyzes technical papers from different fields and analyzes technological trends and developments in those fields to discover technical relationships between those fields. In this way, by analyzing technical papers from different fields, it is possible to discover technical relationships between different fields.

[0062] When analyzing the content of a technical paper, the technical paper analysis unit can automatically generate infographics to visually represent the content of the paper. For example, the technical paper analysis unit uses a generation AI to analyze the content of the technical paper and automatically generate infographics to visually represent that content. For example, it displays key points and data in graphs and charts. The technical paper analysis unit also analyzes the paper's charts and illustrations and extracts visual elements so that the generation AI can analyze the content of the technical paper and automatically generate infographics to visually represent that content. The technical paper analysis unit also visualizes the structure of the paper and the key points of each section so that the generation AI can analyze the content of the technical paper and automatically generate infographics to visually represent that content. This allows for a deeper understanding by automatically generating infographics to visually represent the content of the paper.

[0063] The technical paper analysis unit can use the emotion estimation function to collect readers' emotional reactions to the content of the paper in real time and reflect them in the analysis results. For example, when the generation AI analyzes the content of a technical paper, the technical paper analysis unit can use the emotion estimation function to collect readers' emotional reactions in real time and reflect them in the analysis results. For example, it can analyze readers' facial expressions and voices. The technical paper analysis unit can also use the emotion estimation function to collect readers' emotional reactions to each section of the technical paper in real time and reflect them in the analysis results. For example, it can collect reader feedback. The technical paper analysis unit can also use the emotion estimation function to collect readers' emotional reactions in real time when the generation AI analyzes the content of a technical paper and calculate a reader's emotion score to reflect them in the analysis results. In this way, by collecting readers' emotional reactions in real time and reflecting them in the analysis results, more effective explanatory videos can be generated.

[0064] When generating a scenario for an explanatory video, the scenario generation unit can structure the content of the paper in a storytelling format to attract the viewer's interest. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit structures the content of the paper in a storytelling format, starting with the background and problem statement of the paper and explaining the solution and results in a systematic manner to attract the viewer's interest. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit structures the content in a storytelling format, explaining the content using specific examples and experimental results to attract the viewer's interest. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit structures the content in a storytelling format, emphasizing the important points of the paper as a climax to attract the viewer's interest. In this way, structuring the content in a storytelling format makes it easier to attract the viewer's interest.

[0065] The scenario generation unit can automatically generate animations and diagrams to visually express the contents of a paper when generating a scenario for an explanatory video. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit automatically generates animations to visually express the contents of the paper. For example, it shows the operation of an algorithm using animation. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit automatically generates diagrams to visually express the contents of the paper. For example, it shows changes in data using graphs and charts. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit analyzes the diagrams and illustrations in the paper and extracts visual elements to automatically generate animations and diagrams to visually express the content. This automatically generates animations and diagrams to visually express the content, thereby deepening the viewer's understanding.

[0066] The scenario generation unit can use the emotion estimation function to analyze the viewer's emotional response to the scenario and adjust the content to be more likely to interest the viewer. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit uses the emotion estimation function to analyze the viewer's emotional response and adjust the content to be more likely to interest the viewer. For example, the scenario generation unit analyzes the viewer's facial expressions and voice. The scenario generation unit also uses the emotion estimation function to analyze the viewer's emotional response to the scenario for the explanatory video and adjust the content to be more likely to interest the viewer. For example, the scenario generation unit collects viewer feedback. When the generation AI generates a scenario for an explanatory video, the scenario generation unit also uses the emotion estimation function to analyze the viewer's emotional response and calculates the viewer's emotion score in order to adjust the content to be more likely to interest the viewer. In this way, by analyzing the viewer's emotional response and adjusting the content to be more likely to interest the viewer, it becomes easier to attract the viewer's attention.

[0067] When generating a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields, thereby showing the technological relevance between different fields. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields and extracts keywords and topics from those fields to show the technological relevance between those fields. The scenario generation unit also analyzes technical papers from different fields and references research results and technical application examples from those fields to show the technological relevance. For example, it cross-references papers from different fields. Furthermore, when the generation AI generates a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields and analyzes technological trends and developments in those fields to show the technological relevance between those fields. In this way, by taking up technical papers from different fields, it is possible to show the technological relevance between those fields.

[0068] When generating a scenario for an explanatory video, the scenario generation unit can generate multiple scenarios that explain the content of a paper from different perspectives. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit generates multiple scenarios, such as from a researcher's perspective, a practitioner's perspective, and a student's perspective, in order to explain the content of the paper from different perspectives. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit incorporates different approaches, such as technical details, application examples, and historical background, in order to explain the content from different perspectives. In addition, when the generation AI generates a scenario for an explanatory video, the scenario generation unit generates a scenario that combines knowledge from different fields of expertise in order to explain the content of the paper from different perspectives. In this way, by generating multiple scenarios that explain from different perspectives, the viewer's understanding is deepened.

[0069] The scenario generation unit can use the emotion estimation function to collect viewers' emotional reactions to the scenario in real time and reflect them in the scenario. For example, when the generation AI generates a scenario for an explanatory video, the scenario generation unit uses the emotion estimation function to collect viewers' emotional reactions in real time and reflect them in the scenario. For example, the scenario generation unit analyzes viewers' facial expressions and voices. The scenario generation unit also uses the emotion estimation function to collect viewers' emotional reactions to the scenario for the explanatory video in real time and reflect them in the scenario. For example, the scenario generation unit collects viewers' feedback. Also, when the generation AI generates a scenario for an explanatory video, the scenario generation unit uses the emotion estimation function to collect viewers' emotional reactions in real time and calculates the viewer's emotion score to reflect them in the scenario. In this way, by collecting viewers' emotional reactions in real time and reflecting them in the scenario, it becomes easier to attract viewers' attention.

[0070] The video generation unit can automatically generate animations and diagrams that are visually easy to understand when generating explanatory videos. For example, when the generation AI generates explanatory videos, the video generation unit automatically generates animations that are visually easy to understand. For example, it shows the operation of an algorithm using animation. Furthermore, when the generation AI generates explanatory videos, the video generation unit automatically generates diagrams that are visually easy to understand. For example, it shows changes in data using graphs and charts. Furthermore, when the generation AI generates explanatory videos, the video generation unit analyzes diagrams and illustrations in papers and extracts visual elements to automatically generate animations and diagrams that are visually easy to understand. This automatically generates animations and diagrams that are visually easy to understand, thereby deepening the viewer's understanding.

[0071] When generating an explanatory video, the video generation unit can provide audio commentary in multiple languages ​​and generate multilingual videos. For example, when the generation AI generates an explanatory video, the video generation unit provides audio commentary in multiple languages. For example, audio commentary in English, Japanese, Chinese, etc. is automatically generated. Furthermore, when the generation AI generates an explanatory video, the video generation unit analyzes the technical terms and expressions in each language and generates unified audio commentary to generate a multilingual video. Furthermore, when the generation AI generates an explanatory video, the video generation unit provides audio commentary in multiple languages ​​and analyzes audio data in each language to reproduce natural pronunciation and intonation to generate a multilingual video. As a result, audio commentary can be provided in multiple languages, thereby generating a multilingual video.

[0072] The video generation unit can use the emotion estimation function to analyze the viewer's emotional response to the video and adjust the content to be more likely to interest the viewer. For example, when the generation AI generates an explanatory video, the video generation unit uses the emotion estimation function to analyze the viewer's emotional response and adjust the content to be more likely to interest the viewer. For example, the video generation unit analyzes the viewer's facial expressions and voice. The video generation unit also uses the emotion estimation function to analyze the viewer's emotional response to the explanatory video and adjust the content to be more likely to interest the viewer. For example, the video generation unit collects viewer feedback. Also, when the generation AI generates an explanatory video, the video generation unit uses the emotion estimation function to analyze the viewer's emotional response and calculates the viewer's emotion score in order to adjust the content to be more likely to interest the viewer. In this way, by analyzing the viewer's emotional response and adjusting the content to be more likely to interest the viewer, it becomes easier to attract the viewer's attention.

[0073] When generating explanatory videos, the video generation unit simultaneously takes up technical papers from different fields, thereby showing the technological relevance between different fields. For example, when the generation AI generates an explanatory video, the video generation unit simultaneously takes up technical papers from different fields and extracts keywords and topics from those fields to show the technological relevance between those fields. The video generation unit also analyzes technical papers from different fields and references research results and technical application examples from those fields to show the technological relevance. For example, it cross-references papers from different fields. Furthermore, when the generation AI generates explanatory videos, the video generation unit simultaneously takes up technical papers from different fields and analyzes technological trends and developments in those fields to show the technological relevance between those fields. In this way, by taking up technical papers from different fields, it is possible to show the technological relevance between those fields.

[0074] When generating explanatory videos, the video generation unit can generate multiple videos that explain the contents of a paper from different perspectives. For example, when the generation AI generates an explanatory video, the video generation unit generates multiple videos from the perspective of a researcher, a practitioner, a student, etc., to explain the contents of the paper from different perspectives. In addition, when the generation AI generates an explanatory video, the video generation unit incorporates different approaches, such as technical details, application examples, and historical background, to explain the contents from different perspectives. In addition, when the generation AI generates an explanatory video, the video generation unit generates a video that combines knowledge from different fields of expertise to explain the contents of the paper from different perspectives. In this way, by generating multiple videos that explain from different perspectives, the viewer's understanding is deepened.

[0075] The video generation unit can use the emotion estimation function to collect viewers' emotional reactions to the video in real time and reflect them in the video. For example, when the generation AI generates an explanatory video, the video generation unit uses the emotion estimation function to collect viewers' emotional reactions in real time and reflect them in the video. For example, the video generation unit analyzes viewers' facial expressions and voices. The video generation unit also uses the emotion estimation function to collect viewers' emotional reactions to the explanatory video in real time and reflect them in the video. For example, the video generation unit collects viewer feedback. The video generation unit also uses the emotion estimation function to collect viewers' emotional reactions in real time when the generation AI generates an explanatory video and calculates the viewer's emotion score to reflect them in the video. In this way, by collecting viewers' emotional reactions in real time and reflecting them in the video, it becomes easier to attract viewers' attention.

[0076] The providing unit can include visually easy-to-understand animations and diagrams when providing the generated explanatory video to the user. For example, when the generation AI generates the explanatory video, the providing unit automatically generates visually easy-to-understand animations. For example, the operation of an algorithm is shown using animation. Furthermore, when the generation AI generates the explanatory video, the providing unit automatically generates visually easy-to-understand diagrams and charts. For example, changes in data are shown using graphs and charts. Furthermore, when the generation AI generates the explanatory video, the providing unit analyzes diagrams and illustrations in the paper and extracts visual elements to automatically generate visually easy-to-understand animations and diagrams. This deepens the user's understanding by including visually easy-to-understand animations and diagrams.

[0077] When providing the generated explanatory video to a user, the providing unit can provide audio commentary in multiple languages ​​and generate a multilingual video. For example, when the generation AI generates an explanatory video, the providing unit provides audio commentary in multiple languages. For example, audio commentary in English, Japanese, Chinese, etc. is automatically generated. Furthermore, when the generation AI generates an explanatory video, the providing unit analyzes the technical terms and expressions in each language and generates a unified audio commentary to generate a multilingual video. Furthermore, when the generation AI generates an explanatory video, the providing unit provides audio commentary in multiple languages ​​and analyzes audio data in each language to reproduce natural pronunciation and intonation to generate a multilingual video. In this way, by providing audio commentary in multiple languages, a multilingual video can be generated.

[0078] The providing unit can use the emotion estimation function to analyze the viewer's emotional response to the video and adjust the content to be more likely to interest the viewer. For example, when the generation AI generates an explanatory video, the providing unit uses the emotion estimation function to analyze the viewer's emotional response and adjust the content to be more likely to interest the viewer. For example, the providing unit analyzes the viewer's facial expressions and voice. The providing unit also uses the emotion estimation function to analyze the viewer's emotional response to the explanatory video and adjust the content to be more likely to interest the viewer. For example, the providing unit collects viewer feedback. When the generation AI generates an explanatory video, the providing unit also uses the emotion estimation function to analyze the viewer's emotional response and calculates the viewer's emotion score in order to adjust the content to be more likely to interest the viewer. In this way, analyzing the viewer's emotional response and adjusting the content to be more likely to interest the viewer makes it easier to attract the viewer's attention.

[0079] When providing the generated explanatory video to the user, the providing unit can simultaneously take up technical papers from different fields and demonstrate the technical relevance between different fields. For example, when the generation AI generates the explanatory video, the providing unit simultaneously takes up technical papers from different fields and extracts keywords and topics from those fields to demonstrate the technical relevance between those fields. The providing unit also analyzes technical papers from different fields and references research results and technical application examples from those fields to demonstrate the technical relevance. For example, the providing unit cross-references papers from those fields. Furthermore, when the generation AI generates the explanatory video, the providing unit simultaneously takes up technical papers from different fields and analyzes technological trends and developments in those fields to demonstrate the technical relevance between those fields. In this way, by taking up technical papers from different fields, the technical relevance between those fields can be demonstrated.

[0080] When providing the generated explanatory video to a user, the providing unit can generate multiple videos that explain the content of the paper from different perspectives. For example, when the generation AI generates an explanatory video, the providing unit generates multiple videos from the perspective of a researcher, a practitioner, a student, etc., to explain the content of the paper from different perspectives. In addition, when the generation AI generates an explanatory video, the providing unit incorporates different approaches, such as technical details, application examples, and historical background, to explain the content from different perspectives. In addition, when the generation AI generates an explanatory video, the providing unit generates a video that combines knowledge from different fields of expertise to explain the content of the paper from different perspectives. In this way, by generating multiple videos that explain from different perspectives, the viewer's understanding is deepened.

[0081] The providing unit can use the emotion estimation function to collect viewers' emotional reactions to the video in real time and reflect them in the video. For example, when the generation AI generates an explanatory video, the providing unit uses the emotion estimation function to collect viewers' emotional reactions in real time and reflect them in the video. For example, the providing unit analyzes viewers' facial expressions and voices. The providing unit also uses the emotion estimation function to collect viewers' emotional reactions to the explanatory video in real time and reflect them in the video. For example, the providing unit collects viewer feedback. The providing unit also uses the emotion estimation function to collect viewers' emotional reactions in real time when the generation AI generates an explanatory video, and calculates the viewer's emotion score to reflect them in the video. In this way, by collecting viewers' emotional reactions in real time and reflecting them in the video, it becomes easier to attract viewers' attention.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] When analyzing the contents of technical papers, the technical paper analysis unit can estimate the intentions and background information of the paper's author and reflect this in the analysis results. For example, the technical paper analysis unit refers to the author's past papers and research themes to understand the consistency and purpose of the author's research. The technical paper analysis unit also analyzes the literature and related research cited by the paper to clarify the paper's positioning and background. Furthermore, by focusing on analyzing the introduction and conclusion of the paper, the technical paper analysis unit can reflect the author's intentions and background information in the analysis results. This allows for a deeper understanding by reflecting the author's intentions and background information in the analysis results.

[0084] When analyzing the contents of a technical paper, the technical paper analysis unit can compare it with other related papers and patent documents to clarify the positioning of the technology. For example, the technical paper analysis unit automatically searches for other related papers and analyzes citation relationships and technical differences to clarify the positioning of the technology. The technical paper analysis unit also analyzes patent documents and compares them with the contents of the technical paper to clarify the novelty and uniqueness of the technology. For example, it identifies related patents by referencing a patent database. The technical paper analysis unit can also clarify the positioning of the technology by analyzing technological trends and progress. This makes it possible to clarify the positioning of the technology by comparing it with other related papers and patent documents.

[0085] The technical paper analysis unit uses the emotion estimation function to analyze the reader's emotional response to the content of the paper, and can identify easy-to-understand points and difficult parts. For example, the technical paper analysis unit identifies easy-to-understand points and difficult parts by analyzing the reader's facial expressions and voice. The technical paper analysis unit also collects reader feedback and analyzes it using the emotion estimation function to identify easy-to-understand points and difficult parts. Furthermore, the technical paper analysis unit can identify easy-to-understand points and difficult parts by calculating the reader's emotion score. In this way, easy-to-understand points and difficult parts can be identified by analyzing the reader's emotional response.

[0086] When analyzing the contents of technical papers, the technical paper analysis unit simultaneously analyzes technical papers from different fields, making it possible to discover technical relationships between different fields. For example, the technical paper analysis unit simultaneously analyzes technical papers from different fields and extracts keywords and topics from different fields. The technical paper analysis unit also discovers technical relationships by referring to research results and technical application examples from different fields. Furthermore, the technical paper analysis unit can discover technical relationships between different fields by analyzing technological trends and developments in different fields. In this way, by analyzing technical papers from different fields, it is possible to discover technical relationships between different fields.

[0087] When analyzing the contents of technical papers, the technical paper analysis unit can automatically generate infographics to visually represent the contents of the paper. For example, the technical paper analysis unit automatically generates infographics that display key points and data in graphs and charts. The technical paper analysis unit also automatically generates infographics by analyzing the charts and illustrations in the paper and extracting visual elements. Furthermore, the technical paper analysis unit can automatically generate infographics that visualize the structure of the paper and the main points of each section. This allows for a deeper understanding by automatically generating infographics to visually represent the contents of the paper.

[0088] The technical paper analysis unit can use the emotion estimation function to collect readers' emotional reactions to the content of the paper in real time and reflect them in the analysis results. For example, the technical paper analysis unit collects emotional reactions in real time by analyzing readers' facial expressions and voices. The technical paper analysis unit also collects reader feedback and analyzes it in real time using the emotion estimation function, thereby reflecting it in the analysis results. Furthermore, the technical paper analysis unit can collect emotional reactions in real time by calculating readers' emotion scores and reflecting them in the analysis results. In this way, by collecting readers' emotional reactions in real time and reflecting them in the analysis results, more effective explanatory videos can be generated.

[0089] When generating a scenario for an explanatory video, the scenario generation unit can structure the content of the paper in a storytelling format to attract the viewer's interest. For example, the scenario generation unit can start with the background and problem statement of the paper, and then explain the solution and results in a systematic manner. The scenario generation unit can also attract the viewer's interest by explaining the paper while incorporating specific examples and experimental results. Furthermore, the scenario generation unit can more easily attract the viewer's interest by emphasizing the important points of the paper as the climax. In this way, structuring the video in a storytelling format makes it easier to attract the viewer's interest.

[0090] When generating a scenario for an explanatory video, the scenario generation unit can automatically generate animations and diagrams to visually express the contents of a paper. For example, the scenario generation unit automatically generates animations that animate the operation of an algorithm. The scenario generation unit also automatically generates diagrams that show changes in data in graphs and charts. Furthermore, the scenario generation unit can automatically generate animations and diagrams by analyzing the diagrams and illustrations in the paper and extracting visual elements. This automatically generates animations and diagrams for visual expression, deepening the viewer's understanding.

[0091] The scenario generation unit can use the emotion estimation function to analyze the viewer's emotional response to the scenario and adjust the content to be more likely to interest the viewer. For example, the scenario generation unit analyzes the viewer's facial expressions and voice to analyze the emotional response. The scenario generation unit also collects viewer feedback and analyzes it using the emotion estimation function to adjust the content to be more likely to interest the viewer. Furthermore, the scenario generation unit can calculate the viewer's emotion score to analyze the emotional response and adjust the content to be more likely to interest the viewer. In this way, analyzing the viewer's emotional response and adjusting the content to be more likely to interest the viewer makes it easier to attract the viewer's attention.

[0092] When generating a scenario for an explanatory video, the scenario generation unit simultaneously takes up technical papers from different fields, making it possible to show the technological relevance between different fields. For example, the scenario generation unit shows the technological relevance between different fields by extracting keywords and topics from different fields. The scenario generation unit also shows the technological relevance by referencing research results and technical application examples from different fields. Furthermore, the scenario generation unit can show the technological relevance between different fields by analyzing technological trends and developments in different fields. In this way, by taking up technical papers from different fields, it is possible to show the technological relevance between different fields.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The technical paper analysis unit analyzes the content of the technical paper. For example, the technical paper analysis unit receives the text data of a technical paper as input and analyzes its content. The technical paper analysis unit can also understand the structure and terminology of the paper and extract its main points. Furthermore, it organizes the purpose, method, and results of the paper, clarifies the purpose, analyzes the methods used, and summarizes the results. Step 2: The scenario generation unit generates a scenario for the explanatory video based on the content analyzed by the technical paper analysis unit. For example, based on the analysis results, it generates a scenario that includes explanations of the paper's main points, important figures and diagrams, and technical terminology. It also generates a scenario that explains the advantages of new algorithms and methods, and a scenario that includes animations and figures to visually represent the content of the paper. Step 3: The video generation unit generates explanatory videos based on the scenario generated by the scenario generation unit. For example, based on the scenario, explanatory videos including audio commentary, animation, and the display of figures and tables are generated. Also, explanatory videos are generated that use visual animations to explain technical terms, highlight important parts, and dynamically display figures and tables to emphasize the main points of the paper. Step 4: The providing unit provides the explanatory video generated by the video generating unit to the user. For example, the generated explanatory video is provided to the user through an online platform or streaming service. The explanatory video may also be provided in a downloadable format and in multiple languages. For example, the explanatory video may include audio commentary in English, Japanese, Chinese, etc.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[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 robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[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 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.

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

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a technical paper analysis section that analyzes the contents of technical papers; a scenario generation unit that generates a scenario for an explanatory video based on the content analyzed by the technical paper analysis unit; a video generation unit that generates an explanatory video based on the scenario generated by the scenario generation unit; a providing unit that provides the user with the explanatory video generated by the video generating unit. A system characterized by:

2. The technical paper analysis unit When analyzing the contents of technical papers, we estimate the intentions and background information of the paper's author and reflect this in the analysis results.

2. The system of claim 1.

3. The technical paper analysis unit When analyzing the contents of technical papers, compare them with other related papers and patent documents to clarify the position of the technology.

2. The system of claim 1.

4. The technical paper analysis unit When analyzing the content of technical papers, we analyze the reader's emotional response to the content of the paper and identify the easy-to-understand and difficult parts.

2. The system of claim 1.

5. The technical paper analysis unit When analyzing the contents of technical papers, simultaneously analyze technical papers from different fields to find technical relationships between different fields.

2. The system of claim 1.

6. The technical paper analysis unit When analyzing the contents of technical papers, we automatically generate infographics to visually represent the contents of the papers.

2. The system of claim 1.

7. The technical paper analysis unit When analyzing the content of technical papers, we collect readers' emotional reactions to the content of the paper in real time and reflect them in the analysis results.

2. The system of claim 1.

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