system

Abstract

The system according to this embodiment aims to automatically generate a large number of explanatory videos for efficiently learning the contents of business books. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a distribution unit, a reception unit, and an answer unit. The analysis unit analyzes the content of a business book. The generation unit generates an explanatory video based on the content analyzed by the analysis unit. The distribution unit distributes the explanatory video generated by the generation unit. The reception unit receives questions from users regarding the explanatory video distributed by the distribution unit. The answer unit generates answers to the questions received by the reception unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to generate a large number of and automatically generate explanatory videos for efficiently learning the content of business documents.

[0005] The system according to the embodiment aims to efficiently generate a large number of and automatically generate explanatory videos for learning the content of business documents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a distribution unit, a reception unit, and an answer unit. The analysis unit analyzes the content of a business book. The generation unit generates an explanatory video based on the content analyzed by the analysis unit. The distribution unit distributes the explanatory video generated by the generation unit. The reception unit receives questions from users regarding the explanatory video distributed by the distribution unit. The answer unit generates answers to the questions received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate a large number of explanatory videos for efficiently learning the contents of business books. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The explanatory video generation system according to an embodiment of the present invention is a system that uses generation AI to automatically generate a large number of explanatory videos from business books. This explanatory video generation system aims to transform adult learning by allowing people to easily acquire the essence of learning through an enjoyable experience, similar to popular educational channels on video distribution sites such as YouTube, rather than studying alone as in the past. First, it automatically generates a large number of explanatory videos from business books using various generation AIs. In conventional educational channels, "human labor" such as performers, planning, video shooting, and editing accounted for the majority of the cost, but by using generation AI, explanatory videos can be automatically generated, making it possible to suppress costs while maintaining quality. Next is the "production" part that utilizes generation AI. Existing educational channels have ensured audience attraction and continued viewing through the talent of their performers, but by using generation AI, it is possible to adjust the voice and reading speed to match the content and target audience, and to use appropriate VTubers, thus ensuring equivalent quality through a different approach. Moreover, the cost is almost constant, making it possible to create explanatory videos at a very low cost. Furthermore, after attracting viewers from existing video distribution sites such as YouTube, the company can distribute videos on its own website and also utilize AI generation to provide feedback on the content. This enables a deeper understanding of the content through two-way interaction, rather than the one-way approach of existing explanatory videos. Users are also provided with a link to purchase the original book, thus maintaining the value of existing book sales services. These features not only transform individual recurrent learning and corporate reskilling into tangible results, but also contribute to the Japanese labor market's challenge of "further strengthening the Japanese workforce," aiming to achieve both "business viability" and "social contribution." As a result, the explanatory video generation system can automatically analyze the content of business books, generate and distribute explanatory videos, accept questions from users, and generate answers.

[0029] The explanatory video generation system according to this embodiment comprises an analysis unit, a generation unit, a distribution unit, a reception unit, and a response unit. The analysis unit analyzes the content of a business book. Business books include, but are not limited to, management books, marketing books, and self-help books. The analysis unit analyzes the content of the business book using, for example, text mining technology. The analysis unit can also analyze the content of the business book using natural language processing technology. Furthermore, the analysis unit can also analyze the content of the business book using a generation AI. For example, the analysis unit inputs the text data of the business book into the generation AI and extracts important points. The generation unit generates an explanatory video based on the content analyzed by the analysis unit. The explanatory video includes, for example, the length of the video and the media format used, but is not limited to these examples. The generation unit generates the explanatory video using, for example, a generation AI. The generation unit can also set the voice and reading speed using the generation AI. Furthermore, the generation unit can also use an appropriate VTuber using the generation AI. For example, the generation unit inputs the analyzed content into the generation AI and generates an explanatory video. The distribution unit distributes explanatory videos generated by the generation unit to users. Distribution includes, but is not limited to, distribution platforms and distribution formats. The distribution unit distributes explanatory videos using existing video distribution sites such as YouTube. The distribution unit can also distribute explanatory videos on its own website. Furthermore, the distribution unit can optimize the content of the distribution using generation AI. For example, the distribution unit inputs the explanatory video into the generation AI and selects the optimal distribution method. The reception unit receives questions from users regarding the explanatory videos distributed by the distribution unit. Questions include, but are not limited to, text format and audio format. For example, the reception unit receives questions from users in text format. The reception unit can also receive questions from users in audio format. Furthermore, the reception unit can analyze the content of the questions using generation AI. For example, the reception unit inputs the question from the user into the generation AI and analyzes the content of the question. The answering unit generates answers to the questions received by the reception unit.The responses may include, but are not limited to, text or audio formats. The response unit generates responses using, for example, a generative AI. The response unit can also adjust the way the response is expressed using the generative AI. Furthermore, the response unit can adjust the level of detail in the response using the generative AI. For example, the response unit inputs the question into the generative AI and generates the response. As a result, the explanatory video generation system according to this embodiment can analyze the content of a business book, generate and distribute explanatory videos, receive questions from users, and generate answers.

[0030] The analysis unit can analyze the content of business books and extract key points. For example, the analysis unit can analyze the content of business books using text mining technology. For example, text mining technology analyzes the text data of business books and extracts frequently occurring keywords. The analysis unit can also analyze the content of business books using natural language processing technology. For example, natural language processing technology analyzes the text data of business books and analyzes the structure of the sentences. Furthermore, the analysis unit can also analyze the content of business books using generative AI. For example, generative AI takes the text data of business books as input and extracts key points. By extracting the key points of business books, the content of explanatory videos becomes more effective. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input the text data of business books into generative AI and have the generative AI perform the extraction of key points.

[0031] The generation unit can generate explanatory videos based on the extracted key points. The generation unit can generate explanatory videos using, for example, a generation AI. For example, the generation AI takes the extracted key points as input and generates an explanatory video. The generation unit can also use the generation AI to set the voice and reading speed. For example, the generation AI generates the voice for the explanatory video and sets an appropriate reading speed. Furthermore, the generation unit can use the generation AI to use an appropriate VTuber. For example, the generation AI selects and uses a VTuber suitable for the explanatory video. This allows for the provision of useful information to viewers by generating explanatory videos based on key points. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the extracted key points into a generation AI and have the generation AI perform the generation of the explanatory video.

[0032] The distribution unit can distribute the generated explanatory videos to users. The distribution unit can distribute the explanatory videos using existing video distribution sites such as YouTube. For example, the distribution unit can upload explanatory videos to YouTube and distribute them to users. The distribution unit can also distribute explanatory videos on its own website. For example, the distribution unit can upload explanatory videos to its own website and distribute them to users. Furthermore, the distribution unit can optimize the distribution content using generative AI. For example, the generative AI takes the explanatory video as input and selects the optimal distribution method. This allows the distribution unit to provide users with the essence of learning by distributing the generated explanatory videos. Some or all of the above processing in the distribution unit may be performed using generative AI, or it may be performed without using generative AI. For example, the distribution unit can input the explanatory video into the generative AI and have the generative AI select the optimal distribution method.

[0033] The reception desk can receive questions from users. The reception desk can receive questions from users in text format, for example. For example, the reception desk can receive questions in text format entered by the user. The reception desk can also receive questions from users in voice format, for example. For example, the reception desk can receive questions in voice format recorded by the user. Furthermore, the reception desk can analyze the content of the questions using a generative AI. For example, the generative AI takes the question from the user as input and analyzes the content of the question. This enables two-way interaction by receiving questions from users. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the question from the user into a generative AI and have the generative AI perform the analysis of the question content.

[0034] The answering unit can generate answers to received questions. The answering unit can generate answers using, for example, a generative AI. For example, the generative AI takes the received question as input and generates an answer. The answering unit can also adjust the way the answer is expressed using the generative AI. For example, the generative AI adjusts the tone and style of the answer. Furthermore, the answering unit can also adjust the level of detail of the answer using the generative AI. For example, the generative AI adjusts the level of detail of the answer according to the importance of the question. In this way, the user's doubts can be resolved by generating answers to received questions. Some or all of the above processing in the answering unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the answering unit can input the received question into a generative AI and have the generative AI perform the generation of the answer.

[0035] The generation unit can use a generation AI to set the voice, reading speed, and use of a VTuber to match the content and target audience. For example, the generation unit can use the generation AI to set the voice and reading speed. For example, the generation AI generates the voice for an explanatory video and sets an appropriate reading speed. The generation unit can also use the generation AI to use an appropriate VTuber. For example, the generation AI selects and uses a VTuber suitable for the explanatory video. This improves the viewing experience by setting the voice, reading speed, and use of a VTuber to match the viewer. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can have the generation AI perform the settings for the voice and VTuber of an explanatory video.

[0036] The distribution department can attract viewers from existing video distribution sites such as YouTube. For example, the distribution department can use existing video distribution sites such as YouTube to attract viewers. For example, the distribution department can upload explanatory videos to YouTube to attract viewers. The distribution department can also implement SEO measures to attract viewers from search engines. For example, the distribution department can promote viewers from search engines by including appropriate keywords in the titles and descriptions of explanatory videos. Furthermore, the distribution department can also attract viewers by conducting advertising campaigns. For example, the distribution department can use YouTube ads and social media ads to promote explanatory videos and attract viewers. In this way, the distribution department can increase its audience by attracting viewers from existing video distribution sites. Some or all of the above processes in the distribution department may be performed using, for example, generative AI, or not using generative AI. For example, the distribution department can have generative AI select the method of attracting viewers.

[0037] The analysis unit can improve the accuracy of its analysis of business book content based on the author's intentions and background information. For example, the analysis unit can use a generative AI to analyze the content of business books. For example, the generative AI takes the text data of the business book as input and performs the analysis while considering the author's intentions and background information. The analysis unit can also refer to the author's past interviews and lectures to perform an analysis that reflects their intentions. For example, the generative AI takes the author's interviews and lectures as input and performs an analysis that reflects their intentions. Furthermore, the analysis unit can refer to the author's other works to perform a consistent analysis. For example, the generative AI takes the author's other works as input and performs a consistent analysis. This improves the accuracy of the analysis by considering the author's intentions and background information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the text data of the business book into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0038] The analysis unit can apply different analysis algorithms to each chapter of a business book during analysis. For example, the analysis unit can use a generative AI to apply different analysis algorithms to each chapter of a business book. For example, the generative AI selects an appropriate analysis algorithm according to the theme of each chapter. The analysis unit can also adjust the depth of analysis according to the complexity of the content of each chapter. For example, the generative AI takes the complexity of the content of each chapter as input and adjusts the depth of analysis. Furthermore, the analysis unit can combine different analysis methods according to the purpose of each chapter. For example, the generative AI performs analysis by combining different analysis methods according to the purpose of each chapter. This improves the accuracy of the analysis by applying an appropriate analysis algorithm to each chapter. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input the data of each chapter of a business book into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0039] The analysis unit can improve the accuracy of its analysis based on the publication year of the business book and the author's past works. For example, the analysis unit can analyze the content of the business book using a generative AI. For example, the generative AI takes the text data of the business book as input and performs the analysis considering the publication year and the author's past works. The analysis unit can also perform an analysis that takes the historical context into account based on the publication year of the business book. For example, the generative AI takes the publication year of the business book as input and performs an analysis that takes the historical context into account. Furthermore, the analysis unit can also refer to the author's past works to perform a consistent analysis. For example, the generative AI takes the author's past works as input and performs a consistent analysis. This improves the accuracy of the analysis by referring to the publication year and the author's past works. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the text data of the business book into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0040] The analysis unit can perform analysis by referring to other literature and materials related to the content of the business book. For example, the analysis unit can analyze the content of the business book using a generative AI. For example, the generative AI takes the text data of the business book as input and performs analysis by referring to related literature and materials. The analysis unit can also refer to academic papers related to the content of the business book to perform analysis that promotes a deeper understanding. For example, the generative AI takes related academic papers as input and performs analysis that promotes a deeper understanding. Furthermore, the analysis unit can also refer to other books related to the content of the business book to perform a consistent analysis. For example, the generative AI takes other related books as input and performs analysis that promotes a consistent analysis. This improves the depth of the analysis by referring to related literature and materials. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the text data of the business book into a generative AI and have the generative AI perform the referencing of related literature.

[0041] The generation unit can add visual effects to highlight key points of a business book when generating explanatory videos. For example, the generation unit can generate explanatory videos using a generation AI. For instance, the generation AI takes key points from a business book as input and generates an explanatory video. The generation unit can also add visual effects using the generation AI. For example, the generation AI can add text highlighting to emphasize key points. Furthermore, the generation unit can add graphs and charts using the generation AI. For example, the generation AI generates graphs and charts to visually represent key points. Additionally, the generation unit can add animation effects using the generation AI. For example, the generation AI generates animation effects to highlight key points. This visually emphasizes key points, enhancing viewer understanding. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can have the generation AI perform the addition of visual effects.

[0042] The generation unit can add interactive elements based on the content of a business book when generating explanatory videos. For example, the generation unit can generate explanatory videos using a generation AI. For example, the generation AI takes the content of a business book as input and generates an explanatory video. The generation unit can also add interactive elements using the generation AI. For example, the generation AI can add an interactive element that allows the user to answer questions in the video. Furthermore, the generation unit can add an interactive element that allows the user to select options using the generation AI. For example, the generation AI generates an interactive element that allows the user to select options in the video. Furthermore, the generation unit can add an interactive element that allows the user to provide feedback using the generation AI. For example, the generation AI generates an interactive element that allows the user to provide feedback in the video. By adding interactive elements, the desire to participate in the viewer's activities increases. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have the generation AI perform the addition of interactive elements.

[0043] The generation unit can add examples and case studies related to the content of the business book when generating explanatory videos. The generation unit can generate explanatory videos using, for example, a generation AI. For example, the generation AI takes the content of the business book as input and generates an explanatory video. The generation unit can also add examples and case studies using the generation AI. For example, the generation AI adds examples related to the content of the business book to the explanatory video. Furthermore, the generation unit can also add case studies using the generation AI. For example, the generation AI adds case studies related to the content of the business book to the explanatory video. Furthermore, the generation unit can also add success stories using the generation AI. For example, the generation AI adds success stories related to the content of the business book to the explanatory video. In this way, adding examples and case studies deepens the viewer's understanding. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have the generation AI perform the addition of examples and case studies.

[0044] The generation unit can incorporate quizzes and tests based on the content of business books when generating explanatory videos. For example, the generation unit generates explanatory videos using a generation AI. For example, the generation AI takes the content of a business book as input and generates an explanatory video. The generation unit can also incorporate quizzes and tests using the generation AI. For example, the generation AI incorporates a quiz into the explanatory video to check understanding based on the content of the business book. Furthermore, the generation unit can incorporate tests to enhance learning effectiveness using the generation AI. For example, the generation AI incorporates a test into the explanatory video based on the content of the business book. Furthermore, the generation unit can incorporate review quizzes using the generation AI. For example, the generation AI incorporates a review quiz into the explanatory video based on the content of the business book. By incorporating quizzes and tests, the viewer's understanding can be checked, and the learning effect can be enhanced. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have the generation AI perform the incorporation of quizzes and tests.

[0045] The distribution unit can select a distribution method by referring to the user's past viewing history at the time of distribution. For example, the distribution unit can refer to the user's past viewing history using a generative AI. For example, the generative AI takes the user's past viewing history as input and selects the optimal distribution method. The distribution unit can also select the optimal distribution method by analyzing the trends of videos the user has watched in the past. For example, the generative AI takes the trends of videos the user has watched in the past as input and selects the optimal distribution method. Furthermore, the distribution unit can also select the optimal distribution timing by referring to the time of day when the user has watched videos in the past. For example, the generative AI takes the time of day when the user has watched videos in the past as input and selects the optimal distribution timing. In this way, by referring to past viewing history, the distribution unit can provide viewers with the most suitable distribution method. Some or all of the above processing in the distribution unit may be performed using a generative AI, for example, or without using a generative AI. For example, the distribution unit can input the user's viewing history into a generative AI and have the generative AI select the distribution method.

[0046] The distribution unit can select a distribution format based on the user's device information during distribution. For example, the distribution unit may use a generative AI to access the user's device information. For example, the generative AI may take the user's device information as input and select the optimal distribution format. Furthermore, if the user is using a smartphone, the distribution unit can select a mobile-optimized distribution format. For example, the generative AI may take the input that the user is using a smartphone and select a mobile-optimized distribution format. In addition, if the user is using a tablet, the distribution unit can select a large-screen-optimized distribution format. For example, the generative AI may take the input that the user is using a tablet and select a large-screen-optimized distribution format. This allows the distribution unit to provide the optimal distribution format to viewers by considering device information. Some or all of the above processing in the distribution unit may be performed using a generative AI, or without using a generative AI. For example, the distribution unit can input the user's device information into a generative AI and have the generative AI select the distribution format.

[0047] The distribution unit can prioritize the delivery of highly relevant content based on the user's geographical location information during distribution. For example, the distribution unit can use a generative AI to refer to the user's geographical location information. For example, the generative AI takes the user's geographical location information as input and selects highly relevant content. Furthermore, if the user is in a specific region, the distribution unit can prioritize the delivery of content related to that region. For example, the generative AI takes the user's location as input and selects content related to that region. In addition, if the user is traveling, the distribution unit can prioritize the delivery of content related to their travel destination. For example, the generative AI takes the user's travel status as input and selects content related to their travel destination. In this way, by considering geographical location information, highly relevant content can be provided to viewers. Some or all of the above processing in the distribution unit may be performed using a generative AI, for example, or without using a generative AI. For example, the distribution unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant content.

[0048] The distribution unit can deliver relevant content based on the user's social media activity at the time of distribution. The distribution unit can analyze the user's social media activity using, for example, generative AI. For example, the generative AI takes the user's social media activity as input and selects relevant content. The distribution unit can also deliver content related to topics the user has shown interest in on social media. For example, the generative AI takes the topics the user has shown interest in on social media as input and selects relevant content. Furthermore, the distribution unit can deliver content related to accounts the user follows on social media. For example, the generative AI takes the accounts the user follows as input and selects relevant content. In this way, by analyzing social media activity, the distribution unit can provide viewers with highly relevant content. Some or all of the above processing in the distribution unit may be performed using, for example, generative AI, or without generative AI. For example, the distribution unit can input the user's social media activity into the generative AI and have the generative AI select relevant content.

[0049] The reception unit can select a reception method based on the user's past question history when a question is received. For example, the reception unit can refer to the user's past question history using a generative AI. For example, the generative AI takes the user's past question history as input and selects the most suitable reception method. The reception unit can also automatically display topics that the user has frequently asked about in the past as suggestions. For example, the generative AI takes topics that the user has frequently asked about in the past as input and displays them as suggestions. Furthermore, the reception unit can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. For example, the generative AI takes question methods that the user has used in the past as input and suggests them preferentially. In this way, by referring to the past question history, the reception unit can provide the user with the most suitable question reception method. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's question history into a generative AI and have the generative AI select the reception method.

[0050] The reception unit can filter questions based on the user's current areas of interest when they are received. For example, the reception unit can use a generative AI to identify the user's current areas of interest. For example, the generative AI can take the user's search history and viewing history as input to identify the current areas of interest. The reception unit can also prioritize receiving questions related to topics the user is currently interested in. For example, the generative AI can take the topics the user is currently interested in as input and prioritize receiving related questions. Furthermore, the reception unit can also prioritize receiving questions related to keywords the user has recently searched for. For example, the generative AI can take the keywords the user has recently searched for as input and prioritize receiving related questions. This allows the reception unit to prioritize receiving highly relevant questions by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's areas of interest into a generative AI and have the generative AI perform the filtering.

[0051] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, the reception desk can use a generative AI to refer to the user's geographical location. For example, the generative AI takes the user's geographical location as input and selects highly relevant questions. Furthermore, if the user is in a specific region, the reception desk can prioritize receiving questions related to that region. For example, the generative AI takes the user's location as input and selects questions related to that region. In addition, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. For example, the generative AI takes the user's travel status as input and selects questions related to their travel destination. In this way, by considering geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using a generative AI, or without one. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI select highly relevant questions.

[0052] The reception desk can accept questions based on the user's social media activity when a question is received. The reception desk can analyze the user's social media activity using, for example, generative AI. For example, the generative AI takes the user's social media activity as input and selects relevant questions. The reception desk can also accept questions related to topics the user has shown interest in on social media. For example, the generative AI takes the topics the user has shown interest in on social media as input and selects relevant questions. Furthermore, the reception desk can accept questions related to accounts the user follows on social media. For example, the generative AI takes the accounts the user follows as input and selects relevant questions. In this way, by analyzing social media activity, the reception desk can accept highly relevant questions. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's social media activity into the generative AI and have the generative AI select relevant questions.

[0053] The answering unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the answering unit can use a generation AI to evaluate the importance of the question. For example, the generation AI takes the content of the question as input and evaluates its importance. The answering unit can also adjust the level of detail in the answer based on the importance of the question. For example, it can generate a detailed answer for important questions. It can also generate a concise answer for general questions. Furthermore, it can generate a quick answer for urgent questions. In this way, by adjusting the level of detail in the answer based on the importance of the question, an appropriate answer can be provided. Some or all of the above processing in the answering unit may be performed using a generation AI, for example, or without a generation AI. For example, the answering unit can input the importance of the question into the generation AI and have the generation AI perform the adjustment of the level of detail in the answer.

[0054] The answering unit can apply different answering algorithms depending on the question category when generating answers. For example, the answering unit can use a generative AI to identify the question category. For example, the generative AI takes the question content as input and identifies the category. The answering unit can also apply different answering algorithms depending on the question category. For example, a specialized answering algorithm can be applied to technical questions. A concise answering algorithm can also be applied to general questions. Furthermore, a rapid answering algorithm can be applied to urgent questions. By applying the appropriate answering algorithm according to the question category, more accurate answers can be provided. Some or all of the above processing in the answering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the answering unit can input the question category into a generative AI and have the generative AI perform the application of the answering algorithm.

[0055] The answering unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the answering unit can use a generation AI to evaluate the submission timing of questions. For example, the generation AI takes the date and time of question submission as input and determines the priority. The answering unit can also determine the priority of answers based on when the questions were submitted. For example, it can generate answers quickly for urgent questions. For general questions, it can generate answers with normal priority. Furthermore, it can generate detailed answers for important questions. This allows for the rapid provision of answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the answering unit may be performed using a generation AI, for example, or without a generation AI. For example, the answering unit can input the submission timing of questions into the generation AI and have the generation AI determine the priority of answers.

[0056] The answering unit can adjust the order of answers based on the relevance of the questions when generating answers. The answering unit can evaluate the relevance of questions using, for example, a generation AI. For example, the generation AI takes the content of the questions as input and evaluates the relevance. The answering unit can also adjust the order of answers based on the relevance of the questions. For example, it can generate answers first for important questions. For general questions, it can also generate answers in the usual order. Furthermore, it can generate answers quickly for urgent questions. In this way, by adjusting the order of answers based on the relevance of the questions, answers can be provided quickly for important questions. Some or all of the above processing in the answering unit may be performed using, for example, a generation AI, or without a generation AI. For example, the answering unit can input the relevance of the questions into the generation AI and have the generation AI perform the adjustment of the order of answers.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can adjust the depth of analysis when analyzing the content of business books by referring to the user's past learning history. For example, based on what the user has learned in the past, it can analyze parts that the user already understands concisely and parts that are not well understood in detail. The analysis unit can also adjust the speed of the analysis according to the user's learning pace. For example, if the user is learning at a fast pace, the analysis can proceed quickly, and if the user is learning slowly, detailed explanations can be added. Furthermore, the analysis unit can customize the analysis method according to the user's learning style. For example, it can make extensive use of graphs and charts for visual learners and emphasize audio explanations for auditory learners.

[0059] The generation unit can customize the content of explanatory videos by referring to the user's past viewing history. For example, it can highlight relevant topics based on the content of videos the user has watched in the past. The generation unit can also adjust the length of the video based on the user's viewing history. For example, if the user prefers short videos, it can generate a short video that gets straight to the point, and if the user prefers longer videos, it can generate a video with detailed explanations. Furthermore, the generation unit can adjust the style of the video based on the user's viewing history. For example, if the user prefers animation, it can generate a video that uses a lot of animation, and if the user prefers live-action, it can generate a video that includes a lot of live-action footage.

[0060] The distribution team can prioritize the delivery of explanatory videos on business books relevant to a user's region based on their geographical location. For example, if a user is in a specific region, the distribution team can deliver explanatory videos on books related to the business environment and culture of that region. The distribution team can also provide information on local events and seminars within the videos based on the user's geographical location. For example, if a user is in a specific region, the distribution team can introduce information on business seminars and events held in that region within the videos. Furthermore, the distribution team can cover local news and topics within the videos based on the user's geographical location. For example, if a user is in a specific region, the distribution team can explain the latest news and topics from that region within the videos.

[0061] The reception desk can prioritize questions based on the user's social media activity. For example, it can prioritize questions related to topics the user has shown interest in on social media. It can also prioritize questions related to accounts the user follows on social media. For example, it can prioritize questions related to business influencers the user follows. Furthermore, the reception desk can adjust the priority of questions based on the user's frequency of social media activity. For example, it can prioritize questions from users who are actively using social media.

[0062] The answer function can adjust the level of detail in its answers based on the user's past question history. For example, if the user has previously requested detailed answers, it can generate detailed answers; if they have requested concise answers, it can generate concise answers. The answer function can also provide additional relevant information based on the user's past question history. For example, it can provide new information or updates related to topics the user has previously asked about. Furthermore, the answer function can adjust the format of its answers based on the user's past question history. For example, if the user has previously preferred text-based answers, it can provide text-based answers; if they have preferred audio-based answers, it can provide audio-based answers.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The analysis unit analyzes the content of the business book. Business books include, but are not limited to, management books, marketing books, and self-help books. The analysis unit uses text mining technology, natural language processing technology, and generative AI to analyze the content of the business book and extract key points. Step 2: The generation unit generates an explanatory video based on the analysis performed by the analysis unit. The explanatory video includes, but is not limited to, the video length and the media format used. The generation unit can also generate the explanatory video using a generation AI, set the voice and reading speed, and use an appropriate VTuber. Step 3: The distribution unit delivers the explanatory videos generated by the generation unit to users. Distribution includes, but is not limited to, the distribution platform and format. The distribution unit can also distribute the explanatory videos on existing video distribution sites such as YouTube or on its own website, and optimize the content using generation AI. Step 4: The reception desk receives questions from users regarding the explanatory videos distributed by the distribution desk. Questions may be in text format, audio format, or other formats. The reception desk can accept questions from users in text format or audio format and can also analyze the content of the questions using generative AI. Step 5: The response unit generates answers to the questions received by the reception unit. These answers may be in text format, audio format, or other formats. The response unit can generate answers using a generation AI and can also adjust the wording and level of detail of the answers.

[0065] (Example of form 2) The explanatory video generation system according to an embodiment of the present invention is a system that uses generation AI to automatically generate a large number of explanatory videos from business books. This explanatory video generation system aims to transform adult learning by allowing people to easily acquire the essence of learning through an enjoyable experience, similar to popular educational channels on video distribution sites such as YouTube, rather than studying alone as in the past. First, it automatically generates a large number of explanatory videos from business books using various generation AIs. In conventional educational channels, "human labor" such as performers, planning, video shooting, and editing accounted for the majority of the cost, but by using generation AI, explanatory videos can be automatically generated, making it possible to suppress costs while maintaining quality. Next is the "production" part using generation AI. Existing educational channels have ensured audience attraction and continued viewing through the talent of their performers, but by using generation AI, it is possible to adjust the voice and reading speed to match the content and target audience, and to use appropriate VTubers, thus ensuring equivalent quality through a different approach. Moreover, the cost is almost constant, making it possible to create explanatory videos at a very low cost. Furthermore, after attracting viewers from existing video distribution sites such as YouTube, the company can distribute videos on its own website and also utilize AI generation to provide feedback on the content. This enables a deeper understanding of the content through two-way interaction, rather than the one-way approach of existing explanatory videos. Users are also provided with a link to purchase the original book, thus maintaining the value of existing book sales services. These features not only transform individual recurrent learning and corporate reskilling into tangible results, but also contribute to the Japanese labor market's challenge of "further strengthening the Japanese workforce," aiming to achieve both "business viability" and "social contribution." As a result, the explanatory video generation system can automatically analyze the content of business books, generate and distribute explanatory videos, accept questions from users, and generate answers.

[0066] The explanatory video generation system according to this embodiment comprises an analysis unit, a generation unit, a distribution unit, a reception unit, and a response unit. The analysis unit analyzes the content of a business book. Business books include, but are not limited to, management books, marketing books, and self-help books. The analysis unit analyzes the content of the business book using, for example, text mining technology. The analysis unit can also analyze the content of the business book using natural language processing technology. Furthermore, the analysis unit can also analyze the content of the business book using a generation AI. For example, the analysis unit inputs the text data of the business book into the generation AI and extracts important points. The generation unit generates an explanatory video based on the content analyzed by the analysis unit. The explanatory video includes, for example, the length of the video and the media format used, but is not limited to these examples. The generation unit generates the explanatory video using, for example, a generation AI. The generation unit can also set the voice and reading speed using the generation AI. Furthermore, the generation unit can also use an appropriate VTuber using the generation AI. For example, the generation unit inputs the analyzed content into the generation AI and generates an explanatory video. The distribution unit distributes explanatory videos generated by the generation unit to users. Distribution includes, but is not limited to, distribution platforms and distribution formats. The distribution unit distributes explanatory videos using existing video distribution sites such as YouTube. The distribution unit can also distribute explanatory videos on its own website. Furthermore, the distribution unit can optimize the content of the distribution using generation AI. For example, the distribution unit inputs the explanatory video into the generation AI and selects the optimal distribution method. The reception unit receives questions from users regarding the explanatory videos distributed by the distribution unit. Questions include, but are not limited to, text format and audio format. For example, the reception unit receives questions from users in text format. The reception unit can also receive questions from users in audio format. Furthermore, the reception unit can analyze the content of the questions using generation AI. For example, the reception unit inputs the question from the user into the generation AI and analyzes the content of the question. The answering unit generates answers to the questions received by the reception unit.The responses may include, but are not limited to, text or audio formats. The response unit generates responses using, for example, a generative AI. The response unit can also adjust the way the response is expressed using the generative AI. Furthermore, the response unit can adjust the level of detail in the response using the generative AI. For example, the response unit inputs the question into the generative AI and generates the response. As a result, the explanatory video generation system according to this embodiment can analyze the content of a business book, generate and distribute explanatory videos, receive questions from users, and generate answers.

[0067] The analysis unit can analyze the content of business books and extract key points. For example, the analysis unit can analyze the content of business books using text mining technology. For example, text mining technology analyzes the text data of business books and extracts frequently occurring keywords. The analysis unit can also analyze the content of business books using natural language processing technology. For example, natural language processing technology analyzes the text data of business books and analyzes the structure of the sentences. Furthermore, the analysis unit can also analyze the content of business books using generative AI. For example, generative AI takes the text data of business books as input and extracts key points. By extracting the key points of business books, the content of explanatory videos becomes more effective. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input the text data of business books into generative AI and have the generative AI perform the extraction of key points.

[0068] The generation unit can generate explanatory videos based on the extracted key points. The generation unit can generate explanatory videos using, for example, a generation AI. For example, the generation AI takes the extracted key points as input and generates an explanatory video. The generation unit can also use the generation AI to set the voice and reading speed. For example, the generation AI generates the voice for the explanatory video and sets an appropriate reading speed. Furthermore, the generation unit can use the generation AI to use an appropriate VTuber. For example, the generation AI selects and uses a VTuber suitable for the explanatory video. This allows for the provision of useful information to viewers by generating explanatory videos based on key points. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the extracted key points into a generation AI and have the generation AI perform the generation of the explanatory video.

[0069] The distribution unit can distribute the generated explanatory videos to users. The distribution unit can distribute the explanatory videos using existing video distribution sites such as YouTube. For example, the distribution unit can upload explanatory videos to YouTube and distribute them to users. The distribution unit can also distribute explanatory videos on its own website. For example, the distribution unit can upload explanatory videos to its own website and distribute them to users. Furthermore, the distribution unit can optimize the distribution content using generative AI. For example, the generative AI takes the explanatory video as input and selects the optimal distribution method. This allows the distribution unit to provide users with the essence of learning by distributing the generated explanatory videos. Some or all of the above processing in the distribution unit may be performed using generative AI, or it may be performed without using generative AI. For example, the distribution unit can input the explanatory video into the generative AI and have the generative AI select the optimal distribution method.

[0070] The reception desk can receive questions from users. The reception desk can receive questions from users in text format, for example. For example, the reception desk can receive questions in text format entered by the user. The reception desk can also receive questions from users in voice format, for example. For example, the reception desk can receive questions in voice format recorded by the user. Furthermore, the reception desk can analyze the content of the questions using a generative AI. For example, the generative AI takes the question from the user as input and analyzes the content of the question. This enables two-way interaction by receiving questions from users. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the question from the user into a generative AI and have the generative AI perform the analysis of the question content.

[0071] The answering unit can generate answers to received questions. The answering unit can generate answers using, for example, a generative AI. For example, the generative AI takes the received question as input and generates an answer. The answering unit can also adjust the way the answer is expressed using the generative AI. For example, the generative AI adjusts the tone and style of the answer. Furthermore, the answering unit can also adjust the level of detail of the answer using the generative AI. For example, the generative AI adjusts the level of detail of the answer according to the importance of the question. In this way, the user's doubts can be resolved by generating answers to received questions. Some or all of the above processing in the answering unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the answering unit can input the received question into a generative AI and have the generative AI perform the generation of the answer.

[0072] The generation unit can use a generation AI to set the voice, reading speed, and use of a VTuber to match the content and target audience. For example, the generation unit can use the generation AI to set the voice and reading speed. For example, the generation AI generates the voice for an explanatory video and sets an appropriate reading speed. The generation unit can also use the generation AI to use an appropriate VTuber. For example, the generation AI selects and uses a VTuber suitable for the explanatory video. This improves the viewing experience by setting the voice, reading speed, and use of a VTuber to match the viewer. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can have the generation AI perform the settings for the voice and VTuber of an explanatory video.

[0073] The distribution department can attract viewers from existing video distribution sites such as YouTube. For example, the distribution department can use existing video distribution sites such as YouTube to attract viewers. For example, the distribution department can upload explanatory videos to YouTube to attract viewers. The distribution department can also implement SEO measures to attract viewers from search engines. For example, the distribution department can promote viewers from search engines by including appropriate keywords in the titles and descriptions of explanatory videos. Furthermore, the distribution department can also attract viewers by conducting advertising campaigns. For example, the distribution department can use YouTube ads and social media ads to promote explanatory videos and attract viewers. In this way, the distribution department can increase its audience by attracting viewers from existing video distribution sites. Some or all of the above processes in the distribution department may be performed using, for example, generative AI, or not using generative AI. For example, the distribution department can have generative AI select the method of attracting viewers.

[0074] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using a generative AI. For example, the generative AI takes the user's facial expression data and voice data as input and estimates the emotions. The analysis unit can also adjust the depth of the analysis based on the estimated emotions of the user. For example, if the user is excited, a detailed analysis can be performed to promote a deeper understanding. If the user is tired, a concise analysis can be performed to provide only the essentials. Furthermore, if the user is relaxed, a balanced analysis can be performed to provide an appropriate amount of information. In this way, by adjusting the depth of the analysis according to the user's emotions, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the depth of the analysis.

[0075] The analysis unit can improve the accuracy of its analysis of business book content based on the author's intentions and background information. For example, the analysis unit can use a generative AI to analyze the content of business books. For example, the generative AI takes the text data of the business book as input and performs the analysis while considering the author's intentions and background information. The analysis unit can also refer to the author's past interviews and lectures to perform an analysis that reflects their intentions. For example, the generative AI takes the author's interviews and lectures as input and performs an analysis that reflects their intentions. Furthermore, the analysis unit can refer to the author's other works to perform a consistent analysis. For example, the generative AI takes the author's other works as input and performs a consistent analysis. This improves the accuracy of the analysis by considering the author's intentions and background information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the text data of the business book into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0076] The analysis unit can apply different analysis algorithms to each chapter of a business book during analysis. For example, the analysis unit can use a generative AI to apply different analysis algorithms to each chapter of a business book. For example, the generative AI selects an appropriate analysis algorithm according to the theme of each chapter. The analysis unit can also adjust the depth of analysis according to the complexity of the content of each chapter. For example, the generative AI takes the complexity of the content of each chapter as input and adjusts the depth of analysis. Furthermore, the analysis unit can combine different analysis methods according to the purpose of each chapter. For example, the generative AI performs analysis by combining different analysis methods according to the purpose of each chapter. This improves the accuracy of the analysis by applying an appropriate analysis algorithm to each chapter. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input the data of each chapter of a business book into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using a generative AI. For example, the generative AI takes the user's facial expression data and voice data as input and estimates the emotions. The analysis unit can also adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, a visually stimulating display method can be provided. If the user is tired, a simple and highly visible display method can be provided. Furthermore, if the user is relaxed, a balanced display method can be provided. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0078] The analysis unit can improve the accuracy of its analysis based on the publication year of the business book and the author's past works. For example, the analysis unit can analyze the content of the business book using a generative AI. For example, the generative AI takes the text data of the business book as input and performs the analysis considering the publication year and the author's past works. The analysis unit can also perform an analysis that takes the historical context into account based on the publication year of the business book. For example, the generative AI takes the publication year of the business book as input and performs an analysis that takes the historical context into account. Furthermore, the analysis unit can also refer to the author's past works to perform a consistent analysis. For example, the generative AI takes the author's past works as input and performs a consistent analysis. This improves the accuracy of the analysis by referring to the publication year and the author's past works. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the text data of the business book into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0079] The analysis unit can perform analysis by referring to other literature and materials related to the content of the business book. For example, the analysis unit can analyze the content of the business book using a generative AI. For example, the generative AI takes the text data of the business book as input and performs analysis by referring to related literature and materials. The analysis unit can also refer to academic papers related to the content of the business book to perform analysis that promotes a deeper understanding. For example, the generative AI takes related academic papers as input and performs analysis that promotes a deeper understanding. Furthermore, the analysis unit can also refer to other books related to the content of the business book to perform a consistent analysis. For example, the generative AI takes other related books as input and performs analysis that promotes a consistent analysis. This improves the depth of the analysis by referring to related literature and materials. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the text data of the business book into a generative AI and have the generative AI perform the referencing of related literature.

[0080] The generation unit can estimate the user's emotions and adjust the tone and style of the explanatory video based on the estimated emotions. For example, the generation unit estimates the user's emotions using a generation AI. For example, the generation AI takes the user's facial expression data and voice data as input and estimates the emotions. The generation unit can also adjust the tone and style of the explanatory video based on the estimated emotions of the user. For example, if the user is relaxed, the explanatory video can be generated in a relaxed tone. If the user is in a hurry, the explanatory video can be generated in a quick and concise tone. Furthermore, if the user is excited, the explanatory video can be generated in a visually stimulating style. In this way, the viewing experience is improved by adjusting the tone and style of the explanatory video according to the user's emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the tone and style adjustments.

[0081] The generation unit can add visual effects to highlight key points of a business book when generating explanatory videos. For example, the generation unit can generate explanatory videos using a generation AI. For instance, the generation AI takes key points from a business book as input and generates an explanatory video. The generation unit can also add visual effects using the generation AI. For example, the generation AI can add text highlighting to emphasize key points. Furthermore, the generation unit can add graphs and charts using the generation AI. For example, the generation AI generates graphs and charts to visually represent key points. Additionally, the generation unit can add animation effects using the generation AI. For example, the generation AI generates animation effects to highlight key points. This visually emphasizes key points, enhancing viewer understanding. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can have the generation AI perform the addition of visual effects.

[0082] The generation unit can add interactive elements based on the content of a business book when generating explanatory videos. For example, the generation unit can generate explanatory videos using a generation AI. For example, the generation AI takes the content of a business book as input and generates an explanatory video. The generation unit can also add interactive elements using the generation AI. For example, the generation AI can add an interactive element that allows the user to answer questions in the video. Furthermore, the generation unit can add an interactive element that allows the user to select options using the generation AI. For example, the generation AI generates an interactive element that allows the user to select options in the video. Furthermore, the generation unit can add an interactive element that allows the user to provide feedback using the generation AI. For example, the generation AI generates an interactive element that allows the user to provide feedback in the video. By adding interactive elements, the desire to participate in the viewer's activities increases. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have the generation AI perform the addition of interactive elements.

[0083] The generation unit can estimate the user's emotions and adjust the length of the explanatory video based on the estimated emotions. For example, the generation unit estimates the user's emotions using a generation AI. For example, the generation AI takes the user's facial expression data and voice data as input and estimates the emotions. The generation unit can also adjust the length of the explanatory video based on the estimated emotions of the user. For example, if the user is in a hurry, it can generate a short, concise explanatory video. If the user is relaxed, it can generate a longer explanatory video with detailed explanations. Furthermore, if the user is excited, it can generate an explanatory video with visually stimulating effects. By adjusting the length of the explanatory video according to the user's emotions, viewer satisfaction is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the explanatory video.

[0084] The generation unit can add examples and case studies related to the content of the business book when generating explanatory videos. The generation unit can generate explanatory videos using, for example, a generation AI. For example, the generation AI takes the content of the business book as input and generates an explanatory video. The generation unit can also add examples and case studies using the generation AI. For example, the generation AI adds examples related to the content of the business book to the explanatory video. Furthermore, the generation unit can also add case studies using the generation AI. For example, the generation AI adds case studies related to the content of the business book to the explanatory video. Furthermore, the generation unit can also add success stories using the generation AI. For example, the generation AI adds success stories related to the content of the business book to the explanatory video. In this way, adding examples and case studies deepens the viewer's understanding. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have the generation AI perform the addition of examples and case studies.

[0085] The generation unit can incorporate quizzes and tests based on the content of business books when generating explanatory videos. For example, the generation unit generates explanatory videos using a generation AI. For example, the generation AI takes the content of a business book as input and generates an explanatory video. The generation unit can also incorporate quizzes and tests using the generation AI. For example, the generation AI incorporates a quiz into the explanatory video to check understanding based on the content of the business book. Furthermore, the generation unit can incorporate tests to enhance learning effectiveness using the generation AI. For example, the generation AI incorporates a test into the explanatory video based on the content of the business book. Furthermore, the generation unit can incorporate review quizzes using the generation AI. For example, the generation AI incorporates a review quiz into the explanatory video based on the content of the business book. By incorporating quizzes and tests, the viewer's understanding can be checked, and the learning effect can be enhanced. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have the generation AI perform the incorporation of quizzes and tests.

[0086] The distribution unit can estimate the user's emotions and adjust the distribution timing based on the estimated emotions. For example, the distribution unit can estimate the user's emotions using generative AI. For example, the generative AI takes the user's facial expression data and voice data as input and estimates the emotions. The distribution unit can also adjust the distribution timing based on the estimated emotions of the user. For example, if the user is relaxed, distribution can be done at night. If the user is busy, distribution can be done during lunch breaks or commute times. Furthermore, if the user is excited, distribution can be done in real time. By adjusting the distribution timing according to the user's emotions, viewer satisfaction can be improved. Some or all of the above processing in the distribution unit may be done using generative AI, for example, or without using generative AI. For example, the distribution unit can input user emotion data into the generative AI and have the generative AI adjust the distribution timing.

[0087] The distribution unit can select a distribution method by referring to the user's past viewing history at the time of distribution. For example, the distribution unit can refer to the user's past viewing history using a generative AI. For example, the generative AI takes the user's past viewing history as input and selects the optimal distribution method. The distribution unit can also select the optimal distribution method by analyzing the trends of videos the user has watched in the past. For example, the generative AI takes the trends of videos the user has watched in the past as input and selects the optimal distribution method. Furthermore, the distribution unit can also select the optimal distribution timing by referring to the time of day when the user has watched videos in the past. For example, the generative AI takes the time of day when the user has watched videos in the past as input and selects the optimal distribution timing. In this way, by referring to past viewing history, the distribution unit can provide viewers with the most suitable distribution method. Some or all of the above processing in the distribution unit may be performed using a generative AI, for example, or without using a generative AI. For example, the distribution unit can input the user's viewing history into a generative AI and have the generative AI select the distribution method.

[0088] The distribution unit can select a distribution format based on the user's device information during distribution. For example, the distribution unit may use a generative AI to access the user's device information. For example, the generative AI may take the user's device information as input and select the optimal distribution format. Furthermore, if the user is using a smartphone, the distribution unit can select a mobile-optimized distribution format. For example, the generative AI may take the input that the user is using a smartphone and select a mobile-optimized distribution format. In addition, if the user is using a tablet, the distribution unit can select a large-screen-optimized distribution format. For example, the generative AI may take the input that the user is using a tablet and select a large-screen-optimized distribution format. This allows the distribution unit to provide the optimal distribution format to viewers by considering device information. Some or all of the above processing in the distribution unit may be performed using a generative AI, or without using a generative AI. For example, the distribution unit can input the user's device information into a generative AI and have the generative AI select the distribution format.

[0089] The distribution unit can estimate the user's emotions and determine the priority of the content to be distributed based on the estimated emotions. The distribution unit can estimate the user's emotions using, for example, generative AI. For example, the generative AI takes the user's facial expression data and voice data as input and estimates the emotions. The distribution unit can also determine the priority of the content to be distributed based on the estimated emotions of the user. For example, if the user is excited, visually stimulating content can be distributed preferentially. If the user is tired, relaxing content can be distributed preferentially. Furthermore, if the user is relaxed, balanced content can be distributed preferentially. By determining the priority of the content to be distributed according to the user's emotions, viewer satisfaction can be improved. Some or all of the above processing in the distribution unit may be performed using, for example, generative AI, or without using generative AI. For example, the distribution unit can input user emotion data into the generative AI and have the generative AI determine the priority of the content to be distributed.

[0090] The distribution unit can prioritize the delivery of highly relevant content based on the user's geographical location information during distribution. For example, the distribution unit can use a generative AI to refer to the user's geographical location information. For example, the generative AI takes the user's geographical location information as input and selects highly relevant content. Furthermore, if the user is in a specific region, the distribution unit can prioritize the delivery of content related to that region. For example, the generative AI takes the user's location as input and selects content related to that region. In addition, if the user is traveling, the distribution unit can prioritize the delivery of content related to their travel destination. For example, the generative AI takes the user's travel status as input and selects content related to their travel destination. In this way, by considering geographical location information, highly relevant content can be provided to viewers. Some or all of the above processing in the distribution unit may be performed using a generative AI, for example, or without using a generative AI. For example, the distribution unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant content.

[0091] The distribution unit can deliver relevant content based on the user's social media activity at the time of distribution. The distribution unit can analyze the user's social media activity using, for example, generative AI. For example, the generative AI takes the user's social media activity as input and selects relevant content. The distribution unit can also deliver content related to topics the user has shown interest in on social media. For example, the generative AI takes the topics the user has shown interest in on social media as input and selects relevant content. Furthermore, the distribution unit can deliver content related to accounts the user follows on social media. For example, the generative AI takes the accounts the user follows as input and selects relevant content. In this way, by analyzing social media activity, the distribution unit can provide viewers with highly relevant content. Some or all of the above processing in the distribution unit may be performed using, for example, generative AI, or without generative AI. For example, the distribution unit can input the user's social media activity into the generative AI and have the generative AI select relevant content.

[0092] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, the reception unit can estimate the user's emotions using generative AI. For example, the generative AI takes the user's facial expression data or voice data as input and estimates the emotions. The reception unit can also adjust the question reception method based on the estimated emotions of the user. For example, if the user is nervous, it can provide a simple and intuitive question reception method. If the user is relaxed, it can also provide a detailed question reception method. Furthermore, if the user is in a hurry, it can prioritize voice input and quickly receive questions. In this way, by adjusting the question reception method according to the user's emotions, the question reception becomes smoother. Some or all of the above processing in the reception unit may be performed using generative AI, for example, or without generative AI. For example, the reception unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the question reception method.

[0093] The reception unit can select a reception method based on the user's past question history when a question is received. For example, the reception unit can refer to the user's past question history using a generative AI. For example, the generative AI takes the user's past question history as input and selects the most suitable reception method. The reception unit can also automatically display topics that the user has frequently asked about in the past as suggestions. For example, the generative AI takes topics that the user has frequently asked about in the past as input and displays them as suggestions. Furthermore, the reception unit can also prioritize suggesting question methods (voice, text, etc.) that the user has used in the past. For example, the generative AI takes question methods that the user has used in the past as input and suggests them preferentially. In this way, by referring to the past question history, the reception unit can provide the user with the most suitable question reception method. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's question history into a generative AI and have the generative AI select the reception method.

[0094] The reception unit can filter questions based on the user's current areas of interest when they are received. For example, the reception unit can use a generative AI to identify the user's current areas of interest. For example, the generative AI can take the user's search history and viewing history as input to identify the current areas of interest. The reception unit can also prioritize receiving questions related to topics the user is currently interested in. For example, the generative AI can take the topics the user is currently interested in as input and prioritize receiving related questions. Furthermore, the reception unit can also prioritize receiving questions related to keywords the user has recently searched for. For example, the generative AI can take the keywords the user has recently searched for as input and prioritize receiving related questions. This allows the reception unit to prioritize receiving highly relevant questions by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's areas of interest into a generative AI and have the generative AI perform the filtering.

[0095] The reception desk can estimate the user's emotions and determine the priority of questions based on the estimated emotions. The reception desk can estimate the user's emotions using, for example, generative AI. For example, the generative AI takes the user's facial expression data or voice data as input and estimates the emotions. The reception desk can also determine the priority of questions based on the estimated emotions of the user. For example, if the user is excited, urgent questions can be processed first. If the user is tired, simple questions can be processed first. Furthermore, if the user is relaxed, balanced questions can be processed first. In this way, important questions can be processed first by determining the priority of questions according to the user's emotions. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's emotion data into the generative AI and have the generative AI perform the determination of question priorities.

[0096] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, the reception desk can use a generative AI to refer to the user's geographical location. For example, the generative AI takes the user's geographical location as input and selects highly relevant questions. Furthermore, if the user is in a specific region, the reception desk can prioritize receiving questions related to that region. For example, the generative AI takes the user's location as input and selects questions related to that region. In addition, if the user is traveling, the reception desk can prioritize receiving questions related to their travel destination. For example, the generative AI takes the user's travel status as input and selects questions related to their travel destination. In this way, by considering geographical location, highly relevant questions can be prioritized. Some or all of the above processing in the reception desk may be performed using a generative AI, or without one. For example, the reception desk can input the user's geographical location into a generative AI and have the generative AI select highly relevant questions.

[0097] The reception desk can accept questions based on the user's social media activity when a question is received. The reception desk can analyze the user's social media activity using, for example, generative AI. For example, the generative AI takes the user's social media activity as input and selects relevant questions. The reception desk can also accept questions related to topics the user has shown interest in on social media. For example, the generative AI takes the topics the user has shown interest in on social media as input and selects relevant questions. Furthermore, the reception desk can accept questions related to accounts the user follows on social media. For example, the generative AI takes the accounts the user follows as input and selects relevant questions. In this way, by analyzing social media activity, the reception desk can accept highly relevant questions. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's social media activity into the generative AI and have the generative AI select relevant questions.

[0098] The response unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, the response unit can estimate the user's emotions using a generative AI. For example, the generative AI takes the user's facial expression data or voice data as input and estimates the emotions. The response unit can also adjust the way it expresses its response based on the estimated user emotions. For example, if the user is nervous, it can generate a response in a calm tone. If the user is relaxed, it can generate a response in a bright tone. Furthermore, if the user is in a hurry, it can generate a quick and concise response. In this way, by adjusting the way the response is expressed according to the user's emotions, a more appropriate response can be provided. Some or all of the above processing in the response unit may be performed using a generative AI, for example, or without a generative AI. For example, the response unit can input the user's emotion data into a generative AI and have the generative AI adjust the way the response is expressed.

[0099] The answering unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the answering unit can use a generation AI to evaluate the importance of the question. For example, the generation AI takes the content of the question as input and evaluates its importance. The answering unit can also adjust the level of detail in the answer based on the importance of the question. For example, it can generate a detailed answer for important questions. It can also generate a concise answer for general questions. Furthermore, it can generate a quick answer for urgent questions. In this way, by adjusting the level of detail in the answer based on the importance of the question, an appropriate answer can be provided. Some or all of the above processing in the answering unit may be performed using a generation AI, for example, or without a generation AI. For example, the answering unit can input the importance of the question into the generation AI and have the generation AI perform the adjustment of the level of detail in the answer.

[0100] The answering unit can apply different answering algorithms depending on the question category when generating answers. For example, the answering unit can use a generative AI to identify the question category. For example, the generative AI takes the question content as input and identifies the category. The answering unit can also apply different answering algorithms depending on the question category. For example, a specialized answering algorithm can be applied to technical questions. A concise answering algorithm can also be applied to general questions. Furthermore, a rapid answering algorithm can be applied to urgent questions. By applying the appropriate answering algorithm according to the question category, more accurate answers can be provided. Some or all of the above processing in the answering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the answering unit can input the question category into a generative AI and have the generative AI perform the application of the answering algorithm.

[0101] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, the response unit can estimate the user's emotions using generative AI. For example, the generative AI takes the user's facial expression data and voice data as input and estimates the emotions. The response unit can also adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, it can generate a short, to-the-point response. If the user is relaxed, it can generate a longer response that includes detailed explanations. Furthermore, if the user is excited, it can generate a response with visually stimulating effects. By adjusting the length of the response according to the user's emotions, viewer satisfaction is improved. Some or all of the above processing in the response unit may be performed using generative AI, for example, or without generative AI. For example, the response unit can input user emotion data into the generative AI and have the generative AI adjust the length of the response.

[0102] The answering unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the answering unit can use a generation AI to evaluate the submission timing of questions. For example, the generation AI takes the date and time of question submission as input and determines the priority. The answering unit can also determine the priority of answers based on when the questions were submitted. For example, it can generate answers quickly for urgent questions. For general questions, it can generate answers with normal priority. Furthermore, it can generate detailed answers for important questions. This allows for the rapid provision of answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the answering unit may be performed using a generation AI, for example, or without a generation AI. For example, the answering unit can input the submission timing of questions into the generation AI and have the generation AI determine the priority of answers.

[0103] The answering unit can adjust the order of answers based on the relevance of the questions when generating answers. The answering unit can evaluate the relevance of questions using, for example, a generation AI. For example, the generation AI takes the content of the questions as input and evaluates the relevance. The answering unit can also adjust the order of answers based on the relevance of the questions. For example, it can generate answers first for important questions. For general questions, it can also generate answers in the usual order. Furthermore, it can generate answers quickly for urgent questions. In this way, by adjusting the order of answers based on the relevance of the questions, answers can be provided quickly for important questions. Some or all of the above processing in the answering unit may be performed using, for example, a generation AI, or without a generation AI. For example, the answering unit can input the relevance of the questions into the generation AI and have the generation AI perform the adjustment of the order of answers. === Hard Collateral 1-1 === Each of the multiple elements described above, including the analysis unit, generation unit, distribution unit, reception unit, and response unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The distribution unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reception unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The response unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the analysis unit, generation unit, distribution unit, reception unit, and response unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The distribution unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The reception unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The response unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the analysis unit, generation unit, distribution unit, reception unit, and response unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The distribution unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The reception unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The response unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the analysis unit, generation unit, distribution unit, reception unit, and response unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The distribution unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The reception unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The response unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can adjust the depth of analysis when analyzing the content of business books by referring to the user's past learning history. For example, based on what the user has learned in the past, it can analyze parts that the user already understands concisely and parts that are not well understood in detail. The analysis unit can also adjust the speed of the analysis according to the user's learning pace. For example, if the user is learning at a fast pace, the analysis can proceed quickly, and if the user is learning slowly, detailed explanations can be added. Furthermore, the analysis unit can customize the analysis method according to the user's learning style. For example, it can make extensive use of graphs and charts for visual learners and emphasize audio explanations for auditory learners.

[0106] The generation unit can customize the content of explanatory videos by referring to the user's past viewing history. For example, it can highlight relevant topics based on the content of videos the user has watched in the past. The generation unit can also adjust the length of the video based on the user's viewing history. For example, if the user prefers short videos, it can generate a short video that gets straight to the point, and if the user prefers longer videos, it can generate a video with detailed explanations. Furthermore, the generation unit can adjust the style of the video based on the user's viewing history. For example, if the user prefers animation, it can generate a video that uses a lot of animation, and if the user prefers live-action, it can generate a video that includes a lot of live-action footage.

[0107] The distribution team can prioritize the delivery of explanatory videos on business books relevant to a user's region based on their geographical location. For example, if a user is in a specific region, the distribution team can deliver explanatory videos on books related to the business environment and culture of that region. The distribution team can also provide information on local events and seminars within the videos based on the user's geographical location. For example, if a user is in a specific region, the distribution team can introduce information on business seminars and events held in that region within the videos. Furthermore, the distribution team can cover local news and topics within the videos based on the user's geographical location. For example, if a user is in a specific region, the distribution team can explain the latest news and topics from that region within the videos.

[0108] The reception desk can prioritize questions based on the user's social media activity. For example, it can prioritize questions related to topics the user has shown interest in on social media. It can also prioritize questions related to accounts the user follows on social media. For example, it can prioritize questions related to business influencers the user follows. Furthermore, the reception desk can adjust the priority of questions based on the user's frequency of social media activity. For example, it can prioritize questions from users who are actively using social media.

[0109] The answer function can adjust the level of detail in its answers based on the user's past question history. For example, if the user has previously requested detailed answers, it can generate detailed answers; if they have requested concise answers, it can generate concise answers. The answer function can also provide additional relevant information based on the user's past question history. For example, it can provide new information or updates related to topics the user has previously asked about. Furthermore, the answer function can adjust the format of its answers based on the user's past question history. For example, if the user has previously preferred text-based answers, it can provide text-based answers; if they have preferred audio-based answers, it can provide audio-based answers.

[0110] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is excited, a detailed analysis can be performed to facilitate a deeper understanding. If the user is tired, a concise analysis can be performed to provide only the essential points. Furthermore, if the user is relaxed, a balanced analysis can be performed to provide an appropriate amount of information. By adjusting the depth of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0111] The generation unit can estimate the user's emotions and adjust the tone and style of the explanatory video based on those emotions. For example, if the user is relaxed, it can generate an explanatory video in a calm tone. If the user is in a hurry, it can generate an explanatory video in a quick and concise tone. Furthermore, if the user is excited, it can generate an explanatory video in a visually stimulating style. By adjusting the tone and style of the explanatory video according to the user's emotions, the viewing experience is improved.

[0112] The broadcasting unit can estimate the user's emotions and adjust the broadcasting timing based on those estimates. For example, if the user is relaxed, broadcasts can be made at night. If the user is busy, broadcasts can be made during lunch breaks or commutes. Furthermore, if the user is excited, broadcasts can be made in real time. By adjusting the broadcasting timing according to the user's emotions, viewer satisfaction can be improved.

[0113] The reception desk can estimate the user's emotions and adjust the question-taking method based on that estimation. For example, if the user is nervous, it can provide a simple and intuitive question-taking method. If the user is relaxed, it can provide a more detailed question-taking method. Furthermore, if the user is in a hurry, it can prioritize voice input and take questions quickly. This allows for a smoother question-taking process by adjusting the question-taking method according to the user's emotions.

[0114] The response section can estimate the user's emotions and adjust the length of the response based on that estimation. For example, if the user is in a hurry, it can generate a short, to-the-point response. If the user is relaxed, it can generate a longer response with detailed explanations. Furthermore, if the user is excited, it can generate a response with visually stimulating effects. By adjusting the response length according to the user's emotions, viewer satisfaction is improved.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis unit analyzes the content of the business book. Business books include, but are not limited to, management books, marketing books, and self-help books. The analysis unit uses text mining technology, natural language processing technology, and generative AI to analyze the content of the business book and extract key points. Step 2: The generation unit generates an explanatory video based on the analysis performed by the analysis unit. The explanatory video includes, but is not limited to, the video length and the media format used. The generation unit can also generate the explanatory video using a generation AI, set the voice and reading speed, and use an appropriate VTuber. Step 3: The distribution unit delivers the explanatory videos generated by the generation unit to users. Distribution includes, but is not limited to, the distribution platform and format. The distribution unit can also distribute the explanatory videos on existing video distribution sites such as YouTube or on its own website, and optimize the content using generation AI. Step 4: The reception desk receives questions from users regarding the explanatory videos distributed by the distribution desk. Questions may be in text format, audio format, or other formats. The reception desk can accept questions from users in text format or audio format and can also analyze the content of the questions using generative AI. Step 5: The response unit generates answers to the questions received by the reception unit. These answers may be in text format, audio format, or other formats. The response unit can generate answers using a generation AI and can also adjust the wording and level of detail of the answers.

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 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.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the content of business books, A generation unit generates an explanatory video based on the content analyzed by the analysis unit, A distribution unit that distributes the explanatory video generated by the generation unit, A reception unit that receives questions from users regarding the explanatory videos distributed by the aforementioned distribution unit, The system includes a response unit that generates answers to questions received by the reception unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the content of business books and extract key points. The system according to feature 1.

3. The generating unit is Generate explanatory videos based on the key points extracted. The system according to feature 1.

4. The aforementioned distribution unit, The generated explanatory video is distributed to the user. The system according to feature 1.

5. The aforementioned reception unit is We accept questions from users. The system according to feature 1.

6. The aforementioned response section is, Generate an answer to the received question. The system according to feature 1.

7. The generating unit is The AI ​​generates and sets the voice, reading speed, and use of VTubers to match the content and target audience. The system according to feature 1.

8. The aforementioned distribution unit, Attract customers through video streaming sites. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and adjusts the depth of the analysis based on the estimated user emotions. The system according to feature 1.

Images