System

Abstract

An object of the system according to the embodiment is to efficiently perform generation, peer review, and publication of lecture content.SOLUTION: A system includes a lecture generation part, a peer review part, and a publishing part. The lecture generation unit includes a generation AI. The peer review unit peer-reviews the lecture content generated by the lecture generation unit. The publishing department publishes the lecture content peer-reviewed by the peer-reviewing department as a scientific magazine.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the processes of generating, peer-reviewing, and publishing lecture content required time and effort, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently carry out the processes from the generation of lecture content to peer review and publication. [Means for solving the problem]

[0006] The system according to the embodiment includes a lecture generation unit, a peer review unit, and a publishing unit. The lecture generation unit is equipped with a generation AI. The peer review unit peer-reviews the lecture content generated by the lecture generation unit. The publishing unit publishes the lecture content peer-reviewed by the peer review unit as a scientific journal. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out the processes from generating lecture content to peer review and publication. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The academic content generation system according to an embodiment of the present invention is a system that entrusts all lectures and content supporting learning to a generation AI, ultimately forming a huge academic society. This system provides efficient, high-quality academic content by having the generation AI generate lecture content and also perform peer review. This allows the academic content generation system to provide efficient, high-quality academic content, aiming to advance the academic community as a whole.

[0029] An academic content generation system according to an embodiment includes a lecture generation unit, a peer review unit, and a publishing unit. The lecture generation unit is equipped with a generation AI and generates lecture content tailored to the learner's needs and academic field. For example, the generation AI creates lecture materials covering everything from basic concepts to applications of physics for a learner who requests a physics lecture. The generation AI receives input from prompts containing instructions about the learner's desired lecture content, and the generation AI generates lecture content based on the prompts. The peer review unit peer-reviews the lecture content generated by the lecture generation unit. For example, the generation AI peer-reviews academic papers and research results to evaluate the accuracy and novelty of their content. The generation AI receives input from the papers and research results to be peer-reviewed, and generates peer-review results based on the content. The publishing unit publishes the lecture content peer-reviewed by the peer review unit as a scientific journal. For example, it publishes a special issue compiling the lecture content generated by the generation AI and provides it to the academic community. Furthermore, by publishing papers that have been evaluated through peer review, reliable academic information is provided. As a result, the academic content generation system according to the embodiment can provide efficient and high-quality academic content, aiming to contribute to the development of the academic world as a whole.

[0030] The lecture generation unit can provide personalized lecture materials that reflect a learner's past learning history and interests. For example, the generation AI analyzes a learner's past learning history and generates lecture materials that reflect their level of understanding and interests in a specific field. For example, a learner who already understands the basics of physics is provided with lecture materials that include applied content. Furthermore, the lecture generation unit generates related lecture content based on topics in which the learner has previously shown interest. For example, a learner who is interested in quantum mechanics is provided with lecture materials that include the latest research and applications of quantum mechanics. Furthermore, the lecture generation unit creates personalized learning plans based on the learner's learning history and generates lecture content based on those plans. For example, a learner who has a low level of understanding in a specific field is provided with supplementary materials specialized for that field. This allows the learner to receive personalized lecture materials, thereby improving their learning effectiveness.

[0031] The lecture generation unit can provide lecture content that reflects the latest research results and trends in real time. For example, the lecture generation unit uses a generation AI to automatically collect the latest research papers and academic articles and reflect their contents in the lecture content. For example, the lecture generation unit provides lecture materials including the latest physics research results. The lecture generation unit also uses a generation AI to regularly scan academic databases and incorporate newly published research results and trends into the lecture content. For example, the lecture generation unit provides lecture materials including the latest technological and market trends. The lecture generation unit also uses a generation AI to analyze academic news and trend information that is updated in real time and reflects that information in the lecture content. For example, the lecture generation unit provides lecture materials including the latest science and technology news. This allows learners' knowledge to be kept up to date by always providing the latest information.

[0032] The lecture generation unit can provide lecture content that corresponds to different languages ​​and cultures. For example, the lecture generation unit constructs a system in which a generation AI automatically translates lecture content and provides it in different languages. For example, it provides lecture materials in multiple languages ​​such as English, Japanese, and French. The lecture generation unit also generates lecture content that takes cultural context into account to accommodate learners with different cultural backgrounds. For example, it provides lecture materials that include culturally appropriate examples and case studies. The lecture generation unit also provides a multilingual interface using the generation AI, allowing learners to receive lecture content in their own language. For example, it adds multilingual subtitles to lecture videos. This makes it possible to accommodate global learners and broaden the scope of learning.

[0033] The lecture generation unit can provide lecture content in a multimedia format including visuals and audio. For example, the lecture generation unit uses a generation AI to generate lecture content in a multimedia format including visuals and audio and provide it to learners. For example, it adds charts, graphs, and audio commentary to lecture materials. The lecture generation unit also uses a generation AI to create video lectures and interactive simulations, allowing learners to learn through their eyes and ears. For example, it provides lecture materials that show experimental procedures in video. The lecture generation unit also uses a generation AI to generate content that provides audio commentary on the lecture content using speech recognition technology. For example, it provides a function to read lecture materials aloud. This can improve learning effectiveness through sight and hearing.

[0034] The peer review department can learn from past peer review history and evaluation criteria to provide more consistent peer review results. For example, the generation AI learns from past peer review history and provides consistent peer review results based on the evaluation criteria. For example, the same evaluation criteria are used to peer review multiple papers. The peer review department also analyzes past peer review data and builds a system in which the generation AI automatically adjusts the evaluation criteria. For example, it adjusts the weighting of the evaluation criteria to maintain consistency. The peer review department also learns evaluation criteria for specific fields or themes based on past peer review history to provide more accurate peer review results. For example, it uses evaluation criteria specialized for a specific research field. This can improve the reliability of the peer review process by providing consistent peer review results.

[0035] The peer review department can analyze the citation relationships and influence of papers to evaluate their importance. For example, the peer review department could build a system in which a generation AI analyzes a paper's citation relationships and evaluates its influence. For example, the importance of a paper could be evaluated based on the number of citations and the number of times it has been cited. The peer review department could also analyze a paper's citation network and have a generation AI evaluate its influence. For example, it could identify important papers and papers by influential researchers. The peer review department could also develop a system in which a generation AI evaluates the influence of an entire research field based on the citation relationships of a paper. For example, it could identify important research results in a specific topic. This could increase the academic value of a paper by evaluating its importance.

[0036] The peer review department can also analyze the visuals and datasets of a paper and provide a comprehensive evaluation. For example, the generative AI analyzes the visual data and datasets of a paper, and provides a comprehensive review result based on the results. For example, it evaluates the accuracy of graphs and figures. The peer review department can also analyze the datasets included in a paper and build a system for the generative AI to evaluate the reliability and reproducibility of the data. For example, it evaluates the consistency and statistical significance of the data. The peer review department can also evaluate the overall quality of a paper based on the visual data and datasets of the generative AI. For example, it evaluates the interpretation of the visual data and how the datasets are used. This comprehensive evaluation can improve the quality of the paper.

[0037] The publishing department can feature the latest research topics and trends to attract readers' attention. For example, the publishing department can use generative AI to automatically collect the latest research topics and trends and publish them as feature articles in scientific journals. For example, they can feature the latest research results in AI technology or biotechnology. The publishing department can also use generative AI to scan academic databases and compile newly published research topics and trends as feature articles. For example, they can feature the latest developments in a specific research field. The publishing department can also use generative AI to analyze academic news and trend information that is updated in real time and generate feature articles based on that information. For example, they can feature the latest science and technology news. This can attract readers' attention and increase the value of scientific journals.

[0038] Publishers can incorporate interactive elements to provide content in which readers can participate. For example, publishers can use generative AI to incorporate interactive quizzes and surveys into science magazines to provide content in which readers can participate. For example, they can publish quizzes related to feature articles. Publishers can also incorporate interactive simulations and experiments into science magazines to provide content that readers can actually try. For example, they can publish simulations of physics experiments. Publishers can also use generative AI to collect reader feedback in real time and provide interactive content based on the results. For example, they can publish feature articles that reflect reader opinions. This can increase reader engagement by providing content in which readers can participate.

[0039] Publishers can provide scientific journals in different media formats (e.g., e-books, podcasts) to broaden reader access. For example, the publisher may use generative AI to provide the content of a scientific journal in e-book format, allowing readers to access it on digital devices. For example, the publisher may distribute the scientific journal in PDF or ePub format. The publisher may also provide the content of a scientific journal in podcast format, allowing readers to access academic information audio-wise. For example, the publisher may distribute a podcast that provides an audio explanation of a feature article. The publisher may also use generative AI to provide the content of a scientific journal in video format, allowing readers to access academic information visually. For example, the publisher may distribute content that explains a feature article in video. This allows the publisher to broaden reader access by providing the content in different media formats.

[0040] The publishing department can publish columns by researchers and experts from different fields to provide an interdisciplinary perspective. For example, the publishing department uses a generative AI to collect the opinions of researchers and experts from different fields and publish the content as a column in a scientific journal. For example, the publishing department could publish a conversation between an AI researcher and a biologist. The publishing department could also build a system to regularly publish columns by experts from different fields to provide an interdisciplinary perspective. For example, each issue could feature a column by an expert from a different field. The publishing department could also use a generative AI to analyze the opinions of researchers and experts from different fields and generate columns based on that content. For example, the publishing department could publish a feature article that incorporates perspectives from different fields. This can broaden the reader's knowledge by providing an interdisciplinary perspective.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The lecture generation unit can provide customized lecture content according to the learner's learning style. For example, visual learners can be provided with lecture materials that make extensive use of charts and visual aids. Auditory learners can be provided with lecture materials in the form of audio commentary or podcasts. Furthermore, hands-on learners can be provided with lecture materials that include interactive simulations and experiments. This allows for an optimal learning experience according to each learner's learning style.

[0043] The lecture generation unit can collect learner feedback and continuously improve the lecture content based on the results. For example, it can analyze feedback provided by learners on the lecture content and identify areas for improvement. It can also add new topics and approaches to the lecture content based on learner feedback. It can also reflect learner feedback in real time and instantly adjust the lecture content. This makes it possible to provide high-quality lecture content that meets learner needs.

[0044] The lecture generation unit can incorporate gamification elements into lecture content to attract learners' interest. For example, learners can earn points each time they complete a specific task and compete for rankings. Learners can also earn badges and titles according to the goals they achieve. Furthermore, it can provide team-based activities in which learners collaborate with other learners to solve tasks. This increases learners' motivation and makes learning more enjoyable.

[0045] The lecture generation unit can provide content for the learner to review what they have learned in the past based on their learning history. For example, it can provide quizzes and practice questions related to topics the learner has previously studied. It can also provide summary materials for the learner to review what they have previously learned. Furthermore, it can automatically identify topics that need to be reviewed based on the learner's learning history and provide review content at the appropriate time. This can strengthen the learner's memory and improve learning effectiveness.

[0046] The lecture generation unit can provide customized lecture content according to the learner's learning environment. For example, it can provide interactive lecture materials suitable for an online learning environment. It can also provide lecture materials including group discussions and experiments suitable for face-to-face classes. Furthermore, to accommodate hybrid learning environments, it can provide lecture content that can be used both online and face-to-face. This makes it possible to provide an optimal learning experience according to the learner's learning environment.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The lecture generation unit is equipped with a generation AI and generates lecture content according to the learner's needs and academic field. For example, for a learner who wants a physics lecture, the generation AI creates lecture materials covering everything from basic concepts to applications of physics. The input to the generation AI is a prompt containing instructions on the learner's desired lecture content, and the generation AI generates the lecture content based on that prompt. Step 2: The peer review unit reviews the lecture content generated by the lecture generation unit. For example, the generation AI peer reviews academic papers and research results, evaluating the accuracy and novelty of their content. The input to the generation AI is the paper or research result to be peer-reviewed, and the generation AI generates peer review results based on that content. Step 3: The Publishing Department publishes the lecture content that has been peer-reviewed by the Peer Review Department as a scientific journal. For example, it may publish a special issue compiling the lecture content generated by the generative AI and provide it to the academic community. It also publishes papers that have been evaluated through peer review, providing reliable academic information.

[0049] (Example 2) The academic content generation system according to an embodiment of the present invention is a system that entrusts all lectures and content supporting learning to a generation AI, ultimately forming a huge academic society. This system provides efficient, high-quality academic content by having the generation AI generate lecture content and also perform peer review. This allows the academic content generation system to provide efficient, high-quality academic content, aiming to advance the academic community as a whole.

[0050] An academic content generation system according to an embodiment includes a lecture generation unit, a peer review unit, and a publishing unit. The lecture generation unit is equipped with a generation AI and generates lecture content tailored to the learner's needs and academic field. For example, the generation AI creates lecture materials covering everything from basic concepts to applications of physics for a learner who requests a physics lecture. The generation AI receives input from prompts containing instructions about the learner's desired lecture content, and the generation AI generates lecture content based on the prompts. The peer review unit peer-reviews the lecture content generated by the lecture generation unit. For example, the generation AI peer-reviews academic papers and research results to evaluate the accuracy and novelty of their content. The generation AI receives input from the papers and research results to be peer-reviewed, and generates peer-review results based on the content. The publishing unit publishes the lecture content peer-reviewed by the peer review unit as a scientific journal. For example, it publishes a special issue compiling the lecture content generated by the generation AI and provides it to the academic community. Furthermore, by publishing papers that have been evaluated through peer review, reliable academic information is provided. As a result, the academic content generation system according to the embodiment can provide efficient and high-quality academic content, aiming to contribute to the development of the academic world as a whole.

[0051] The lecture generation unit can provide personalized lecture materials that reflect a learner's past learning history and interests. For example, the generation AI analyzes a learner's past learning history and generates lecture materials that reflect their level of understanding and interests in a specific field. For example, a learner who already understands the basics of physics is provided with lecture materials that include applied content. Furthermore, the lecture generation unit generates related lecture content based on topics in which the learner has previously shown interest. For example, a learner who is interested in quantum mechanics is provided with lecture materials that include the latest research and applications of quantum mechanics. Furthermore, the lecture generation unit creates personalized learning plans based on the learner's learning history and generates lecture content based on those plans. For example, a learner who has a low level of understanding in a specific field is provided with supplementary materials specialized for that field. This allows the learner to receive personalized lecture materials, thereby improving their learning effectiveness.

[0052] The lecture generation unit can provide lecture content that reflects the latest research results and trends in real time. For example, the lecture generation unit uses a generation AI to automatically collect the latest research papers and academic articles and reflect their contents in the lecture content. For example, the lecture generation unit provides lecture materials including the latest physics research results. The lecture generation unit also uses a generation AI to regularly scan academic databases and incorporate newly published research results and trends into the lecture content. For example, the lecture generation unit provides lecture materials including the latest technological and market trends. The lecture generation unit also uses a generation AI to analyze academic news and trend information that is updated in real time and reflects that information in the lecture content. For example, the lecture generation unit provides lecture materials including the latest science and technology news. This allows learners' knowledge to be kept up to date by always providing the latest information.

[0053] The lecture generation unit can use the emotion estimation function to analyze the learner's emotional state and generate interactive content to increase motivation. For example, the lecture generation unit uses a generation AI to analyze the learner's emotional state in real time and incorporate interactive quizzes and games into the lecture content to increase motivation. For example, the lecture generation unit presents questions in a format that is likely to interest the learner. The lecture generation unit also uses the emotion estimation function to provide interactive content that refreshes tired learners. For example, the lecture generation unit inserts short break videos or relaxation music into lectures. The lecture generation unit also generates interactive content that provides encouraging messages and positive feedback according to the learner's emotional state. For example, an encouraging message is displayed when a learner faces a difficult problem. This can increase the learner's motivation and improve learning effectiveness.

[0054] The lecture generation unit can provide lecture content that corresponds to different languages ​​and cultures. For example, the lecture generation unit constructs a system in which a generation AI automatically translates lecture content and provides it in different languages. For example, it provides lecture materials in multiple languages ​​such as English, Japanese, and French. The lecture generation unit also generates lecture content that takes cultural context into account to accommodate learners with different cultural backgrounds. For example, it provides lecture materials that include culturally appropriate examples and case studies. The lecture generation unit also provides a multilingual interface using the generation AI, allowing learners to receive lecture content in their own language. For example, it adds multilingual subtitles to lecture videos. This makes it possible to accommodate global learners and broaden the scope of learning.

[0055] The lecture generation unit can provide lecture content in a multimedia format including visuals and audio. For example, the lecture generation unit uses a generation AI to generate lecture content in a multimedia format including visuals and audio and provide it to learners. For example, it adds charts, graphs, and audio commentary to lecture materials. The lecture generation unit also uses a generation AI to create video lectures and interactive simulations, allowing learners to learn through their eyes and ears. For example, it provides lecture materials that show experimental procedures in video. The lecture generation unit also uses a generation AI to generate content that provides audio commentary on the lecture content using speech recognition technology. For example, it provides a function to read lecture materials aloud. This can improve learning effectiveness through sight and hearing.

[0056] The lecture generation unit can use the emotion estimation function to provide feedback in real time according to the learner's emotions. For example, the generation AI in the lecture generation unit analyzes the learner's emotional state in real time and provides appropriate feedback based on the results. For example, when a learner faces a difficult problem, an encouraging message is displayed. The lecture generation unit also uses the emotion estimation function to provide learning advice in real time according to the learner's emotions. For example, when a learner is tired, a message urging the learner to take a break is displayed. The lecture generation unit also uses the generation AI to adjust the learning content based on the learner's emotional response to provide an optimal learning experience. For example, topics that are likely to interest the learner are provided preferentially. This makes it possible to maximize learning effectiveness by providing feedback according to the learner's emotions.

[0057] The peer review department can learn from past peer review history and evaluation criteria to provide more consistent peer review results. For example, the generation AI learns from past peer review history and provides consistent peer review results based on the evaluation criteria. For example, the same evaluation criteria are used to peer review multiple papers. The peer review department also analyzes past peer review data and builds a system in which the generation AI automatically adjusts the evaluation criteria. For example, it adjusts the weighting of the evaluation criteria to maintain consistency. The peer review department also learns evaluation criteria for specific fields or themes based on past peer review history to provide more accurate peer review results. For example, it uses evaluation criteria specialized for a specific research field. This can improve the reliability of the peer review process by providing consistent peer review results.

[0058] The peer review department can analyze the citation relationships and influence of papers to evaluate their importance. For example, the peer review department could build a system in which a generation AI analyzes a paper's citation relationships and evaluates its influence. For example, the importance of a paper could be evaluated based on the number of citations and the number of times it has been cited. The peer review department could also analyze a paper's citation network and have a generation AI evaluate its influence. For example, it could identify important papers and papers by influential researchers. The peer review department could also develop a system in which a generation AI evaluates the influence of an entire research field based on the citation relationships of a paper. For example, it could identify important research results in a specific topic. This could increase the academic value of a paper by evaluating its importance.

[0059] The peer review department can use the emotion estimation function to analyze the emotional state of reviewers and ensure the fairness of the peer review process. For example, the generation AI analyzes the emotional state of reviewers in real time and ensures the fairness of the peer review process based on the results. For example, if the emotions are biased, the peer review results are reevaluated. The peer review department can also use the emotion estimation function to monitor the emotional state of reviewers and build a system that provides fair evaluations. For example, if the emotions are negative, the peer review process can be suspended. The peer review department can also use the generation AI to analyze the emotional state of reviewers and adjust the peer review process based on the results. For example, reviewers with stable emotions can be given priority for peer review. This ensures the fairness of the peer review process and improves reliability.

[0060] The peer review department can also analyze the visuals and datasets of a paper and provide a comprehensive evaluation. For example, the generative AI analyzes the visual data and datasets of a paper, and provides a comprehensive review result based on the results. For example, it evaluates the accuracy of graphs and figures. The peer review department can also analyze the datasets included in a paper and build a system for the generative AI to evaluate the reliability and reproducibility of the data. For example, it evaluates the consistency and statistical significance of the data. The peer review department can also evaluate the overall quality of a paper based on the visual data and datasets of the generative AI. For example, it evaluates the interpretation of the visual data and how the datasets are used. This comprehensive evaluation can improve the quality of the paper.

[0061] The peer review department can use the emotion estimation function to provide feedback based on the reviewer's emotions and improve the peer review process. For example, the peer review department will build a system in which the generation AI analyzes the reviewer's emotional state and provides feedback based on the results. For example, if the emotion is negative, positive feedback will be provided. The peer review department can also use the emotion estimation function to provide real-time feedback based on the reviewer's emotions. For example, if the reviewer is feeling stressed, the department will make suggestions to help them relax. The peer review department can also use the generation AI to monitor the reviewer's emotional state and improve the peer review process based on the results. For example, the department will prioritize peer reviews by assigning peer reviews to reviewers who are emotionally stable. This can improve the peer review process and thereby improve the quality of peer reviews.

[0062] The publishing department can feature the latest research topics and trends to attract readers' attention. For example, the publishing department can use generative AI to automatically collect the latest research topics and trends and publish them as feature articles in scientific journals. For example, they can feature the latest research results in AI technology or biotechnology. The publishing department can also use generative AI to scan academic databases and compile newly published research topics and trends as feature articles. For example, they can feature the latest developments in a specific research field. The publishing department can also use generative AI to analyze academic news and trend information that is updated in real time and generate feature articles based on that information. For example, they can feature the latest science and technology news. This can attract readers' attention and increase the value of scientific journals.

[0063] Publishers can incorporate interactive elements to provide content in which readers can participate. For example, publishers can use generative AI to incorporate interactive quizzes and surveys into science magazines to provide content in which readers can participate. For example, they can publish quizzes related to feature articles. Publishers can also incorporate interactive simulations and experiments into science magazines to provide content that readers can actually try. For example, they can publish simulations of physics experiments. Publishers can also use generative AI to collect reader feedback in real time and provide interactive content based on the results. For example, they can publish feature articles that reflect reader opinions. This can increase reader engagement by providing content in which readers can participate.

[0064] The publishing department can use the emotion estimation function to analyze readers' emotional responses and publish special issues that match their interests. For example, the publishing department will build a system in which a generation AI analyzes readers' emotional responses and publishes special issues based on the results. For example, the department will feature topics that attract a lot of positive emotional responses. The publishing department will also use the emotion estimation function to collect readers' emotional responses in real time and publish special issues based on the results. For example, the department will feature topics that are likely to interest readers. The publishing department will also develop a system in which the generation AI adjusts the content of the special issue based on the readers' emotional responses. For example, the department will prioritize featuring topics that attract a high emotional response. This will allow the publishing of special issues that match readers' interests, thereby increasing reader satisfaction.

[0065] Publishers can provide scientific journals in different media formats (e.g., e-books, podcasts) to broaden reader access. For example, the publisher may use generative AI to provide the content of a scientific journal in e-book format, allowing readers to access it on digital devices. For example, the publisher may distribute the scientific journal in PDF or ePub format. The publisher may also provide the content of a scientific journal in podcast format, allowing readers to access academic information audio-wise. For example, the publisher may distribute a podcast that provides an audio explanation of a feature article. The publisher may also use generative AI to provide the content of a scientific journal in video format, allowing readers to access academic information visually. For example, the publisher may distribute content that explains a feature article in video. This allows the publisher to broaden reader access by providing the content in different media formats.

[0066] The publishing department can publish columns by researchers and experts from different fields to provide an interdisciplinary perspective. For example, the publishing department uses a generative AI to collect the opinions of researchers and experts from different fields and publish the content as a column in a scientific journal. For example, the publishing department could publish a conversation between an AI researcher and a biologist. The publishing department could also build a system to regularly publish columns by experts from different fields to provide an interdisciplinary perspective. For example, each issue could feature a column by an expert from a different field. The publishing department could also use a generative AI to analyze the opinions of researchers and experts from different fields and generate columns based on that content. For example, the publishing department could publish a feature article that incorporates perspectives from different fields. This can broaden the reader's knowledge by providing an interdisciplinary perspective.

[0067] The publishing department will use the emotion estimation function to collect feedback based on readers' emotions, allowing it to continuously improve the content of scientific journals. For example, the publishing department will build a system in which a generative AI analyzes readers' emotional responses and improves the content of scientific journals based on the results. For example, increasing the number of articles with a high number of positive emotional responses. The publishing department will also use the emotion estimation function to collect readers' emotional responses in real time and adjust the content of scientific journals based on the results. For example, prioritizing the publication of topics with a high emotional response. The publishing department will also develop a system in which a generative AI continuously improves the content of scientific journals based on readers' emotional responses. For example, improving articles with a low emotional response. In this way, the content of scientific journals can be continuously improved by collecting feedback based on readers' emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The lecture generation unit can provide customized lecture content according to the learner's learning style. For example, visual learners can be provided with lecture materials that make extensive use of charts and visual aids. Auditory learners can be provided with lecture materials in the form of audio commentary or podcasts. Furthermore, hands-on learners can be provided with lecture materials that include interactive simulations and experiments. This allows for an optimal learning experience according to each learner's learning style.

[0070] The lecture generation unit can monitor learners' progress in real time and provide feedback at the appropriate time. For example, if a learner is struggling with a particular topic, it can provide supplementary materials or additional practice questions. Also, when a learner achieves their goal, it can provide positive feedback to enhance their sense of accomplishment. Furthermore, it can suggest the next topic to study based on the learner's progress. This helps maintain learner motivation and support effective learning.

[0071] The lecture generation unit can collect learner feedback and continuously improve the lecture content based on the results. For example, it can analyze feedback provided by learners on the lecture content and identify areas for improvement. It can also add new topics and approaches to the lecture content based on learner feedback. It can also reflect learner feedback in real time and instantly adjust the lecture content. This makes it possible to provide high-quality lecture content that meets learner needs.

[0072] The lecture generation unit can use the emotion estimation function to analyze the learner's stress level and provide content to reduce stress. For example, if a learner is feeling high stress, it can provide relaxation techniques and stress management guides. It can also insert music and videos that help the learner relax during the lecture. Furthermore, it can adjust the learning pace and propose a reasonable learning plan according to the learner's stress level. This can reduce learner stress and support effective learning.

[0073] The lecture generation unit can incorporate gamification elements into lecture content to attract learners' interest. For example, learners can earn points each time they complete a specific task and compete for rankings. Learners can also earn badges and titles according to the goals they achieve. Furthermore, it can provide team-based activities in which learners collaborate with other learners to solve tasks. This increases learners' motivation and makes learning more enjoyable.

[0074] The lecture generation unit can use the learner's emotion estimation function to prioritize providing topics that are likely to interest the learner. For example, if a learner expresses positive emotions toward a particular topic, it can provide additional lecture materials related to that topic. It can also provide lecture content in a format that is likely to interest the learner. Furthermore, it can adjust the lecture content based on the learner's emotional response to provide an optimal learning experience. This can maintain the learner's interest and support effective learning.

[0075] The lecture generation unit can provide content for the learner to review what they have learned in the past based on their learning history. For example, it can provide quizzes and practice questions related to topics the learner has previously studied. It can also provide summary materials for the learner to review what they have previously learned. Furthermore, it can automatically identify topics that need to be reviewed based on the learner's learning history and provide review content at the appropriate time. This can strengthen the learner's memory and improve learning effectiveness.

[0076] The lecture generation unit can use the learner's emotion estimation function to provide appropriate support when a learner faces a difficult problem. For example, if a learner expresses negative emotions in response to a difficult problem, additional hints and explanations can be provided. It can also provide a step-by-step guide for the learner to solve the problem. Furthermore, if a learner expresses positive emotions in response to a difficult problem, it can provide an even more challenging problem. This makes it possible to provide appropriate support according to the learner's emotions and maximize learning effectiveness.

[0077] The lecture generation unit can provide customized lecture content according to the learner's learning environment. For example, it can provide interactive lecture materials suitable for an online learning environment. It can also provide lecture materials including group discussions and experiments suitable for face-to-face classes. Furthermore, to accommodate hybrid learning environments, it can provide lecture content that can be used both online and face-to-face. This makes it possible to provide an optimal learning experience according to the learner's learning environment.

[0078] The lecture generation unit can use the emotion estimation function to provide a learning plan based on the learner's emotions. For example, if the learner shows positive emotions, it can provide more challenging tasks. If the learner shows negative emotions, it can suggest relaxing content or breaks. Furthermore, it can adjust the learning pace and content according to the learner's emotions and provide an optimal learning plan. This makes it possible to support effective learning according to the learner's emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The lecture generation unit is equipped with a generation AI and generates lecture content according to the learner's needs and academic field. For example, for a learner who wants a physics lecture, the generation AI creates lecture materials covering everything from basic concepts to applications of physics. The input to the generation AI is a prompt containing instructions on the learner's desired lecture content, and the generation AI generates the lecture content based on that prompt. Step 2: The peer review unit reviews the lecture content generated by the lecture generation unit. For example, the generation AI peer reviews academic papers and research results, evaluating the accuracy and novelty of their content. The input to the generation AI is the paper or research result to be peer-reviewed, and the generation AI generates peer review results based on that content. Step 3: The Publishing Department publishes the lecture content that has been peer-reviewed by the Peer Review Department as a scientific journal. For example, it may publish a special issue compiling the lecture content generated by the generative AI and provide it to the academic community. It also publishes papers that have been evaluated through peer review, providing reliable academic information.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A lecture generation unit equipped with generative AI, a review unit that reviews the lecture content generated by the lecture generation unit; a publishing department that publishes the lecture content peer-reviewed by the peer-review department as a scientific journal. A system characterized by:

2. The lecture generation unit Providing personalized lecture materials that reflect learners' past learning history and interests 2. The system of claim 1.

3. The lecture generation unit Providing lecture content that reflects the latest research findings and trends in real time 2. The system of claim 1.

4. The lecture generation unit Analyzing learners' emotional states and generating interactive content to increase motivation 2. The system of claim 1.

5. The lecture generation unit Providing lecture content that is suitable for different languages ​​and cultures 2. The system of claim 1.

6. The lecture generation unit Delivering lecture content in multimedia formats, including visuals and audio 2. The system of claim 1.

7. The lecture generation unit Providing real-time feedback based on learners' emotions 2. The system of claim 1.

8. The peer review department: Learn from past peer review history and evaluation criteria to provide more consistent peer review results 2. The system of claim 1.

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