System

Abstract

An object of a system according to an embodiment is to generate an appropriate question for a presentation or a presentation material.SOLUTION: A system includes an input unit, a question generation unit, and an output unit. The input unit inputs a presentation or a presentation material. The question generation unit generates a question based on the content of the presentation or the presentation material input by the input unit. The output unit outputs the question generated by the question generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have difficulty generating appropriate questions for presentations and presentation materials, and there is room for improvement.

[0005] The system according to the embodiment aims to generate appropriate questions for presentations and presentation materials. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a question generation unit, and an output unit. The input unit inputs a presentation or presentation materials. The question generation unit generates questions based on the content of the presentation or presentation materials input by the input unit. The output unit outputs the questions generated by the question generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate appropriate questions for presentations and presentation materials. [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 question generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates 100 questions for a created presentation or presentation materials. This allows the question generation system to deepen the user's understanding of the presentation or presentation materials and enable effective preparation.

[0029] A question generation system according to an embodiment includes an input unit, a question generation unit, and an output unit. The input unit inputs presentations or presentation materials. For example, the input can be in various formats, such as a text file, a PDF file, or a slide-format file. The question generation unit generates questions based on the content of the presentation or presentation materials input by the input unit. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the presentation or presentation materials and generate 100 related questions. The generation AI can also use a multimodal generation AI to analyze the content of the presentation or presentation materials and generate questions. The output unit outputs the questions generated by the question generation unit. For example, the generated questions are provided to the user in text format or a list format. The output unit can also save the generated questions on the user's device. In this way, the question generation system can deepen the user's understanding by automatically generating and outputting questions for the presentation or presentation materials.

[0030] The question generation unit can refer to relevant external databases when analyzing the content of a presentation or presentation materials. For example, when analyzing the content of a presentation or presentation materials, the question generation unit may refer to a database of academic papers to supplement the specialized knowledge. For example, the latest research results on a specific technology or theory may be incorporated. The question generation unit may also refer to industry reports to supplement market trends and competitive information related to the presentation content. For example, the latest data on a specific market segment may be incorporated. The question generation unit may also refer to a database of news articles to supplement the latest events and trends related to the presentation content. For example, information on recent technological innovations and regulatory changes may be incorporated. In this way, by referencing external databases, the accuracy and relevance of questions are improved.

[0031] The question generation unit can refer to the creator's past presentations and presentation materials to evaluate consistency and evolution. For example, the generation AI analyzes the creator's past presentations and evaluates consistency with the current material. For example, it compares past slide designs and terminology usage. The question generation unit also refers to the creator's past presentations and evaluates the evolution of the content. For example, it analyzes changes in the topics covered in previous and current materials. The question generation unit also refers to feedback from the creator of the material's past presentations and identifies areas for improvement in the current material. For example, it suggests areas for strengthening the current material based on past feedback. This allows more effective questions to be generated by evaluating consistency and evolution with past materials.

[0032] The question generation unit can also analyze non-digital data such as handwritten notes and voice memos. For example, the question generation unit uses image recognition technology to analyze handwritten notes and convert the content into digital text. For example, it automatically reads and analyzes handwritten notes and diagrams. The question generation unit also uses speech recognition technology to analyze voice memos and convert the content into text. For example, it automatically transcribes recordings of meetings and presentations. When analyzing handwritten notes and voice memos, the question generation unit uses the AI ​​to understand the context and complement the relevant information. For example, it automatically references digital materials related to handwritten notes. This allows questions to be generated from a wider variety of information sources by analyzing non-digital data as well.

[0033] The question generation unit can automatically translate presentations and presentation materials created in different languages ​​and generate questions in multiple languages. In the question generation unit, for example, the generation AI automatically translates presentations created in different languages ​​and analyzes the content. For example, English materials are translated into Japanese and analyzed. In addition, the question generation unit generates multilingual questions using the generation AI based on the automatically translated content of the presentation. For example, questions are generated in both English and Japanese. In addition, the question generation unit analyzes presentation materials created in different languages ​​using the generation AI and generates questions corresponding to each language. For example, French materials are analyzed and questions are generated in French. In this way, multilingual questions can be generated, making it possible to handle materials in different languages.

[0034] The question generation unit can also include counterarguments to the content of the document or questions from different perspectives. For example, the question generation unit generates questions that include counterarguments to the content of the document using a generation AI. For example, it generates a question such as, "How is the reliability of this data ensured?" The question generation unit also generates questions from different perspectives regarding the content of the document. For example, it generates a question such as, "Are there any alternatives to this approach?" The question generation unit also generates questions that are critical of the content of the document using a generation AI. For example, it generates a question such as, "Are there any factors that were not considered in the process of arriving at this conclusion?" This makes it possible to evaluate the document from multiple perspectives by including counterarguments and questions from different perspectives.

[0035] The question generation unit can generate topical questions by referring to the latest research results and industry trends related to the content of the document. In the question generation unit, for example, the generation AI refers to the latest research results and generates questions related to the content of the document. For example, it generates a question such as, "What progress has there been in recent research?" The question generation unit also refers to industry trends and generates topical questions related to the content of the document. For example, it generates a question such as, "How are you responding to current market trends?" The question generation unit also refers to the latest technological trends and generates questions related to the content of the document. For example, it generates a question such as, "How do you think new technology will affect this project?" In this way, topical questions can be generated by referring to the latest research results and industry trends.

[0036] The question generation unit generates questions from the perspectives of different fields of expertise, thereby promoting a multifaceted evaluation of materials. In the question generation unit, for example, the generation AI generates questions from the perspectives of different fields of expertise. For example, it generates questions from a technical perspective or an economic perspective. The question generation unit also generates questions that incorporate knowledge from different fields of expertise to promote a multifaceted evaluation of the content of the material. For example, it generates questions from a legal perspective or an ethical perspective. The question generation unit also generates questions that combine the perspectives of different fields of expertise. For example, it generates questions that take into account both technical and economic perspectives. This makes it possible to generate questions from the perspectives of different fields of expertise, thereby enabling a multifaceted evaluation of the material.

[0037] The question generation unit can refer to the user's past answer history and generate questions according to the user's level of understanding. In the question generation unit, for example, a generation AI refers to the user's past answer history and generates questions according to the user's level of understanding. For example, it generates applied questions related to questions that were answered correctly in the past. The question generation unit also generates questions of difficulty according to the user's level of understanding based on the user's past answer history. For example, it generates basic questions related to questions that were answered incorrectly in the past. The question generation unit also analyzes the user's answer history and generates customized questions according to the user's level of understanding. For example, it generates questions to deepen the user's understanding of a specific topic. This enables more effective learning by generating questions according to the user's level of understanding.

[0038] The output unit can output questions in stages in line with the progress of the user's presentation. For example, the generation AI in the output unit outputs questions in stages in line with the progress of the presentation. For example, it sequentially outputs questions corresponding to the content of each slide. The output unit also analyzes the progress of the presentation in real time and outputs questions in line with the progress. For example, it outputs questions about important points in the middle of the presentation. The output unit also adjusts the timing of question output in line with the progress of the presentation by the generation AI. For example, it outputs a general question after the presentation ends. In this way, more effective feedback can be obtained by outputting questions in line with the progress of the presentation.

[0039] The output unit can provide questions in a format that is optimal for the user's presentation style. For example, the generation AI in the output unit analyzes the user's presentation style and provides questions in a format that is optimal for that style. For example, questions that include diagrams are provided for a visually-oriented presentation. The output unit also customizes the question output format according to the presentation style. For example, text-based questions are provided for a text-format presentation. The output unit also learns the user's presentation style and generates questions that are optimal for that style. For example, dialogue-style questions are provided for an interactive presentation. In this way, feedback that meets the user's needs can be obtained by providing questions in a format that is optimal for the presentation style.

[0040] The output unit can automatically save the questions generated by the generation AI not only on the user's device but also in a shared space on the cloud, allowing them to be shared with team members in real time. For example, the output unit can automatically save the questions generated by the generation AI in a shared space on the cloud and share them with team members in real time. For example, it can save them in Google Drive or Dropbox. The output unit can also save the question output not only on the user's device but also in a project management tool on the cloud, allowing the entire team to access it. For example, it can save them in Trello or Asana. The output unit can also save the questions generated by the generation AI in a shared space on the cloud, allowing team members to add comments and feedback in real time. For example, it can connect to Slack or Microsoft Teams. This makes collaboration more efficient by saving them in a shared space on the cloud and sharing them with team members in real time.

[0041] The output unit can provide questions in voice format through a voice assistant, allowing the user to confirm the questions hands-free. For example, the output unit provides questions generated by the generation AI in voice format through a voice assistant (e.g., Amazon Alexa or Google Assistant). The user can confirm the questions hands-free. The output unit also provides the question output in voice format, allowing the user to confirm the questions while driving or on the move. For example, using a smartphone's voice assistant. The output unit also links the questions generated by the generation AI to the voice assistant, allowing the user to control the questions with voice commands. For example, it responds to commands such as "tell me the next question." As a result, by providing questions in voice format through the voice assistant, the user can confirm the questions hands-free.

[0042] A question generation system according to an embodiment includes a question customization unit. The question customization unit provides example answers and reference materials suggested by the generation AI. For example, the question customization unit suggests example answers to questions generated by the generation AI. For example, it provides a specific answer in the form of, "Example answers to this question are as follows." The question customization unit also allows the generation AI to suggest reference materials related to the question. For example, it provides a link in the form of, "Reference materials related to this question are here." The question customization unit also allows the generation AI to provide example answers and reference materials to questions, allowing the user to prepare an answer based on them. For example, past presentation materials or academic papers can be referenced. In this way, the example answers and reference materials suggested by the generation AI allow the user to more effectively prepare for questions.

[0043] The question customization unit can refer to the user's past customization history and suggest the optimal customization option. In the question customization unit, for example, the generation AI refers to the user's past customization history and suggests the optimal customization option. For example, suggestions are made based on the question format and tone selected in the past. In addition, the question customization unit suggests the optimal question customization option based on the user's customization history. For example, it prioritizes suggesting question formats that have been well received in the past. In addition, the question customization unit analyzes the user's customization history and suggests the optimal customization option in real time. For example, it provides customization options that meet the user's current needs. In this way, the optimal customization option can be suggested to the user by referring to the past customization history.

[0044] The question customization unit can provide a collaboration function for working together with other users. For example, the question customization unit provides a collaboration function for the generation AI to collaborate with other users to customize questions. For example, it provides a shared document that allows questions to be edited in real time. The question customization unit also provides a collaboration tool for collaborating with team members to customize questions. For example, it uses an online conference tool for collaborative editing. The question customization unit also provides a platform for the generation AI to collaborate with other users to customize questions. For example, it uses a project management tool to customize questions. Thus, by providing the collaboration function, it becomes possible to collaborate with other users to customize questions.

[0045] The question customization unit can create templates so that they can be applied to different presentations and presentation materials. For example, the question customization unit allows the generation AI to create templates for customized questions, making them applicable to different presentations and presentation materials. For example, it creates a common question template. The question customization unit also creates templates for customized questions, making them applicable to different projects or themes. For example, it provides question templates specialized for specific industries or fields. The question customization unit also allows the generation AI to create templates for customized questions, making them easy for users to apply. For example, it provides a function to add questions to templates by drag and drop. This allows the templates to be applied to different presentations and presentation materials.

[0046] A question generation system according to an embodiment includes a question storage unit. The question storage unit is automatically updated according to the progress of the presentation, providing the latest list of questions. For example, the generation AI analyzes the progress of the presentation in real time and automatically updates the question list. For example, when a new slide is added, it generates a related question. The question storage unit also dynamically updates the question list according to the progress of the presentation. For example, when a new topic is added during the presentation, it generates a question. The question storage unit also monitors the progress of the presentation and provides the latest list of questions. For example, a general question is added after the presentation ends. In this way, the question list can be automatically updated according to the progress of the presentation, allowing the latest questions to be always provided.

[0047] The question storage unit can enable saving not only in text format but also in audio or video format. For example, the question storage unit can save questions generated by the generation AI not only in text format but also in audio format. For example, the question storage unit can save the questions as audio files so that they can be played back later. The question storage unit can also diversify the saving format of questions and save them in video format. For example, the question can be saved as a video clip so that it can be visually confirmed. The question storage unit can also save questions generated by the generation AI in text, audio, or video format so that the user can use them in the most optimal format. For example, the question can be saved in audio format for practicing a presentation. This allows questions to be saved in not only text format but also audio or video format, allowing them to be saved in a format that meets the user's needs.

[0048] The question storage unit can enable seamless saving across different devices. For example, the question storage unit builds a system that seamlessly synchronizes questions generated by the generation AI across different devices. For example, questions are automatically synchronized between smartphones, tablets, and PCs. The question storage unit also saves and shares questions on a cloud-based basis, allowing users to access them from any device. For example, the question storage unit saves questions using a cloud storage service. The question storage unit also updates questions generated by the generation AI across different devices in real time, allowing users to access the latest question list. For example, if a question is added during a presentation, it is immediately reflected on all devices. This allows seamless saving across different devices, allowing users to access questions from any device.

[0049] The question storage unit can be made to be able to manage questions by associating them with specific projects or teams. For example, the question storage unit builds a system that manages questions generated by the generation AI by associating them with specific projects or teams. For example, a question list is created for each project and shared with team members. The question storage unit also links the saving and sharing of questions with a project management tool and manages them by associating them with specific projects or teams. For example, questions are managed by linking with Asana or Trello. The question storage unit also tags questions generated by the generation AI by associating them with specific projects or teams, allowing for efficient management. For example, questions can be filtered by project name or team name. This enables efficient question management by managing questions by associating them with specific projects or teams.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The question generation unit generates relevant questions based on the content of a user's presentation or presentation materials. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the presentation or presentation materials and generate 100 relevant questions. The generation AI can also use a multimodal generation AI to analyze the content of the presentation or presentation materials and generate questions. The output unit provides the generated questions to the user in text or list format. The output unit can also save the generated questions on the user's device. In this way, the question generation system can deepen the user's understanding by automatically generating and outputting questions for presentations and presentation materials.

[0052] The question generation unit can refer to relevant external databases when analyzing the content of a presentation or presentation materials. For example, when analyzing the content of a presentation or presentation materials, the generation AI may refer to a database of academic papers to supplement expert knowledge. For example, it may incorporate the latest research results on a specific technology or theory. The question generation unit may also refer to industry reports to supplement market trends and competitive information related to the presentation content. For example, it may incorporate the latest data on a specific market segment. The question generation unit may also refer to a database of news articles to supplement the latest events and trends related to the presentation content. For example, it may incorporate information on recent technological innovations and regulatory changes. In this way, by referencing external databases, the accuracy and relevance of questions are improved.

[0053] The question generation unit can refer to the creator's past presentations and presentation materials to evaluate consistency and evolution. For example, the generation AI can analyze the creator's past presentations and evaluate consistency with the current material. For example, it can compare past slide designs and terminology usage. The question generation unit can also refer to the creator's past presentations and evaluate the evolution of the content. For example, it can analyze changes in the topics covered in previous and current materials. The question generation unit can also refer to feedback from the creator of the material's past presentations to identify areas for improvement in the current material. For example, it can suggest areas for strengthening the current material based on past feedback. This allows for the generation of more effective questions by evaluating consistency and evolution with past materials.

[0054] The question generation unit can also analyze non-digital data such as handwritten notes and voice memos. For example, the generation AI can analyze handwritten notes using image recognition technology and convert the content into digital text. For example, it can automatically read and analyze handwritten notes and diagrams. The question generation unit can also analyze voice memos using speech recognition technology and convert the content into text. For example, it can automatically transcribe recordings of meetings and presentations. When analyzing handwritten notes and voice memos, the generation AI can understand the context and complement the relevant information. For example, it can automatically reference digital materials related to handwritten notes. This allows questions to be generated from a wider variety of information sources by analyzing non-digital data as well.

[0055] The question generation unit can automatically translate presentations and presentation materials created in different languages ​​and generate questions that support multiple languages. For example, the generation AI automatically translates a presentation created in a different language and analyzes the content. For example, it translates English materials into Japanese and analyzes them. The question generation unit also generates questions that support multiple languages ​​based on the content of the automatically translated presentation. For example, it generates questions in both English and Japanese. The question generation unit also analyzes presentation materials created in different languages ​​and generates questions that support each language. For example, it analyzes French materials and generates questions in French. This allows the generation of multilingual questions, making it possible to handle materials in different languages.

[0056] The question generation unit can also include counterarguments to the content of the document or questions from different perspectives. For example, the generation AI generates questions that include counterarguments to the content of the document. For example, it generates a question such as, "How is the reliability of this data ensured?" The question generation unit also generates questions from different perspectives to the content of the document. For example, it generates a question such as, "Are there any alternatives to this approach?" The question generation unit also generates questions that are critical of the content of the document. For example, it generates a question such as, "Are there any factors that were not considered in the process of reaching this conclusion?" This makes it possible to evaluate the document from multiple perspectives by including counterarguments and questions from different perspectives.

[0057] The question generation unit can generate topical questions by referring to the latest research results and industry trends related to the content of the document. For example, the generation AI refers to the latest research results and generates questions related to the content of the document. For example, it generates a question such as, "What progress has there been in recent research?" The question generation unit also refers to industry trends and generates topical questions related to the content of the document. For example, it generates a question such as, "How are you responding to current market trends?" The question generation unit also refers to the latest technological trends and generates questions related to the content of the document. For example, it generates a question such as, "How do you think new technology will affect this project?" In this way, topical questions can be generated by referring to the latest research results and industry trends.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The input section is where you input presentation and presentation materials. For example, input can be in a variety of formats, such as text files, PDF files, or slide-format files. Step 2: The question generation unit generates questions based on the content of the presentation or presentation materials input by the input unit. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the presentation or presentation materials and generate 100 related questions. The generation AI can also use a multimodal generation AI to analyze the content of the presentation or presentation materials and generate questions. Step 3: The output unit outputs the questions generated by the question generator. For example, the generated questions are provided to the user in a text format or a list format. The output unit can also save the generated questions in the user's device.

[0060] (Example 2) The question generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates 100 questions for a created presentation or presentation materials. This allows the question generation system to deepen the user's understanding of the presentation or presentation materials and enable effective preparation.

[0061] A question generation system according to an embodiment includes an input unit, a question generation unit, and an output unit. The input unit inputs presentations or presentation materials. For example, the input can be in various formats, such as a text file, a PDF file, or a slide-format file. The question generation unit generates questions based on the content of the presentation or presentation materials input by the input unit. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the presentation or presentation materials and generate 100 related questions. The generation AI can also use a multimodal generation AI to analyze the content of the presentation or presentation materials and generate questions. The output unit outputs the questions generated by the question generation unit. For example, the generated questions are provided to the user in text format or a list format. The output unit can also save the generated questions on the user's device. In this way, the question generation system can deepen the user's understanding by automatically generating and outputting questions for the presentation or presentation materials.

[0062] The question generation unit can refer to relevant external databases when analyzing the content of a presentation or presentation materials. For example, when analyzing the content of a presentation or presentation materials, the question generation unit may refer to a database of academic papers to supplement the specialized knowledge. For example, the latest research results on a specific technology or theory may be incorporated. The question generation unit may also refer to industry reports to supplement market trends and competitive information related to the presentation content. For example, the latest data on a specific market segment may be incorporated. The question generation unit may also refer to a database of news articles to supplement the latest events and trends related to the presentation content. For example, information on recent technological innovations and regulatory changes may be incorporated. In this way, by referencing external databases, the accuracy and relevance of questions are improved.

[0063] The question generation unit can refer to the creator's past presentations and presentation materials to evaluate consistency and evolution. For example, the generation AI analyzes the creator's past presentations and evaluates consistency with the current material. For example, it compares past slide designs and terminology usage. The question generation unit also refers to the creator's past presentations and evaluates the evolution of the content. For example, it analyzes changes in the topics covered in previous and current materials. The question generation unit also refers to feedback from the creator of the material's past presentations and identifies areas for improvement in the current material. For example, it suggests areas for strengthening the current material based on past feedback. This allows more effective questions to be generated by evaluating consistency and evolution with past materials.

[0064] The question generation unit can use the emotion estimation function to analyze the emotional impact that the content of the document has on the audience and generate questions based on the results. For example, the question generation unit uses a generation AI to analyze the content of the document and the emotion estimation function to identify emotions that the audience may be feeling. For example, it estimates positive or negative emotions from the tone and wording of the document. The question generation unit also uses the emotion estimation function to evaluate the emotional impact that each section of the document has on the audience and generates questions based on the results. For example, it generates questions focusing on sections that are expected to elicit strong emotional reactions. The question generation unit also generates questions that elicit emotional responses from the audience based on the emotion estimation results for the content of the document. For example, it generates questions that encourage emotional empathy or questions that stimulate discussion. In this way, generating questions that take emotional impact into consideration makes it easier to elicit responses from the audience.

[0065] The question generation unit can also analyze non-digital data such as handwritten notes and voice memos. For example, the question generation unit uses image recognition technology to analyze handwritten notes and convert the content into digital text. For example, it automatically reads and analyzes handwritten notes and diagrams. The question generation unit also uses speech recognition technology to analyze voice memos and convert the content into text. For example, it automatically transcribes recordings of meetings and presentations. When analyzing handwritten notes and voice memos, the question generation unit uses the AI ​​to understand the context and complement the relevant information. For example, it automatically references digital materials related to handwritten notes. This allows questions to be generated from a wider variety of information sources by analyzing non-digital data as well.

[0066] The question generation unit can automatically translate presentations and presentation materials created in different languages ​​and generate questions in multiple languages. In the question generation unit, for example, the generation AI automatically translates presentations created in different languages ​​and analyzes the content. For example, English materials are translated into Japanese and analyzed. In addition, the question generation unit generates multilingual questions using the generation AI based on the automatically translated content of the presentation. For example, questions are generated in both English and Japanese. In addition, the question generation unit analyzes presentation materials created in different languages ​​using the generation AI and generates questions corresponding to each language. For example, French materials are analyzed and questions are generated in French. In this way, multilingual questions can be generated, making it possible to handle materials in different languages.

[0067] The question generation unit can analyze the emotions felt by the document creator when they are entering the data, and adjust the tone and content of the questions based on those emotions. For example, the question generation unit uses a generation AI to analyze the emotions felt by the document creator when they are entering the data, and adjust the tone of the questions based on those emotions. For example, it generates positive questions for a creator who has positive emotions. The question generation unit also analyzes the emotions of the document creator in real time, and adjusts the content of the questions based on those emotions. For example, it generates questions that will relax a creator who is nervous. The question generation unit also generates questions that the document creator can answer most effectively based on the emotion analysis results. For example, it uses specific or abstract questions depending on the creator's emotions. This makes it possible to generate more appropriate questions by adjusting the questions based on the creator's emotions.

[0068] The question generation unit can also include counterarguments to the content of the document or questions from different perspectives. For example, the question generation unit generates questions that include counterarguments to the content of the document using a generation AI. For example, it generates a question such as, "How is the reliability of this data ensured?" The question generation unit also generates questions from different perspectives regarding the content of the document. For example, it generates a question such as, "Are there any alternatives to this approach?" The question generation unit also generates questions that are critical of the content of the document using a generation AI. For example, it generates a question such as, "Are there any factors that were not considered in the process of arriving at this conclusion?" This makes it possible to evaluate the document from multiple perspectives by including counterarguments and questions from different perspectives.

[0069] The question generation unit can generate topical questions by referring to the latest research results and industry trends related to the content of the document. In the question generation unit, for example, the generation AI refers to the latest research results and generates questions related to the content of the document. For example, it generates a question such as, "What progress has there been in recent research?" The question generation unit also refers to industry trends and generates topical questions related to the content of the document. For example, it generates a question such as, "How are you responding to current market trends?" The question generation unit also refers to the latest technological trends and generates questions related to the content of the document. For example, it generates a question such as, "How do you think new technology will affect this project?" In this way, topical questions can be generated by referring to the latest research results and industry trends.

[0070] The question generation unit can predict the audience's emotional reaction to the content of the material and generate questions based on that reaction. For example, the question generation unit uses a generative AI to predict the audience's emotional reaction to the content of the material and generate questions based on that reaction. For example, it generates questions for topics that are expected to elicit strong emotional reactions. The question generation unit also predicts the audience's emotional reaction and adjusts the tone of the questions based on that reaction. For example, it generates cautious questions for emotionally sensitive topics. The question generation unit also generates questions that are likely to attract the audience's attention based on the emotional reaction prediction results. For example, it generates questions that elicit positive emotions or questions that stimulate discussion. In this way, more effective questions can be generated by predicting the audience's emotional reactions.

[0071] The question generation unit generates questions from the perspectives of different fields of expertise, thereby promoting a multifaceted evaluation of materials. In the question generation unit, for example, the generation AI generates questions from the perspectives of different fields of expertise. For example, it generates questions from a technical perspective or an economic perspective. The question generation unit also generates questions that incorporate knowledge from different fields of expertise to promote a multifaceted evaluation of the content of the material. For example, it generates questions from a legal perspective or an ethical perspective. The question generation unit also generates questions that combine the perspectives of different fields of expertise. For example, it generates questions that take into account both technical and economic perspectives. This makes it possible to generate questions from the perspectives of different fields of expertise, thereby enabling a multifaceted evaluation of the material.

[0072] The question generation unit can refer to the user's past answer history and generate questions according to the user's level of understanding. In the question generation unit, for example, a generation AI refers to the user's past answer history and generates questions according to the user's level of understanding. For example, it generates applied questions related to questions that were answered correctly in the past. The question generation unit also generates questions of difficulty according to the user's level of understanding based on the user's past answer history. For example, it generates basic questions related to questions that were answered incorrectly in the past. The question generation unit also analyzes the user's answer history and generates customized questions according to the user's level of understanding. For example, it generates questions to deepen the user's understanding of a specific topic. This enables more effective learning by generating questions according to the user's level of understanding.

[0073] The question generation unit can prioritize generating questions related to topics that are likely to interest the user the most. For example, the question generation unit uses a generation AI to analyze the user's interests and prioritize generating questions related to topics that are likely to interest the user the most. For example, questions are generated based on the user's past search history and browsing history. The question generation unit also prioritizes generating questions related to related topics based on the user's interests. For example, questions related to themes that interest the user are generated. The question generation unit also uses a generation AI to analyze the user's interests in real time and prioritize generating questions based on the results. For example, questions related to topics that the user is currently interested in are generated. This can increase the user's motivation to learn by generating questions based on the user's interests.

[0074] The output unit can output questions in stages in line with the progress of the user's presentation. For example, the generation AI in the output unit outputs questions in stages in line with the progress of the presentation. For example, it sequentially outputs questions corresponding to the content of each slide. The output unit also analyzes the progress of the presentation in real time and outputs questions in line with the progress. For example, it outputs questions about important points in the middle of the presentation. The output unit also adjusts the timing of question output in line with the progress of the presentation by the generation AI. For example, it outputs a general question after the presentation ends. In this way, more effective feedback can be obtained by outputting questions in line with the progress of the presentation.

[0075] The output unit can provide questions in a format that is optimal for the user's presentation style. For example, the generation AI in the output unit analyzes the user's presentation style and provides questions in a format that is optimal for that style. For example, questions that include diagrams are provided for a visually-oriented presentation. The output unit also customizes the question output format according to the presentation style. For example, text-based questions are provided for a text-format presentation. The output unit also learns the user's presentation style and generates questions that are optimal for that style. For example, dialogue-style questions are provided for an interactive presentation. In this way, feedback that meets the user's needs can be obtained by providing questions in a format that is optimal for the presentation style.

[0076] The output unit can predict the user's emotional response when receiving a question and adjust the output order of questions based on that response. For example, the output unit uses a generation AI to predict the user's emotional response and adjusts the output order of questions based on that response. For example, questions that are expected to elicit a positive response may be output first. The output unit also analyzes the user's emotional response in real time and adjusts the output order of questions based on the results. For example, questions that will relieve tension may be output first. The output unit also outputs questions in an order that allows the user to answer most effectively based on the emotional response prediction results. For example, starting with easy questions and gradually increasing the difficulty level. In this way, adjusting the output order of questions based on the user's emotional response provides more effective feedback.

[0077] The output unit can automatically save the questions generated by the generation AI not only on the user's device but also in a shared space on the cloud, allowing them to be shared with team members in real time. For example, the output unit can automatically save the questions generated by the generation AI in a shared space on the cloud and share them with team members in real time. For example, it can save them in Google Drive or Dropbox. The output unit can also save the question output not only on the user's device but also in a project management tool on the cloud, allowing the entire team to access it. For example, it can save them in Trello or Asana. The output unit can also save the questions generated by the generation AI in a shared space on the cloud, allowing team members to add comments and feedback in real time. For example, it can connect to Slack or Microsoft Teams. This makes collaboration more efficient by saving them in a shared space on the cloud and sharing them with team members in real time.

[0078] The output unit can provide questions in voice format through a voice assistant, allowing the user to confirm the questions hands-free. For example, the output unit provides questions generated by the generation AI in voice format through a voice assistant (e.g., Amazon Alexa or Google Assistant). The user can confirm the questions hands-free. The output unit also provides the question output in voice format, allowing the user to confirm the questions while driving or on the move. For example, using a smartphone's voice assistant. The output unit also links the questions generated by the generation AI to the voice assistant, allowing the user to control the questions with voice commands. For example, it responds to commands such as "tell me the next question." As a result, by providing questions in voice format through the voice assistant, the user can confirm the questions hands-free.

[0079] The output unit can adjust the schedule so that questions are output when the user is most relaxed. For example, the generation AI in the output unit analyzes the user's schedule and outputs questions when the user is most relaxed. For example, it references the user's calendar and outputs questions during free time. The output unit also analyzes the user's biometric data (e.g., heart rate and stress level) and outputs questions when the user is most relaxed. For example, it uses data from a smartwatch. The output unit also learns the user's past behavioral patterns and outputs questions when the user is most relaxed. For example, it identifies the time of day when the user is relaxed and outputs questions at that time. In this way, more effective feedback can be obtained by outputting questions when the user is relaxed.

[0080] A question generation system according to an embodiment includes a question customization unit. The question customization unit provides example answers and reference materials suggested by the generation AI. For example, the question customization unit suggests example answers to questions generated by the generation AI. For example, it provides a specific answer in the form of, "Example answers to this question are as follows." The question customization unit also allows the generation AI to suggest reference materials related to the question. For example, it provides a link in the form of, "Reference materials related to this question are here." The question customization unit also allows the generation AI to provide example answers and reference materials to questions, allowing the user to prepare an answer based on them. For example, past presentation materials or academic papers can be referenced. In this way, the example answers and reference materials suggested by the generation AI allow the user to more effectively prepare for questions.

[0081] The question customization unit can refer to the user's past customization history and suggest the optimal customization option. In the question customization unit, for example, the generation AI refers to the user's past customization history and suggests the optimal customization option. For example, suggestions are made based on the question format and tone selected in the past. In addition, the question customization unit suggests the optimal question customization option based on the user's customization history. For example, it prioritizes suggesting question formats that have been well received in the past. In addition, the question customization unit analyzes the user's customization history and suggests the optimal customization option in real time. For example, it provides customization options that meet the user's current needs. In this way, the optimal customization option can be suggested to the user by referring to the past customization history.

[0082] The question customization unit can predict the audience's emotional reaction to questions customized by the user and adjust the customized content based on that reaction. For example, the question customization unit uses a generation AI to predict the audience's emotional reaction to questions customized by the user and adjusts the customized content based on that reaction. For example, it prioritizes questions that are expected to elicit a positive reaction. The question customization unit also predicts the audience's emotional reaction and adjusts the tone and content of questions based on that reaction. For example, it generates careful questions for emotionally sensitive topics. The question customization unit also customizes questions that the user can answer most effectively based on the emotional reaction prediction results. For example, it starts with easy questions and gradually increases the difficulty. This enables more effective customization by predicting the audience's emotional reaction.

[0083] The question customization unit can provide a collaboration function for working together with other users. For example, the question customization unit provides a collaboration function for the generation AI to collaborate with other users to customize questions. For example, it provides a shared document that allows questions to be edited in real time. The question customization unit also provides a collaboration tool for collaborating with team members to customize questions. For example, it uses an online conference tool for collaborative editing. The question customization unit also provides a platform for the generation AI to collaborate with other users to customize questions. For example, it uses a project management tool to customize questions. Thus, by providing the collaboration function, it becomes possible to collaborate with other users to customize questions.

[0084] The question customization unit can create templates so that they can be applied to different presentations and presentation materials. For example, the question customization unit allows the generation AI to create templates for customized questions, making them applicable to different presentations and presentation materials. For example, it creates a common question template. The question customization unit also creates templates for customized questions, making them applicable to different projects or themes. For example, it provides question templates specialized for specific industries or fields. The question customization unit also allows the generation AI to create templates for customized questions, making them easy for users to apply. For example, it provides a function to add questions to templates by drag and drop. This allows the templates to be applied to different presentations and presentation materials.

[0085] The question customization unit can suggest customization options that evoke the most positive emotions in the user. For example, the question customization unit uses a generation AI to analyze the user's emotions and suggest customization options that evoke the most positive emotions. For example, suggestions are made based on the user's past responses. The question customization unit also analyzes the user's emotional responses in real time and suggests optimal customization options based on the results. For example, options that evoke stronger positive emotions are prioritized. The question customization unit also suggests customization options that evoke the most positive emotions in the user based on the emotion analysis results. For example, options that match the user's preferences and interests are provided. This makes it possible to increase user satisfaction by suggesting customization options that evoke the most positive emotions in the user.

[0086] A question generation system according to an embodiment includes a question storage unit. The question storage unit is automatically updated according to the progress of the presentation, providing the latest list of questions. For example, the generation AI analyzes the progress of the presentation in real time and automatically updates the question list. For example, when a new slide is added, it generates a related question. The question storage unit also dynamically updates the question list according to the progress of the presentation. For example, when a new topic is added during the presentation, it generates a question. The question storage unit also monitors the progress of the presentation and provides the latest list of questions. For example, a general question is added after the presentation ends. In this way, the question list can be automatically updated according to the progress of the presentation, allowing the latest questions to be always provided.

[0087] The question storage unit can enable saving not only in text format but also in audio or video format. For example, the question storage unit can save questions generated by the generation AI not only in text format but also in audio format. For example, the question storage unit can save the questions as audio files so that they can be played back later. The question storage unit can also diversify the saving format of questions and save them in video format. For example, the question can be saved as a video clip so that it can be visually confirmed. The question storage unit can also save questions generated by the generation AI in text, audio, or video format so that the user can use them in the most optimal format. For example, the question can be saved in audio format for practicing a presentation. This allows questions to be saved in not only text format but also audio or video format, allowing them to be saved in a format that meets the user's needs.

[0088] The question storage unit can analyze emotional responses to questions saved by users and make suggestions for improving the questions based on the results. For example, the question storage unit allows the generation AI to analyze emotional responses to questions saved by users and make suggestions for improving the questions based on the results. For example, it makes suggestions to improve questions that receive few positive responses. Furthermore, the question storage unit allows the generation AI to identify points for improvement in questions based on the emotional response data and make specific suggestions for improvement. For example, it makes suggestions to adjust the tone or content of the question. Furthermore, the question storage unit allows the generation AI to analyze the user's emotional responses in real time and make suggestions for improving the questions based on the results. For example, it regenerates questions in response to changes in the user's emotions. In this way, by analyzing the user's emotional responses and making suggestions for improving the questions based on the results, more effective questions can be provided.

[0089] The question storage unit can enable seamless saving across different devices. For example, the question storage unit builds a system that seamlessly synchronizes questions generated by the generation AI across different devices. For example, questions are automatically synchronized between smartphones, tablets, and PCs. The question storage unit also saves and shares questions on a cloud-based basis, allowing users to access them from any device. For example, the question storage unit saves questions using a cloud storage service. The question storage unit also updates questions generated by the generation AI across different devices in real time, allowing users to access the latest question list. For example, if a question is added during a presentation, it is immediately reflected on all devices. This allows seamless saving across different devices, allowing users to access questions from any device.

[0090] The question storage unit can be made to be able to manage questions by associating them with specific projects or teams. For example, the question storage unit builds a system that manages questions generated by the generation AI by associating them with specific projects or teams. For example, a question list is created for each project and shared with team members. The question storage unit also links the saving and sharing of questions with a project management tool and manages them by associating them with specific projects or teams. For example, questions are managed by linking with Asana or Trello. The question storage unit also tags questions generated by the generation AI by associating them with specific projects or teams, allowing for efficient management. For example, questions can be filtered by project name or team name. This enables efficient question management by managing questions by associating them with specific projects or teams.

[0091] The question storage unit can collect other users' emotional reactions to shared questions and make suggestions for improving the questions based on those reactions. For example, the question storage unit allows the generation AI to collect other users' emotional reactions to shared questions and make suggestions for improving the questions based on that data. For example, it makes suggestions for improving questions that have few positive reactions. The question storage unit also allows the generation AI to identify points for improving the questions based on the emotional reaction data of other users and make specific suggestions for improvement. For example, it makes suggestions to adjust the tone or content of the question. The question storage unit also allows the generation AI to analyze other users' emotional reactions in real time and make suggestions for improving the questions based on the results. For example, it regenerates questions in response to changes in the user's emotions. In this way, by collecting other users' emotional reactions and making suggestions for improving the questions based on those reactions, more effective questions can be provided.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The question generation unit generates relevant questions based on the content of a user's presentation or presentation materials. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the presentation or presentation materials and generate 100 relevant questions. The generation AI can also use a multimodal generation AI to analyze the content of the presentation or presentation materials and generate questions. The output unit provides the generated questions to the user in text or list format. The output unit can also save the generated questions on the user's device. In this way, the question generation system can deepen the user's understanding by automatically generating and outputting questions for presentations and presentation materials.

[0094] The question generation unit can refer to relevant external databases when analyzing the content of a presentation or presentation materials. For example, when analyzing the content of a presentation or presentation materials, the generation AI may refer to a database of academic papers to supplement expert knowledge. For example, it may incorporate the latest research results on a specific technology or theory. The question generation unit may also refer to industry reports to supplement market trends and competitive information related to the presentation content. For example, it may incorporate the latest data on a specific market segment. The question generation unit may also refer to a database of news articles to supplement the latest events and trends related to the presentation content. For example, it may incorporate information on recent technological innovations and regulatory changes. In this way, by referencing external databases, the accuracy and relevance of questions are improved.

[0095] The question generation unit can refer to the creator's past presentations and presentation materials to evaluate consistency and evolution. For example, the generation AI can analyze the creator's past presentations and evaluate consistency with the current material. For example, it can compare past slide designs and terminology usage. The question generation unit can also refer to the creator's past presentations and evaluate the evolution of the content. For example, it can analyze changes in the topics covered in previous and current materials. The question generation unit can also refer to feedback from the creator of the material's past presentations to identify areas for improvement in the current material. For example, it can suggest areas for strengthening the current material based on past feedback. This allows for the generation of more effective questions by evaluating consistency and evolution with past materials.

[0096] The question generation unit can use the emotion estimation function to analyze the emotional impact that the content of the document has on the audience and generate questions based on the results. For example, the generation AI analyzes the content of the document and uses the emotion estimation function to identify emotions that the audience is likely to feel. For example, it estimates positive or negative emotions from the tone and wording of the document. The question generation unit also uses the emotion estimation function to evaluate the emotional impact that each section of the document has on the audience and generates questions based on the results. For example, it focuses on generating questions for sections that are expected to elicit strong emotional reactions. The question generation unit also generates questions that elicit emotional responses from the audience based on the emotion estimation results for the content of the document. For example, it generates questions that encourage emotional empathy or questions that stimulate discussion. In this way, generating questions that take emotional impact into consideration makes it easier to elicit responses from the audience.

[0097] The question generation unit can also analyze non-digital data such as handwritten notes and voice memos. For example, the generation AI can analyze handwritten notes using image recognition technology and convert the content into digital text. For example, it can automatically read and analyze handwritten notes and diagrams. The question generation unit can also analyze voice memos using speech recognition technology and convert the content into text. For example, it can automatically transcribe recordings of meetings and presentations. When analyzing handwritten notes and voice memos, the generation AI can understand the context and complement the relevant information. For example, it can automatically reference digital materials related to handwritten notes. This allows questions to be generated from a wider variety of information sources by analyzing non-digital data as well.

[0098] The question generation unit can automatically translate presentations and presentation materials created in different languages ​​and generate questions that support multiple languages. For example, the generation AI automatically translates a presentation created in a different language and analyzes the content. For example, it translates English materials into Japanese and analyzes them. The question generation unit also generates questions that support multiple languages ​​based on the content of the automatically translated presentation. For example, it generates questions in both English and Japanese. The question generation unit also analyzes presentation materials created in different languages ​​and generates questions that support each language. For example, it analyzes French materials and generates questions in French. This allows the generation of multilingual questions, making it possible to handle materials in different languages.

[0099] The question generation unit can analyze the emotions felt by the document creator when they are entering the document, and adjust the tone and content of the questions based on those emotions. For example, the generation AI can analyze the emotions felt by the document creator when they are entering the document, and adjust the tone of the questions based on those emotions. For example, it can generate positive questions for a creator who has positive emotions. The question generation unit can also analyze the emotions of the document creator in real time, and adjust the content of the questions based on those emotions. For example, it can generate questions that will relax a nervous creator. The question generation unit can also generate questions that the document creator can answer most effectively based on the emotion analysis results. For example, it can use specific or abstract questions depending on the creator's emotions. This makes it possible to generate more appropriate questions by adjusting the questions based on the creator's emotions.

[0100] The question generation unit can also include counterarguments to the content of the document or questions from different perspectives. For example, the generation AI generates questions that include counterarguments to the content of the document. For example, it generates a question such as, "How is the reliability of this data ensured?" The question generation unit also generates questions from different perspectives to the content of the document. For example, it generates a question such as, "Are there any alternatives to this approach?" The question generation unit also generates questions that are critical of the content of the document. For example, it generates a question such as, "Are there any factors that were not considered in the process of reaching this conclusion?" This makes it possible to evaluate the document from multiple perspectives by including counterarguments and questions from different perspectives.

[0101] The question generation unit can generate topical questions by referring to the latest research results and industry trends related to the content of the document. For example, the generation AI refers to the latest research results and generates questions related to the content of the document. For example, it generates a question such as, "What progress has there been in recent research?" The question generation unit also refers to industry trends and generates topical questions related to the content of the document. For example, it generates a question such as, "How are you responding to current market trends?" The question generation unit also refers to the latest technological trends and generates questions related to the content of the document. For example, it generates a question such as, "How do you think new technology will affect this project?" In this way, topical questions can be generated by referring to the latest research results and industry trends.

[0102] The question generation unit can predict the audience's emotional reaction to the content of the material and generate questions based on that reaction. For example, the generative AI predicts the audience's emotional reaction to the content of the material and generates questions based on that reaction. For example, it generates questions for topics that are expected to elicit strong emotional reactions. The question generation unit also predicts the audience's emotional reaction and adjusts the tone of the questions based on that reaction. For example, it generates careful questions for emotionally sensitive topics. The question generation unit also generates questions that are likely to attract the audience's attention based on the emotional reaction prediction results. For example, it generates questions that elicit positive emotions or questions that stimulate discussion. In this way, more effective questions can be generated by predicting the audience's emotional reactions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The input section is where you input presentation and presentation materials. For example, input can be in a variety of formats, such as text files, PDF files, or slide-format files. Step 2: The question generation unit generates questions based on the content of the presentation or presentation materials input by the input unit. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the presentation or presentation materials and generate 100 related questions. The generation AI can also use a multimodal generation AI to analyze the content of the presentation or presentation materials and generate questions. Step 3: The output unit outputs the questions generated by the question generator. For example, the generated questions are provided to the user in a text format or a list format. The output unit can also save the generated questions in the user's device.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting presentation and presentation materials; a question generation unit that generates questions based on the contents of the presentation or presentation materials input by the input unit; an output unit that outputs the question generated by the question generation unit; A system characterized by:

2. The question generation unit When analyzing the content of the presentations and presentation materials, refer to relevant external databases.

2. The system of claim 1.

3. The question generation unit Refer to the author's previous presentations and presentation materials to assess consistency and evolution 2. The system of claim 1.

4. The question generation unit Analyze the emotional impact of the content of the material on the audience and generate questions based on the results 2. The system of claim 1.

5. The question generation unit It also makes it possible to analyze non-digital data such as handwritten notes and voice memos.

2. The system of claim 1.

6. The question generation unit Automatically translate presentations and presentation materials written in different languages ​​and generate multilingual questions 2. The system of claim 1.

Images