system

Abstract

The system according to the embodiment aims to automatically generate questions for presentations and presentation materials. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs presentation or presentation materials. The analysis unit analyzes the materials input by the reception unit. The generation unit generates questions based on the content of the materials analyzed by the analysis unit. The provision unit provides the questions generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques are unable to automatically generate questions for presentations or presentation materials, and there is room for improvement.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs presentation or presentation materials. The analysis unit analyzes the materials input by the reception unit. The generation unit generates questions based on the content of the materials analyzed by the analysis unit. The provision unit provides the questions generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate 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) A question generation system according to an embodiment of the present invention uses an AI to automatically generate questions for presentations and presentation materials. In this question generation system, a user inputs the presentation or presentation materials into an AI, which then analyzes the input materials and generates questions based on the content. The generated questions are provided to the user, helping them prepare and improve their presentation. This mechanism allows users to gain a deeper understanding of the content of their presentations and presentation materials and deliver more effective presentations. For example, when a user inputs presentation or presentation materials into an AI, they can input materials in various formats, such as slide presentations and PDF presentation materials. Care must be taken to ensure the content of the materials is input accurately. Next, the AI ​​analyzes the input materials. The AI ​​understands the content of the materials and extracts important points and keywords. For example, it identifies the theme, purpose, and main discussion points of the presentation. This analysis process allows the AI ​​to grasp the overall picture of the materials and lays the foundation for generating appropriate questions. After the analysis is complete, the AI ​​generates questions based on the content of the materials. The generated questions are related to the key points and discussion points of the materials and are useful for users in preparing their presentations. For example, questions related to the purpose of the presentation or questions addressing the main discussion points may be generated. The generated questions are provided to the user, who can use these questions as a reference to review and improve the content of their presentation. For example, by preparing answers to the generated questions, users can gain a deeper understanding of the content of their presentation and deliver an effective presentation. This system allows users to gain a deeper understanding of the content of their presentations and presentation materials and deliver effective presentations. For example, by considering answers to questions generated by the AI ​​during the preparation stage of a presentation, users can make the content of their presentation clearer and communicate it more effectively to the audience. In addition, by reviewing the content of their presentation based on the generated questions, the quality of their materials can be improved.This allows the question generation system to help users gain a deeper understanding of the content of the presentation and presentation materials, enabling them to give effective presentations.

[0029] A question generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit inputs presentation or presentation materials. Examples of presentation or presentation materials include, but are not limited to, slide format, PDF format, and text file format. The receiving unit can, for example, read a presentation in slide format and read presentation materials in PDF format. The receiving unit can also read materials in text file format. The analysis unit analyzes the materials input by the receiving unit. The analysis unit understands the content of the materials using, for example, natural language processing technology and extracts important points and keywords. The analysis unit identifies, for example, the theme, purpose, and main discussion points of the materials. The analysis unit can also extract frequently occurring keywords in the materials using keyword extraction technology. The generation unit generates questions based on the content of the materials analyzed by the analysis unit. The generation unit generates, for example, questions based on important points or discussion points. The generation unit can generate, for example, questions related to the purpose of the presentation or questions related to the main discussion points. The providing unit provides the questions generated by the generation unit to a user. The providing unit can provide questions in real time, for example. The providing unit can also provide questions by email. For example, the providing unit can send the generated questions to a user's email address. This allows the question generation system according to the embodiment to enable the user to gain a deeper understanding of the content of the presentation or presentation materials and to deliver an effective presentation.

[0030] The reception unit can input materials in various formats, such as slide presentations or PDF presentation materials. For example, the reception unit can read slide presentations and PDF presentation materials. The reception unit can also read materials in text file format. For example, when reading a slide presentation, the reception unit can accurately read the contents of the slides. When reading PDF presentation materials, the reception unit can accurately analyze the contents of the PDF file. Furthermore, when reading text file materials, the reception unit can accurately analyze the text data. This makes it possible to input materials in various formats. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data obtained by reading a slide presentation into a generation AI to analyze the contents of the slides.

[0031] The analysis unit can understand the content of the document and extract important points and keywords. The analysis unit can understand the content of the document and extract important points and keywords, for example, using natural language processing technology. For example, the analysis unit can identify the theme, purpose, and main discussion points of the document. The analysis unit can also extract frequently occurring keywords in the document using keyword extraction technology. For example, the analysis unit can summarize the content of the document and extract important points. The analysis unit can also extract particularly important keywords in the document and perform analysis based on them. This makes it possible to understand the content of the document and extract important points and keywords. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the content of the document into a generation AI and have the generation AI extract important points and keywords.

[0032] The generation unit can generate questions related to important points and discussion points in the material. The generation unit generates, for example, questions based on important points or questions based on discussion points. For example, the generation unit can generate questions related to the purpose of the presentation or questions related to major discussion points. The generation unit can also generate questions that are useful for a user in preparing for a presentation based on the content of the material. For example, the generation unit generates questions that a user should consider based on important points in the material. The generation unit can also generate questions that a user should prepare based on discussion points in the material. This makes it possible to generate questions related to important points and discussion points in the material. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the content of the material into a generation AI and cause the generation AI to generate questions.

[0033] The providing unit can provide the generated question to the user. The providing unit can provide the question, for example, in real time. The providing unit can also provide the question by email. For example, the providing unit can send the generated question to the user's email address. The providing unit can also provide the question through a web application or a mobile application. For example, the providing unit can display the generated question on a web application so that the user can access the question in real time. The providing unit can also provide the question through a mobile application so that the user can access the question on a smartphone or tablet. In this way, the generated question can be provided to the user. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the generated question to a generation AI and cause the generation AI to provide the question.

[0034] The reception unit can analyze the user's past document input history and select an appropriate input method. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method based on the format of documents the user has previously input. Furthermore, the reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This makes it possible to analyze the user's past document input history and select the optimal input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0035] When inputting materials, the reception unit can filter the materials based on the user's current project or area of ​​interest. For example, the reception unit inputs only materials related to the project the user is currently working on. The reception unit can also preferentially input highly relevant materials based on the user's area of ​​interest. Furthermore, the reception unit can filter related materials by referring to the user's past project history. This makes it possible to filter materials based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data and area of ​​interest data to the generation AI and have the generation AI perform the filtering.

[0036] When inputting materials, the reception unit can prioritize inputting highly relevant materials taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting materials related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize inputting materials related to the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting materials related to the user's home. This makes it possible to prioritize inputting highly relevant materials based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant materials.

[0037] When inputting materials, the reception unit can analyze the user's social media activity and input related materials. The reception unit can input related materials based on, for example, content shared by the user on social media. The reception unit can also input related materials based on the content of accounts the user follows on social media. Furthermore, the reception unit can also input related materials based on the content of groups the user participates in on social media. This makes it possible to input related materials based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related materials.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on documents with high importance. The analysis unit can also perform a simplified analysis on documents with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the document. This makes it possible to adjust the level of detail of the analysis based on the importance of the document. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input document importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the material. For example, the analysis unit applies a technical analysis algorithm to technical materials. The analysis unit can also apply a business analysis algorithm to business-related materials. Furthermore, the analysis unit can apply an academic analysis algorithm to academic materials. This makes it possible to apply different analysis algorithms depending on the category of the material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input material category data into the generation AI and have the generation AI apply the analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the documents. For example, the analysis unit prioritizes analysis of documents with an approaching deadline. The analysis unit can also postpone analysis of documents with a more distant submission date. Furthermore, the analysis unit can adjust the priority of analysis according to the submission date of the documents. This makes it possible to determine the priority of analysis based on the submission date of the documents. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document submission date data into the generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the materials. For example, the analysis unit prioritizes analysis of highly relevant materials. The analysis unit can also postpone analysis of less relevant materials. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the materials. This makes it possible to adjust the order of analysis based on the relevance of the materials. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the materials to the generation AI and have the generation AI adjust the order of analysis.

[0042] The generation unit can adjust the level of detail of the question based on the importance of the material when generating a question. For example, the generation unit generates a detailed question for a material with a high level of importance. The generation unit can also generate a simplified question for a material with a low level of importance. Furthermore, the generation unit can adjust the level of detail of the question according to the importance of the material. This makes it possible to adjust the level of detail of the question based on the importance of the material. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input material importance data into the generation AI and cause the generation AI to adjust the level of detail of the question.

[0043] When generating questions, the generation unit can apply different question generation algorithms depending on the category of the material. For example, the generation unit applies an algorithm that generates technical questions to technical materials. The generation unit can also apply an algorithm that generates business questions to business-related materials. The generation unit can also apply an algorithm that generates academic questions to academic materials. This makes it possible to apply different question generation algorithms depending on the category of the material. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input material category data into the generation AI and cause the generation AI to apply a question generation algorithm.

[0044] When generating questions, the generation unit can determine the priority of questions based on the submission date of materials. For example, the generation unit prioritizes generating questions for materials with an upcoming deadline. The generation unit can also postpone questions for materials with a distant submission date. Furthermore, the generation unit can adjust the priority of questions according to the submission date of materials. This makes it possible to determine the priority of questions based on the submission date of materials. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the submission date of materials into the generation AI and have the generation AI determine the priority of questions.

[0045] When generating questions, the generation unit can adjust the order of questions based on the relevance of the materials. For example, the generation unit generates questions with priority for highly relevant materials. The generation unit can also postpone questions for less relevant materials. Furthermore, the generation unit can adjust the order of questions according to the relevance of the materials. This makes it possible to adjust the order of questions based on the relevance of the materials. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input relevance data of materials to the generation AI and cause the generation AI to adjust the order of questions.

[0046] When providing a question, the providing unit can select an appropriate provision method by referring to the user's past answer history. For example, the providing unit preferentially selects a provision method that the user has previously preferred. The providing unit can also suggest an optimal provision method based on the user's past answer history. Furthermore, the providing unit can analyze the user's past answer history and select the most effective provision method. This makes it possible to select an optimal provision method based on the user's past answer history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's answer history data into the generation AI and cause the generation AI to select a provision method.

[0047] When providing a question, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can select a delivery method that includes detailed information. This makes it possible to select the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select a delivery method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The question generation system may further include a feedback unit. The feedback unit collects answers provided by users to questions generated by the system and provides feedback to the analysis unit. For example, if a user provides a detailed answer to a question generated by the system, the feedback unit transmits the answer to the analysis unit, and the analysis unit can reflect the information in the next question generation. The feedback unit may also instruct the generation unit to adjust the difficulty and level of detail of the question based on the quality and content of the user's answer. Furthermore, the feedback unit may accumulate the user's answer history and use it as long-term learning data. This allows the question generation system to generate more accurate questions by utilizing the user's answer data.

[0050] The question generation system may further include a user profile unit. The user profile unit manages information such as the user's past presentations, presentation material history, interests, and areas of expertise. For example, the user profile unit may record the themes and content of past presentations given by the user and refer to them when generating questions next time. The user profile unit may also provide data for generating more specialized questions based on the user's areas of expertise. Furthermore, the user profile unit may prioritize generating highly relevant questions based on the user's interests. This allows the question generation system to utilize the user's profile information to generate more personalized questions.

[0051] The question generation system may further include a context analysis unit. The context analysis unit analyzes the environment and situation when a user is inputting presentation or presentation materials, and generates questions based on that information. For example, if a user is inputting materials during a meeting, the context analysis unit can generate questions taking into account the theme of the meeting and information about the participants. Also, if a user is participating in a specific event or seminar, the context analysis unit can generate questions related to the content of the event. Furthermore, the context analysis unit can generate questions at an appropriate time by taking into account the user's current work situation and schedule. This allows the question generation system to utilize the user's context information to generate more appropriate questions.

[0052] The question generation system may further include a real-time feedback unit. The real-time feedback unit provides feedback in real time when a user is inputting a presentation or presentation materials. For example, when a user is inputting materials, the real-time feedback unit may instantly generate questions based on the input content and present the questions to the user. The real-time feedback unit may also generate new questions based on the revisions made by the user when the user modifies the materials. Furthermore, the real-time feedback unit may generate additional questions in real time based on the audience's reactions and questions while the user is giving a presentation. This allows the question generation system to utilize real-time feedback to generate more effective questions.

[0053] The question generation system can further include a learning style analysis unit that generates questions based on the user's learning style. The learning style analysis unit analyzes the user's past answer history and learning patterns and generates questions based on that information. For example, if the user prefers visual learning, the learning style analysis unit can generate questions using diagrams and graphs. If the user prefers auditory learning, the learning style analysis unit can also generate questions using audio. Furthermore, if the user prefers practical learning, the learning style analysis unit can also generate questions using actual examples. This allows the question generation system to generate more effective questions by taking the user's learning style into consideration.

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

[0055] Step 1: The reception department inputs the presentation or presentation materials. Presentation or presentation materials can be in slide format, PDF format, text file, etc. The reception department can load materials in these formats. Step 2: The analysis unit analyzes the materials entered by the reception unit. The analysis unit uses natural language processing technology to understand the content of the materials and extract important points and keywords. For example, it identifies the material's theme, purpose, and main points of discussion and extracts frequently occurring keywords. Step 3: The generator generates questions based on the content of the materials analyzed by the analyzer. The generator generates questions based on important points and discussion points, and can generate questions related to the purpose of the presentation and main discussion points. Step 4: The providing unit provides the question generated by the generating unit to the user. The providing unit can provide the question in real time or by email. For example, the generated question can be sent to the user's email address.

[0056] (Example 2) A question generation system according to an embodiment of the present invention uses an AI to automatically generate questions for presentations and presentation materials. In this question generation system, a user inputs the presentation or presentation materials into an AI, which then analyzes the input materials and generates questions based on the content. The generated questions are provided to the user, helping them prepare and improve their presentation. This mechanism allows users to gain a deeper understanding of the content of their presentations and presentation materials and deliver more effective presentations. For example, when a user inputs presentation or presentation materials into an AI, they can input materials in various formats, such as slide presentations and PDF presentation materials. Care must be taken to ensure the content of the materials is input accurately. Next, the AI ​​analyzes the input materials. The AI ​​understands the content of the materials and extracts important points and keywords. For example, it identifies the theme, purpose, and main discussion points of the presentation. This analysis process allows the AI ​​to grasp the overall picture of the materials and lays the foundation for generating appropriate questions. After the analysis is complete, the AI ​​generates questions based on the content of the materials. The generated questions are related to the key points and discussion points of the materials and are useful for users in preparing their presentations. For example, questions related to the purpose of the presentation or questions addressing the main discussion points may be generated. The generated questions are provided to the user, who can use these questions as a reference to review and improve the content of their presentation. For example, by preparing answers to the generated questions, users can gain a deeper understanding of the content of their presentation and deliver an effective presentation. This system allows users to gain a deeper understanding of the content of their presentations and presentation materials and deliver effective presentations. For example, by considering answers to questions generated by the AI ​​during the preparation stage of a presentation, users can make the content of their presentation clearer and communicate it more effectively to the audience. In addition, by reviewing the content of their presentation based on the generated questions, the quality of their materials can be improved.This allows the question generation system to help users gain a deeper understanding of the content of the presentation and presentation materials, enabling them to give effective presentations.

[0057] A question generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit inputs presentation or presentation materials. Examples of presentation or presentation materials include, but are not limited to, slide format, PDF format, and text file format. The receiving unit can, for example, read a presentation in slide format and read presentation materials in PDF format. The receiving unit can also read materials in text file format. The analysis unit analyzes the materials input by the receiving unit. The analysis unit understands the content of the materials using, for example, natural language processing technology and extracts important points and keywords. The analysis unit identifies, for example, the theme, purpose, and main discussion points of the materials. The analysis unit can also extract frequently occurring keywords in the materials using keyword extraction technology. The generation unit generates questions based on the content of the materials analyzed by the analysis unit. The generation unit generates, for example, questions based on important points or discussion points. The generation unit can generate, for example, questions related to the purpose of the presentation or questions related to the main discussion points. The providing unit provides the questions generated by the generation unit to a user. The providing unit can provide questions in real time, for example. The providing unit can also provide questions by email. For example, the providing unit can send the generated questions to a user's email address. This allows the question generation system according to the embodiment to enable the user to gain a deeper understanding of the content of the presentation or presentation materials and to deliver an effective presentation.

[0058] The reception unit can input materials in various formats, such as slide presentations or PDF presentation materials. For example, the reception unit can read slide presentations and PDF presentation materials. The reception unit can also read materials in text file format. For example, when reading a slide presentation, the reception unit can accurately read the contents of the slides. When reading PDF presentation materials, the reception unit can accurately analyze the contents of the PDF file. Furthermore, when reading text file materials, the reception unit can accurately analyze the text data. This makes it possible to input materials in various formats. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data obtained by reading a slide presentation into a generation AI to analyze the contents of the slides.

[0059] The analysis unit can understand the content of the document and extract important points and keywords. The analysis unit can understand the content of the document and extract important points and keywords, for example, using natural language processing technology. For example, the analysis unit can identify the theme, purpose, and main discussion points of the document. The analysis unit can also extract frequently occurring keywords in the document using keyword extraction technology. For example, the analysis unit can summarize the content of the document and extract important points. The analysis unit can also extract particularly important keywords in the document and perform analysis based on them. This makes it possible to understand the content of the document and extract important points and keywords. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the content of the document into a generation AI and have the generation AI extract important points and keywords.

[0060] The generation unit can generate questions related to important points and discussion points in the material. The generation unit generates, for example, questions based on important points or questions based on discussion points. For example, the generation unit can generate questions related to the purpose of the presentation or questions related to major discussion points. The generation unit can also generate questions that are useful for a user in preparing for a presentation based on the content of the material. For example, the generation unit generates questions that a user should consider based on important points in the material. The generation unit can also generate questions that a user should prepare based on discussion points in the material. This makes it possible to generate questions related to important points and discussion points in the material. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the content of the material into a generation AI and cause the generation AI to generate questions.

[0061] The providing unit can provide the generated question to the user. The providing unit can provide the question, for example, in real time. The providing unit can also provide the question by email. For example, the providing unit can send the generated question to the user's email address. The providing unit can also provide the question through a web application or a mobile application. For example, the providing unit can display the generated question on a web application so that the user can access the question in real time. The providing unit can also provide the question through a mobile application so that the user can access the question on a smartphone or tablet. In this way, the generated question can be provided to the user. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the generated question to a generation AI and cause the generation AI to provide the question.

[0062] The reception unit can estimate the user's emotions and adjust the timing of inputting materials based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide time for the user to relax. Furthermore, if the user is concentrating, the reception unit can also prompt the user to immediately input materials. Furthermore, if the user is tired, the reception unit can also adjust the input timing to allow the user to take a break. This allows the timing of inputting materials to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0063] The reception unit can analyze the user's past document input history and select an appropriate input method. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method based on the format of documents the user has previously input. Furthermore, the reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This makes it possible to analyze the user's past document input history and select the optimal input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0064] When inputting materials, the reception unit can filter the materials based on the user's current project or area of ​​interest. For example, the reception unit inputs only materials related to the project the user is currently working on. The reception unit can also preferentially input highly relevant materials based on the user's area of ​​interest. Furthermore, the reception unit can filter related materials by referring to the user's past project history. This makes it possible to filter materials based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data and area of ​​interest data to the generation AI and have the generation AI perform the filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of materials to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can postpone inputting less important materials. Furthermore, if the user is relaxed, the reception unit can also prioritize inputting more important materials. Furthermore, if the user is in a hurry, the reception unit can input the most important materials first. This allows the priority of materials to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0066] When inputting materials, the reception unit can prioritize inputting highly relevant materials taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting materials related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize inputting materials related to the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting materials related to the user's home. This makes it possible to prioritize inputting highly relevant materials based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant materials.

[0067] When inputting materials, the reception unit can analyze the user's social media activity and input related materials. The reception unit can input related materials based on, for example, content shared by the user on social media. The reception unit can also input related materials based on the content of accounts the user follows on social media. Furthermore, the reception unit can also input related materials based on the content of groups the user participates in on social media. This makes it possible to input related materials based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related materials.

[0068] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the way the analysis is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on documents with high importance. The analysis unit can also perform a simplified analysis on documents with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the document. This makes it possible to adjust the level of detail of the analysis based on the importance of the document. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input document importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the material. For example, the analysis unit applies a technical analysis algorithm to technical materials. The analysis unit can also apply a business analysis algorithm to business-related materials. Furthermore, the analysis unit can apply an academic analysis algorithm to academic materials. This makes it possible to apply different analysis algorithms depending on the category of the material. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input material category data into the generation AI and have the generation AI apply the analysis algorithm.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0072] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the documents. For example, the analysis unit prioritizes analysis of documents with an approaching deadline. The analysis unit can also postpone analysis of documents with a more distant submission date. Furthermore, the analysis unit can adjust the priority of analysis according to the submission date of the documents. This makes it possible to determine the priority of analysis based on the submission date of the documents. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document submission date data into the generation AI and have the generation AI determine the analysis priority.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the materials. For example, the analysis unit prioritizes analysis of highly relevant materials. The analysis unit can also postpone analysis of less relevant materials. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the materials. This makes it possible to adjust the order of analysis based on the relevance of the materials. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the materials to the generation AI and have the generation AI adjust the order of analysis.

[0074] The generation unit can estimate the user's emotions and adjust the way questions are phrased based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed questions. If the user is in a hurry, the generation unit can also generate concise questions. Furthermore, if the user is excited, the generation unit can generate visually stimulating questions. This allows the way questions are phrased to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0075] The generation unit can adjust the level of detail of the question based on the importance of the material when generating a question. For example, the generation unit generates a detailed question for a material with a high level of importance. The generation unit can also generate a simplified question for a material with a low level of importance. Furthermore, the generation unit can adjust the level of detail of the question according to the importance of the material. This makes it possible to adjust the level of detail of the question based on the importance of the material. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input material importance data into the generation AI and cause the generation AI to adjust the level of detail of the question.

[0076] When generating questions, the generation unit can apply different question generation algorithms depending on the category of the material. For example, the generation unit applies an algorithm that generates technical questions to technical materials. The generation unit can also apply an algorithm that generates business questions to business-related materials. The generation unit can also apply an algorithm that generates academic questions to academic materials. This makes it possible to apply different question generation algorithms depending on the category of the material. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input material category data into the generation AI and cause the generation AI to apply a question generation algorithm.

[0077] The generation unit can estimate the user's emotions and adjust the length of the questions to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short and to-the-point questions. The generation unit can also generate detailed questions if the user is relaxed. Furthermore, if the user is excited, the generation unit can generate visually stimulating questions. This allows the length of questions to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0078] When generating questions, the generation unit can determine the priority of questions based on the submission date of materials. For example, the generation unit prioritizes generating questions for materials with an upcoming deadline. The generation unit can also postpone questions for materials with a distant submission date. Furthermore, the generation unit can adjust the priority of questions according to the submission date of materials. This makes it possible to determine the priority of questions based on the submission date of materials. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the submission date of materials into the generation AI and have the generation AI determine the priority of questions.

[0079] When generating questions, the generation unit can adjust the order of questions based on the relevance of the materials. For example, the generation unit generates questions with priority for highly relevant materials. The generation unit can also postpone questions for less relevant materials. Furthermore, the generation unit can adjust the order of questions according to the relevance of the materials. This makes it possible to adjust the order of questions based on the relevance of the materials. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input relevance data of materials to the generation AI and cause the generation AI to adjust the order of questions.

[0080] The providing unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide questions in a simple, highly visible manner. Furthermore, if the user is relaxed, the providing unit can also provide questions in a manner that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide questions in a manner that focuses on the main points. This allows the way questions are presented to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] When providing a question, the providing unit can select an appropriate provision method by referring to the user's past answer history. For example, the providing unit preferentially selects a provision method that the user has previously preferred. The providing unit can also suggest an optimal provision method based on the user's past answer history. Furthermore, the providing unit can analyze the user's past answer history and select the most effective provision method. This makes it possible to select an optimal provision method based on the user's past answer history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's answer history data into the generation AI and cause the generation AI to select a provision method.

[0082] The providing unit can estimate the user's emotions and adjust the order in which questions are presented based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple questions first. Furthermore, if the user is relaxed, the providing unit can also provide detailed questions first. Furthermore, if the user is in a hurry, the providing unit can also provide questions that focus on the main points first. This makes it possible to adjust the order in which questions are presented according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0083] When providing a question, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can select a delivery method that includes detailed information. This makes it possible to select the optimal delivery method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information to the generation AI and cause the generation AI to select a delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs presentation or presentation materials. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the materials using natural language processing technology. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a question based on the analyzed content. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated question to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs presentation or presentation materials. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the materials using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated questions to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs presentation and presentation materials. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the materials using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated questions to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs presentation or presentation materials. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the materials using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a question based on the analyzed content. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated question to the user.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The question generation system may further include a feedback unit. The feedback unit collects answers provided by users to questions generated by the system and provides feedback to the analysis unit. For example, if a user provides a detailed answer to a question generated by the system, the feedback unit transmits the answer to the analysis unit, and the analysis unit can reflect the information in the next question generation. The feedback unit may also instruct the generation unit to adjust the difficulty and level of detail of the question based on the quality and content of the user's answer. Furthermore, the feedback unit may accumulate the user's answer history and use it as long-term learning data. This allows the question generation system to generate more accurate questions by utilizing the user's answer data.

[0086] The question generation system may further include a user profile unit. The user profile unit manages information such as the user's past presentations, presentation material history, interests, and areas of expertise. For example, the user profile unit may record the themes and content of past presentations given by the user and refer to them when generating questions next time. The user profile unit may also provide data for generating more specialized questions based on the user's areas of expertise. Furthermore, the user profile unit may prioritize generating highly relevant questions based on the user's interests. This allows the question generation system to utilize the user's profile information to generate more personalized questions.

[0087] The question generation system may further include an emotion feedback unit. The emotion feedback unit collects the emotions felt by the user in response to the generated question and provides feedback to the analysis unit. For example, if the user feels stressed by the generated question, the emotion feedback unit transmits the information to the analysis unit, which can then generate a question that reduces stress the next time the question is generated. Furthermore, if the user expresses positive emotions in response to a question, the emotion feedback unit may use the information to adopt a similar approach the next time the question is generated. Furthermore, the emotion feedback unit may accumulate user emotion data and analyze long-term emotion trends. This allows the question generation system to utilize the user emotion data to generate more user-friendly questions.

[0088] The question generation system may further include a context analysis unit. The context analysis unit analyzes the environment and situation when a user is inputting presentation or presentation materials, and generates questions based on that information. For example, if a user is inputting materials during a meeting, the context analysis unit can generate questions taking into account the theme of the meeting and information about the participants. Also, if a user is participating in a specific event or seminar, the context analysis unit can generate questions related to the content of the event. Furthermore, the context analysis unit can generate questions at an appropriate time by taking into account the user's current work situation and schedule. This allows the question generation system to utilize the user's context information to generate more appropriate questions.

[0089] The question generation system may further include a real-time feedback unit. The real-time feedback unit provides feedback in real time when a user is inputting a presentation or presentation materials. For example, when a user is inputting materials, the real-time feedback unit may instantly generate questions based on the input content and present the questions to the user. The real-time feedback unit may also generate new questions based on the revisions made by the user when the user modifies the materials. Furthermore, the real-time feedback unit may generate additional questions in real time based on the audience's reactions and questions while the user is giving a presentation. This allows the question generation system to utilize real-time feedback to generate more effective questions.

[0090] The question generation system can further include an emotion analysis unit. The emotion analysis unit analyzes the emotions of the user when inputting presentation or presentation materials, and generates questions based on that information. For example, if the user is nervous, the emotion analysis unit can generate questions that will relax the user. Also, if the user is excited, the emotion analysis unit can generate questions that make use of that excitement. Furthermore, if the user is tired, the emotion analysis unit can generate simple, easy-to-answer questions. This allows the question generation system to generate more appropriate questions by taking the user's emotions into consideration.

[0091] The question generation system can further include a learning style analysis unit that generates questions based on the user's learning style. The learning style analysis unit analyzes the user's past answer history and learning patterns and generates questions based on that information. For example, if the user prefers visual learning, the learning style analysis unit can generate questions using diagrams and graphs. If the user prefers auditory learning, the learning style analysis unit can also generate questions using audio. Furthermore, if the user prefers practical learning, the learning style analysis unit can also generate questions using actual examples. This allows the question generation system to generate more effective questions by taking the user's learning style into consideration.

[0092] The question generation system can further estimate the user's emotions and adjust the difficulty of the questions based on the estimated emotions. For example, if the user is feeling stressed, it can generate simple questions. If the user is relaxed, it can generate difficult questions. Furthermore, if the user is concentrating, it can generate detailed questions. In this way, the question generation system can adjust the difficulty of the questions according to the user's emotions.

[0093] The question generation system can further estimate the user's emotions and adjust the timing of providing questions based on the estimated emotions. For example, if the user is tired, the system can delay providing questions to allow for a break. If the user is concentrating, the system can provide questions immediately. Furthermore, if the user is feeling stressed, the system can provide questions at a time when the user can relax. This allows the question generation system to adjust the timing of providing questions according to the user's emotions.

[0094] The question generation system can further estimate the user's emotions and adjust the format of the question based on the estimated emotions. For example, if the user is nervous, a simple, highly visible question can be generated. If the user is relaxed, a question containing detailed information can be generated. Furthermore, if the user is in a hurry, a short question that gets straight to the point can be generated. In this way, the question generation system can adjust the format of the question according to the user's emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The reception department inputs the presentation or presentation materials. Presentation or presentation materials can be in slide format, PDF format, text file, etc. The reception department can load materials in these formats. Step 2: The analysis unit analyzes the materials entered by the reception unit. The analysis unit uses natural language processing technology to understand the content of the materials and extract important points and keywords. For example, it identifies the material's theme, purpose, and main points of discussion and extracts frequently occurring keywords. Step 3: The generator generates questions based on the content of the materials analyzed by the analyzer. The generator generates questions based on important points and discussion points, and can generate questions related to the purpose of the presentation and main discussion points. Step 4: The providing unit provides the question generated by the generating unit to the user. The providing unit can provide the question in real time or by email. For example, the generated question can be sent to the user's email address.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception desk for inputting presentation or presentation materials; an analysis unit that analyzes the materials input by the reception unit; a generation unit that generates questions based on the content of the materials analyzed by the analysis unit; a providing unit that provides the question generated by the generating unit; Equipped with system.

2. The reception unit Input slide presentations, PDF presentation materials, and various other materials.

2. The system of claim 1.

3. The analysis unit Understand the content of the materials and extract important points and keywords 2. The system of claim 1.

4. The generation unit Generate questions related to key points and discussion points in the material 2. The system of claim 1.

5. The providing unit Present the generated question to the user 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of inputting materials based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past data entry history and select the appropriate entry method 2. The system of claim 1.

8. The reception unit Filter documents as they are entered based on your current projects and interests 2. The system of claim 1.

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