Explanation Material Presentation Assistance System

Abstract

Provided is a system that can display desirable information and perform a presentation based on the desirable information based on a user's touching story. 【Solution means】An explanation material presentation assistance system 1 having a material storage unit 3 that stores explanation materials, a related word storage unit 5 that stores one or more related words in association with each page of the explanation materials, a topic storage unit 7 that stores a plurality of topic flows related to the explanation materials in association with the related words, an input term analysis unit 11 that analyzes an input term, and based on the input term, analyzes a topic at the current time in a plurality of topic flows and outputs a page of the explanation material related to the next topic after the current time. A next page output unit 13, and a selection page output unit 15 that outputs a page of the explanation material based on a selection instruction input when there are a plurality of next topics of the topic at the current time.

Description

Technical Field

[0001] This invention relates to an auxiliary system for presenting explanatory materials.

Background Art

[0002] Japanese Patent No. 7102035 describes an explanation support system. This system includes a material storage unit that stores explanatory materials and a plurality of related words related to the explanatory materials, and stores display information to be displayed on a display unit based on combinations of related words. It also analyzes voice terms, which are terms included in voice information related to the explanatory materials, and the main body of the voice terms, identifies which of the plurality of related words the voice terms of a specific main body are, reads out the display information from the display information storage unit using the information related to the identified related words, and causes it to be displayed on the display unit. According to this system, based on voice, after analyzing a specific main body, display information suitable for the specific main body can be displayed.

[0003] This explanation support system is preferable because it can analyze a specific main body in a situation where a plurality of voices are input. However, displaying desirable information based on the user's interests in a specific main body or conducting a presentation based on the desirable information is not necessarily intended.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] An object of the present invention is to provide a system that can display desirable information based on the user's interests and conduct a presentation based on the desirable information.

Means for Solving the Problems

[0006] The explanatory material presentation assistance system 1 for solving the above problems includes a material storage unit 3 that stores explanatory materials, a related word storage unit 5 that stores one or more related words in association with each page of the explanatory materials, a topic storage unit 7 that stores the flow of a plurality of topics related to the explanatory materials in association with the related words, an input term analysis unit 11 that analyzes the input terms, a next page output unit 13 that analyzes the current topic in the flow of a plurality of topics based on the input terms and outputs the page of the explanatory material related to the next topic after the current time point, and a selection page output unit 15 that outputs the page of the explanatory material based on the input selection instruction when there are a plurality of next topics after the current topic.

Effect of the Invention

[0007] According to this invention, when there is a branch in the topic based on the page of the material, by allowing the user to select the branch, a system can be provided that can display desired information based on the user's interests and perform a presentation based on the desired information.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Embodiments for Carrying Out the Invention

[0009] FIG. 1 is a block diagram for explaining an example of an explanatory material presentation assistance system. As shown in FIG. 1, the explanatory material presentation assistance system 1 includes a material storage unit 3, a related word storage unit 5, a topic storage unit 7, an input term analysis unit 11, a next page output unit 13, and a selected page output unit 15. In the example shown in FIG. 1, the system 1 further includes a user learning unit 17. This system can output a page selected based on the admiration on the speaker side. This system usually includes a server and a plurality of terminals. That is, this system may be implemented by one or more information processing terminals (computers). An assumed usage example of this system is that the speaker's terminal is connected so as to be able to exchange information with the terminals of one or more listeners. The speaker's terminal receives assistance for presenting explanatory materials based on this system 1. The speaker's terminal is registered in this system 1, and it is preferable that the information assisted according to the speaker is customized by a learned model described later.

[0010] A computer has an input unit, an output unit, a control unit, an arithmetic unit, and a storage unit, and each element is connected by a bus or the like so that information can be exchanged. For example, the storage unit may store a control program or various types of information. When predetermined information is input from the input unit, the control unit reads out the control program stored in the storage unit. Then, the control unit appropriately reads out the information stored in the storage unit and transmits it to the arithmetic unit. Also, the control unit appropriately transmits the input information to the arithmetic unit. The arithmetic unit performs arithmetic processing using the received various types of information and stores it in the storage unit. The control unit reads out the arithmetic result stored in the storage unit and outputs it from the output unit. In this way, various processes and steps are executed. Those that execute these various processes are each unit and each means. The computer may have a processor, and the processor may realize various functions and various steps. The computer may be a stand-alone. Part of the functions of the computer may be distributed between a server and a terminal. In that case, it is preferable that the server and the terminal can exchange information through a network such as the Internet or an intranet. The computer may include a processor and a memory connected to the processor. And the memory stores instructions, and when the instructions are executed by the processor, they may cause the computer to perform various steps or cause the computer to function as various elements. The computer may also build a learning model by providing various types of training data and realize various arithmetic operations through machine learning. In this case, the computer may execute various analyses and examinations using the learning model created by machine learning and deep learning of AI (artificial intelligence).

[0011] The system usually has a display unit (not shown). The display unit is an element for displaying various information based on a computer. A monitor or a display which is a type of output unit of a computer, or a touch panel of a smartphone functions as the display unit. The display unit may be a projector. When giving a presentation, in addition to the monitor of a computer or a tablet functioning as the display unit, the presentation materials may also be projected by a projector. In this case, as will be described later, not only the presentation materials but also either or both of the information regarding the word order of related words and the explanatory text may be displayed on the monitor.

[0012] Figure 2 is a conceptual diagram showing a certain assumed usage example of the explanatory material presentation assistance system. In this example, the speaker's terminal 21 is connected to the audience's terminal 23 via the Internet. This individual usage example may be a web conference, may be for answering questions from the audience who are users, or may be an example where an MR explains to doctors who are the audience based on materials. On the speaker's terminal 21, the face of the speaker photographed by a camera may be displayed, or a certain page of the materials may be displayed. These may also be displayed on the audience's terminal 23. On the other hand, on the speaker's terminal 21, as will be described later, various information for selecting the next page may be displayed. In the example of Figure 2, a thumbnail 25 showing a page of the presentation materials is displayed on the display unit of the speaker's terminal 21, and related words are also displayed in the related word display column 27 regarding that page. When the speaker generates the related words or performs a process for outputting the page displayed as a thumbnail, the page may also be output to the audience's terminal 23 and displayed on the display unit of the audience's terminal 23.

[0013] Material storage unit 3 The material storage unit 3 is an element for storing explanatory materials. The material storage unit 3 functions, for example, as a database for storing materials prepared in advance by the system 1. The material storage unit 3 stores the entire presentation material and each page of the presentation material. The storage unit of the computer functions as the material storage unit 3. Examples of presentation materials are materials created in PowerPoint (registered trademark) or pdf (registered trademark). A presentation material means, for example, the entire set of materials (a certain file) created by software such as PowerPoint (registered trademark), or a specific page. For example, an identification number or ID is assigned to each presentation material. And in the material storage unit 3, a plurality of related words are stored in association with the assigned information (identification number or ID), or the identification number and page number (slide number). In this way, each presentation material or each page (each slide) of each material is associated with a plurality of related words related to the respective presentation material and stored.

[0014] An example of a presentation material is a PowerPoint material regarding the new drug X for diabetes. And examples of a plurality of related words regarding the presentation material are "diabetes", "X", "dosage", "side effects", "dizziness", "sleepiness", "(pregnant women who should not be administered)", and "(persons under 19 years old who should not be administered)", which are related words regarding the PowerPoint material. These are stored in the material storage unit 3, for example, in association with the identification number (and each page number) of the presentation material.

[0015] Related word storage unit 5 The related word storage unit 5 is an element for storing one or more related words in association with each page of the explanatory material. In the related word storage unit 5, related words corresponding to each page are registered, whereby each page of the material can be associated with the topic. For each page, the order of appearance of the related words may also be stored. The related words may be keywords or topics, or synonyms of the keywords.

[0016] Topic Memory Unit 7 The topic memory unit 7 is an element for memorizing the flows of a plurality of topics related to explanatory materials in association with related words. The topic memory unit 7, for example, structurally records the flows of a plurality of topics. It is preferable that the transitions between topics are clearly defined in terms of order and branching based on related words. And for each topic included in the flow of topics, it is preferable that it is memorized in association with related words and each page. In other words, it is preferable that the corresponding page or topic is read out based on related words.

[0017] FIG. 3 is a conceptual diagram for explaining the flow of topics regarding a page of explanatory materials. For example, the topic memory unit 7 may memorize the flows of a plurality of topics in association with the entire explanatory materials. Also, for example, the topic memory unit 7 may memorize the flows of a plurality of topics in association with each page of the explanatory materials. In the flow of topics, for example, related words may exist for each utterance constituting the topic. And the flow of topics may be formed based on the registration order of related words. In the example of FIG. 3, for a certain page read out in association with a certain related word, examples are shown where there are four major flows of topics and branches exist for each of them. For example, based on this page, if the term based on the input voice is a related word of A4, and the term analyzed based on the subsequent input voice is also a related word after A4, the flow of topics is uniform and the next page of this page is also determined. For this reason, for example, when an explanation is made based on this page and the input voice is based on the flow of A4, after the term related to the last related word is input, at a predetermined timing, the next page may be read out and output. On the other hand, for example, when the first related word of the term based on the input voice is like A1, options may be shown and the next page may be displayed based on the selection information of the shown options.

[0018] Input Term Analysis Unit 11 The input term analysis unit 11 is an element for analyzing the voice input to the system and obtaining the terms included in the voice. The input term analysis unit 11 is implemented, for example, by a control program stored in the storage unit. The input term analysis unit 11 can be implemented using a known voice recognition device. The voice input to the system is usually analog information. The input term analysis unit 11 may convert the analog information into digital information and store it in a form that can be processed by a computer as a term. For example, the voice information input to the system 1 is read. Then, the control unit of the computer reads the control program stored in the storage unit and causes the arithmetic unit to analyze the read voice information. At this time, a plurality of terms stored in the storage unit and the voice information of those terms may be read and the terms may be analyzed. Then, the analyzed terms may be appropriately stored in the storage unit. In this way, the input term analysis unit 11 can analyze the terms included in the voice input to the system.

[0019] Next page output unit 13 The next page output unit 13 is an element for analyzing the current topic in the flow of a plurality of topics based on the input term and outputting a page of explanatory materials related to the next topic after the current time. For example, the next page output unit 13 grasps up to which part the conversation has progressed regarding the current topic based on the input term. Moreover, when the page of the explanatory materials related to the next topic is determined, the next page output unit 13 outputs the next page at the timing when the explanation regarding the current topic related to the current page has ended. When the current page is page A1 and the flow of related words included in the topic related to that page is A1A, A1B, A1C, and A1D, the next page output unit 13 may examine whether the term analyzed by the input term analysis unit 11 is related to any of the related words and grasp up to which part the conversation has progressed regarding the current topic. Then, when the next page output unit 13 determines that the term input to the system is related in the order of A1A, A1B, A1C, and A1D, since the conversation is being carried out along the assumed conversation flow for the current topic, page A2, which is the next page after page A1 in the explanatory materials, is displayed, and related words along the conversation flow stored in association with page A2 may be read out. Also, even when the voice is automatically outputting the topic, the next page determined in advance at the timing when the current topic ends may be automatically displayed, and the voice related to the next page may be automatically output.

[0020] Selected page output unit 15 When there are multiple pages of explanatory materials related to the next topic after the topic at the current time (the page currently displayed on the current display section), the selection page output unit 15 is an element for outputting the page of the explanatory materials based on the input selection instruction. When the topic branches into multiple topics, the next page is output based on the selection instruction. When there are multiple next topics after the topic at the current time, the selection page output unit 15 may output related words related to each of the next topics, and output the page of the explanatory materials based on the selection instruction input in relation to the related words. For example, a plurality of related words may be displayed in the related word display column 27. After the topic about the current page, for example, if there are topics (next pages) related to related words such as B1, B2, and B3, on the speaker's terminal 21 and the audience's terminal 23, while the page is being displayed, those related words may be displayed in the related word display column 27 of the speaker's terminal 21. Then, when the speaker speaks like "B1", after the topic about the current page ends, the page related to B1 may be quickly displayed. Also, the system 1 may store voices such as "bee one" and perform voice processing so that the voice related to the related word is not output to the audience's terminal 23 when the related word displayed on the display section is input from the speaker's terminal 21. In this way, an appropriate page can be selected without the audience realizing that the next page has been instructed by voice.

[0021] The selection page output unit 15 is When there are multiple next topics after the topic at the current time and there are pages of explanatory materials corresponding to each of the multiple next topics, thumbnails of each page may be output, and the page of the explanatory materials based on the selection instruction input in relation to a specific page among each page may be output.

[0022] FIG. 4 is a conceptual diagram showing an example in which thumbnail display is performed. In this example, a plurality of thumbnails 25 are displayed on the display unit of the speaker's terminal 21. In this example, the thumbnails are for displaying the next page. And in this example, a display for reading out the page associated with these thumbnails is made on the thumbnails. For example, when a voice input of "at a glance 1" is made, the upper left slide will be output to the audience's terminal (and the speaker's terminal). The thumbnail may be reduced, for example, by reducing the resolution of the photo for the photographic information included in each slide and making it a predetermined reduced size. Also, for example, characters smaller than a predetermined character size may be deleted, and for characters larger than the predetermined character size, they may be reduced to a size multiplied by a predetermined magnification and then displayed on the display unit. In this way, a deformed image of each slide can be obtained. And if the obtained deformed image can be displayed on a plurality of screens, thumbnail display can be performed. Thus, the images related to the plurality of slides displayed on the screen of the slide image may be a set of images obtained by reducing each slide.

[0023] The input selection instruction may be a voice input regarding a pre-registered selection. Examples of selections include going back to the previously displayed page, advancing to the next page, ending the presentation, and going back to the first page. It is preferable that the voice inputted to the speaker-side input unit is canceled so that it cannot be heard by the audience side. For example, when the speaker wants to advance to the next page instead of the page displayed on the display unit, the speaker utters a voice such as "at mark, at mark", and when this voice is inputted to the system, the input term analysis unit 11 may analyze the term "at mark, plus". Then, the explanatory material presentation assistance system 1 may read out the instruction stored in association with "at mark, plus" from the instruction storage unit and perform predetermined control. For example, in this case, the next page of the current page will be displayed. Also, regarding this selection instruction of "at mark, plus", the voice output may be canceled out so that it cannot be heard by the audience side. The storage unit stores a selection command of "advance to the next page" in association with the voice of "at mark, plus", and when "at mark, plus" is inputted by voice, the selection command stored in association with the voice may be read out and the process corresponding to the selection command may be performed. It is preferable that this voice command is not included in the normal explanation.

[0024] Hereinafter, examples of commands (voice commands) by voice input will be described. When using a voice command during voice recognition, since there are no breaks in Japanese, it is preferable that the trigger of the voice command is a term not found in Japanese. An example of a voice command considering such circumstances is the at mark. The at mark may be changed as appropriate. However, terms, related words, keywords, etc. that appear in the explanation of the material are not suitable as triggers. It is preferable to be able to configure a selection command as appropriate, including the terms following the trigger of the voice command.

[0025] Examples of voice commands are as follows. At mark ~ (~ means that there is no voice input.) After a predetermined time (for example, 3 seconds) from the sound of the at-mark, perform RAG extraction and display the result. At-mark + number (for example, ichi) Display a thumbnail preview by number specification. Also, display the page that was displayed as a thumbnail on the next page. When implementing this function, it is preferable not to include numbers in related words and to make it impossible to perform RAG extraction on utterances such as "number ~~~". At-mark plus Display the next page of the preview. At-mark minus Display the previous page of the preview. At-mark at-mark Close the preview. The above are examples of voice commands. By using such voice commands, pages can be displayed based on the user's emotions. Also, at this time, the user learning unit 17 described later can propose candidates for the next page based on the flow of the conversation, so that materials that better reflect the user's emotions can be output.

[0026] User learning unit 17 It is preferable to further include a user learning unit 17 that learns the user's selection instruction information. This is related to the processing of the next page output unit 13 and the selection page output unit 15. FIG. 5 is a conceptual diagram for explaining the user learning unit. Using the information including the user's selection instruction information of the system 1 as learning data, input it into an artificial intelligence model, and construct a learned model that can estimate the candidates for the next page. The learning data of the learned model may include the flow of the topic (the appearance order of related words) and the selection instruction information in each branch. Further, either or both of the speaker's information and the audience's information may be further input as the learning data. When various data are input into the artificial intelligence model as learning data, it becomes possible to effectively output materials based on the user's emotion of the system 1 (such as the emotion of which page to display as the next page). At that time, by including the flow of the topic (the appearance order of related words) before and after the selection instruction information is input as learning data, it becomes possible to effectively learn the relationship between the flow of the topic and the selection in the branch. For example, the flow of the topic shown in FIG. 3 may be appropriately changed by the user learning unit 17. And when data corresponding to the learning data (for example, information regarding the flow of the topic before the branch) is input into the user learning unit 17 constructed in this way, it becomes possible to effectively output regarding the next page. In this way, by using the flow of the topic according to the appearance order of related words related to a plurality of pages as learning data and constructing a learned model, it becomes possible to accurately grasp the user's thought or emotion and construct a user learning unit 17 that can propose (output) the next page along the flow of the topic.

[0027] Either or both of the speaker's information and the audience's information may be stored in the memory unit of System 1. Examples of such information are employee number, name, gender, age, business performance, job title, place of origin, presentation evaluation, years of service, and years in charge. Examples of information about the presentation recipient are the scale of the hospital, the region of the hospital, whether it is a lecture or for a single doctor, and (in the case of for doctors) information about the doctor. Another example of information about the presentation recipient is the region of the lecture, the level of the attendees, the grade of the attendees, the number of attendees, the occupation of the attendees, the job content of the attendees, the years of service of the attendees, and the job title of the attendees.

[0028] When constructing an artificial intelligence model, it is preferable to use RAG (Retrieval-Augmented Generation). Using Retrieval-Augmented Generation, an artificial intelligence model can be constructed as follows. Step 1: Data Preparation Collection of Document Corpus First, collect the knowledge base (document corpus) to be used for retrieval. Examples of the knowledge base are the flow of past topics and the branch selection information at the branch. The collected information may be divided as necessary. Step 2: Preparation of Retrieval Model (Retriever) For example, Dense Retriever may be used to calculate the embedding vectors between the training data and the output. Specifically, pairs of training data and output data may be used to learn to maximize the similarity between the question embedding and the correct answer embedding. However, vector calculation may not be performed. Regarding the case where the proposal of the next page or the candidate for the next page by System 1 is correct and the case where it is incorrect, they may be fed back as answers to train the model. Step 3: Preparation of Generation Model (Generator) Selection of Pre-trained Generation Model Receive the retrieval results as input and train the model to generate answers corresponding to the questions. Step 4: Integration of RAG Model Integration of Retrieval Model and Generation Model

[0029] One invention described in this specification relates to a computer program and a computer-readable non-transitory information recording medium (such as a CD-ROM) storing the program. This program is basically a program readable by a computer for causing the computer to function as any of the above-described explanatory material presentation assistance systems. For example, when this program is installed in a computer, the computer can be caused to function as an explanatory material presentation assistance system 1 having a material storage unit 3, a related word storage unit 5, a topic storage unit 7, an input term analysis unit 11, a next page output unit 13, and a selected page output unit 15.

[0030] Hereinafter, an example of a process of explanatory material presentation assistance using the explanatory material presentation assistance system will be described. As described above, the explanatory material presentation assistance system 1 has a material storage unit 3, a related word storage unit 5, a topic storage unit 7, an input term analysis unit 11, a next page output unit 13, and a selected page output unit 15. Further, the system 1 preferably further has a user learning unit 17. The user learning unit 17 may be constructed by the method described above.

[0031] Suppose a speaker is explaining Page A using the speaker's terminal 21. The user learning unit 17 has learned the flow of Topic A on Page A. The pronunciation by a certain speaker is input into System 1, which analyzes it. System 1 analyzes the flow of a conversation including related words about Page A. System 1 inputs information about the conversation flow into the user learning unit 17. Then, the user learning unit 17 outputs only one candidate for the next page. Presumably, when a similar conversation flow occurred in the past, a specific next page was often used. In this case, the next page output unit 13 of System 1 outputs the next page related to Topic B, which is the next topic of Topic A. This next page (Page B) is also output to the listener's terminal. Then, Page B is displayed on the monitor of the listener's terminal. And if this selection is correct, the speaker, for example, inputs that the selection is correct. Then, the accuracy of the user learning unit 17 is further improved. On the other hand, when Page B is thumbnail-displayed on the speaker's display unit and Page B is incorrect, the speaker makes a pronunciation such as "at mark minus". Then, System 1 performs processing related to the selection instruction "at mark minus". Here, since the thumbnail-displayed candidate for the next page is incorrect, it may be possible to display a plurality of related words about the next page or to perform thumbnail display for a plurality of candidates for the next page.

[0032] Also, assume that a certain speaker is explaining page C using the speaker's terminal 21. Assume that page C is related to topic C. In this example, for simplicity, each page is related to only one topic. And assume that in the flow of the conversation, after topic C, it branches into topic D and topic E. Then, the pages following page C are page D and page E as candidates. The selection page output unit 15 may further display the thumbnails 25 of page D and page E on the display unit of the speaker's terminal 21 on which page C is being displayed, or may display the related words related to page D and page E in the related word display column 27. In this way, the speaker can be effectively made to select the next page. As described above, the candidates related to page D and page E may also be data output by inputting the flow of a certain speaker's conversation (therefore, selection information) as input data to the user learning unit 17.

[0033] Furthermore, if the explanatory sounds related to each page are stored in the storage unit, based on the user's page selection command, appropriate voice explanations can be provided. For this reason, for example, when trying to understand something (alone) based on materials, by making a selection command based on the selection information (for example, thumbnails, related words) displayed at the branch, an appropriate page can be selected, and along the flow of the conversation, appropriate explanations corresponding to the user's emotion (the emotion of wanting such information) can be received.

Industrial Applicability

[0034] This invention can learn the flow of using past materials and propose established materials based on the flow of using actual materials, so it can be used in the information-related industry.

Explanation of Reference Numerals

[0035] 1 Explanation Material Presentation Assistance System 3 Material Storage Unit 5 Related Word Storage Unit 7 Topic Storage Unit 11 Input Term Analysis Unit 13th page output section 15th selection page output section 17th user learning section 21st speaker's terminal 23rd audience's terminal 25th thumbnail 27th related word display column

Claims

1. A material storage unit (3) for storing explanatory materials; a related word storage unit (5) for storing one or more related words in association with each page of the explanatory material; a topic storage unit (7) for storing a plurality of topic flows related to the explanatory material in association with the related words, the topic flow being related to the order in which the related words appear; an input term analysis unit (11) for analyzing input terms; a next page output unit (13) that analyzes a current topic in the flow of the plurality of topics based on the input term and outputs a page of the explanatory material related to the next topic at the current time; a selection page output unit (15) for outputting a page of the explanatory material based on an input selection instruction when there are a plurality of pages of the explanatory material related to the next topic of the current topic; An explanatory material presentation assistance system (1).

2. 2. The explanation material presentation assistance system according to claim 1, The selected page output unit (15) An explanatory material presentation assistance system that, when there are multiple next topics after the current topic, displays the related words related to each next topic on a display unit and outputs a page of the explanatory material based on a selection instruction input in relation to the related words.

3. 2. The explanation material presentation assistance system according to claim 1, The selected page output unit (15) An explanatory material presentation assistance system that, when there are multiple next topics after the current topic and there are pages of the explanatory material corresponding to each of the multiple next topics, outputs thumbnails of each page and outputs a page of the explanatory material based on a selection instruction input in relation to a specific one of the pages.

4. 2. The explanation material presentation assistance system according to claim 1, The input selection instruction is a pre-registered voice input regarding the selection, in the explanatory material presentation assistance system.

5. 2. The explanation material presentation assistance system according to claim 1, An explanatory material presentation assistance system comprising a user learning unit that learns a user's selection instruction information, the user learning unit further comprising: when the user's selection instruction information is input, the user learning unit outputs a page of the explanatory material based on the input selection instruction.

6. 2. The explanation material presentation assistance system according to claim 1, The explanatory material presentation assistance system further comprises an audio output unit, which outputs audio information corresponding to each of the plurality of topics.

7. The computer includes a material storage unit (3) for storing explanatory materials; a related word storage unit (5) for storing one or more related words in association with each page of the explanatory material; a topic storage unit (7) for storing a plurality of topic flows related to the explanatory material in association with the related words, the topic flow being related to the order in which the related words appear; an input term analysis unit (11) for analyzing input terms; a next page output unit (13) that analyzes a current topic in the flow of the plurality of topics based on the input term and outputs a page of the explanatory material related to the next topic at the current time; a selection page output unit (15) for outputting a page of the explanatory material based on an input selection instruction when there are a plurality of pages of the explanatory material related to the next topic of the current topic; A program for functioning as an explanatory material presentation assistance system (1).

8. 8. A computer-readable information recording medium storing the program according to claim 7.

Images