Information processing device, information processing system, information processing method, and program

Abstract

To provide an information processing device, an information processing system, an information processing method, and a program that can improve the levels of understanding of users participating in a web conference.SOLUTION: In an information processing device, data analysis means analyzes reaction data indicating reactions of users participating in a web conference. Understanding level calculation means calculates the levels of understanding of the users based on an analysis result of the reaction data. Question generation means generates a question for confirming understanding of a word. Question transmission means transmits the question to a user terminal of a user whose level of understanding is lower than a preset standard. Answer reception means receives an answer to the question from the user terminal. Answer processing means generates information about the word based on the answer and transmits the information to the user terminal of the user whose level of understanding is lower than the standard.SELECTED DRAWING: Figure 37

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing system, an information processing method, and a program. [Background technology]

[0002] In recent years, the spread of telework has led to an increase in opportunities for online conferences. Compared to face-to-face meetings, online meetings lack information such as the atmosphere of the room, the gazes of participants, and their attitudes. This makes it easy for participants to miss opportunities to speak. When words whose meanings are unclear (hereafter referred to as "unclear words") appear, participants are unable to understand the content of the meeting, which can lead to a decline in the quality of the meeting.

[0003] If the unknown word is a common term, participants can research it themselves. However, during the research, participants cannot join the meeting, and the meeting proceeds without understanding the content. Also, if the unknown word is an internal term, participants can ask other participants questions. However, the problem is that participants' questions can disrupt the progress of the meeting.

[0004] For example, Patent Document 1 describes a system that assists users viewing educational content. The system estimates the user's level of understanding of the content based on the user's eye movements and outputs assistance information to promote the user's understanding. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2022-58315 Summary of the Invention [Problem to be solved by the invention]

[0006] The system described in Patent Document 1 assists users who view educational content. However, this system does not assist users who are participating in a web conference. In a web conference, participants speak in real time, but their comments differ from the content prepared in advance.

[0007] An object of the present disclosure is to provide an information processing device, an information processing system, an information processing method, and a program that solve the above-mentioned problems. [Means for solving the problem]

[0008] An information processing device according to one embodiment of the present disclosure includes a data analysis means for receiving reaction data indicating the reactions of users participating in a web conference from the user terminals used by the users in the web conference and analyzing the reaction data, a comprehension calculation means for calculating the users' levels of understanding based on the results of analyzing the reaction data, a question generation means for generating questions to confirm their understanding of words used in the web conference, a question sending means for sending the questions to user terminals of users whose levels of understanding are lower than a predetermined standard, a response receiving means for receiving answers to the questions from the user terminals, and a response processing means for generating information regarding the words based on the answers and sending the information to the user terminals of the users whose levels of understanding are lower than the standard.

[0009] An information processing system according to one aspect of the present disclosure includes the above-described information processing device and the user terminal that transmits the reaction data and the answer to the information processing device and receives the question and the information from the information processing device.

[0010] An information processing method according to one embodiment of the present disclosure receives reaction data indicating the reactions of users participating in a web conference from the user terminals used by the users in the web conference, analyzes the reaction data, calculates the users' levels of understanding based on the results of analyzing the reaction data, generates questions to confirm their understanding of words used in the web conference, sends the questions to user terminals of users whose levels of understanding are lower than a predetermined standard, receives answers to the questions from the user terminals, generates information about the words based on the answers, and sends the information to the user terminals of users whose levels of understanding are lower than the standard.

[0011] A program according to one embodiment of the present disclosure causes a computer to perform information processing to receive reaction data indicating the reactions of users participating in a web conference from the user terminals used by the users in the web conference, analyze the reaction data, calculate the users' levels of understanding based on the results of analyzing the reaction data, generate questions to confirm their understanding of words used in the web conference, send the questions to user terminals of users whose levels of understanding are lower than a predetermined standard, receive answers to the questions from the user terminals, generate information about the words based on the answers, and send the information to the user terminals of users whose levels of understanding are lower than the standard. [Effects of the Invention]

[0012] According to the above aspect, it is possible to improve the level of understanding of users participating in a Web conference. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram of a Web conference system according to the present disclosure. [Figure 2] FIG. 2 is a diagram illustrating an example of employee information stored in an employee information database (DB) according to the present disclosure. [Figure 3] FIG. 2 is a diagram illustrating an example of understanding level information stored in an understanding level DB according to the present disclosure. [Figure 4]FIG. 2 is a diagram illustrating an example of word information held in a word DB according to the present disclosure. [Figure 5] FIG. 2 is a hardware configuration diagram of a Web conference server according to the present disclosure. [Figure 6] FIG. 2 is a hardware configuration diagram of a DB server according to the present disclosure. [Figure 7] FIG. 2 is a hardware configuration diagram of a user terminal according to the present disclosure. [Figure 8] FIG. 2 is a sequence diagram of a Web conference system according to the present disclosure. [Figure 9] FIG. 2 is a sequence diagram of a Web conference system according to the present disclosure. [Figure 10] FIG. 2 is a sequence diagram of a Web conference system according to the present disclosure. [Figure 11] 10 is a flowchart illustrating the operation of a Web conference server according to the present disclosure. [Figure 12] 3 is a schematic diagram illustrating the operation of a Web conference server according to the present disclosure. FIG. [Figure 13] FIG. 10 is a diagram illustrating an example of comprehension levels according to the present disclosure. [Figure 14] FIG. 2 is a diagram illustrating an example of audio data according to the present disclosure. [Figure 15] FIG. 10 illustrates an example of keyboard input according to the present disclosure. [Figure 16] FIG. 10 is a diagram illustrating an example of employee information stored in an employee information DB according to the present disclosure. [Figure 17] FIG. 10 is a diagram illustrating an example of a correction value for each position according to the present disclosure. [Figure 18] FIG. 2 is a diagram illustrating an example of word information held in a word DB according to the present disclosure. [Figure 19] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 20] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 21] FIG. 10 is a diagram illustrating an example of comprehension levels according to the present disclosure. [Figure 22] FIG. 2 is a diagram illustrating an example of understanding level information stored in an understanding level DB according to the present disclosure. [Figure 23]FIG. 10 illustrates an example of comprehension levels according to the present disclosure. [Figure 24] FIG. 10 illustrates an example of comprehension levels according to the present disclosure. [Figure 25] FIG. 2 is a diagram illustrating an example of understanding level information stored in an understanding level DB according to the present disclosure. [Figure 26] FIG. 10 illustrates an example of comprehension levels according to the present disclosure. [Figure 27] FIG. 10 illustrates an example of comprehension levels according to the present disclosure. [Figure 28] FIG. 2 is a diagram illustrating an example of understanding level information stored in an understanding level DB according to the present disclosure. [Figure 29] 1 is a block diagram of a Web conference system according to the present disclosure. [Figure 30] 10 is a flowchart illustrating the operation of a Web conference server according to the present disclosure. [Figure 31] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 32] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 33] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 34] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 35] FIG. 10 is a diagram illustrating an example of a screen of a display of a user terminal according to the present disclosure. [Figure 36] FIG. 2 is a diagram illustrating an example of word information held in a word DB according to the present disclosure. [Figure 37] 1 is a block diagram of an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Each embodiment will be described below with reference to the drawings. In all drawings, the same or corresponding components are designated by the same reference numerals, and common descriptions will be omitted.

[0015] First Embodiment An embodiment of the present disclosure will be described below with reference to the drawings. FIG. 1 shows the functional configuration of a Web conferencing system 100 according to this embodiment. The Web conferencing system 100 calculates each user's level of understanding of the conference content in real time, and asks questions to users with low levels of understanding to confirm their understanding of the words used in the conference. The Web conferencing system 100 provides users with information that promotes understanding based on their answers to the questions.

[0016] The Web conferencing system 100 can automatically ask questions to users and obtain answers, so the Web conferencing system 100 can improve users' understanding and the quality of the conference without disrupting the progress of the conference by requiring users to investigate or ask questions to others.

[0017] The Web conference system 100 includes a Web conference server 1, a database (DB) server 2, and a user terminal 3. The Web conference server 1 and the DB server 2 are computer systems such as personal computers (PCs). The user terminal 3 is an information device such as a PC, a smartphone, or a tablet terminal.

[0018] The Web conference server 1 is connected to a network and relays the Web conference. The DB server 2 stores the content processed by the Web conference server 1. The user terminal 3 communicates with the Web conference server 1 via the network. The Web conference system 100 includes multiple user terminals 3, but the configuration of one user terminal 3 is shown in FIG. 1.

[0019] The Web conference server 1 includes a user identification unit 11, a sentiment analysis unit 12, a speech volume analysis unit 13, a keyboard input analysis unit 14, a response processing unit 15, an understanding level calculation unit 16, a question generation unit 17, and a question transmission unit 18. The DB server 2 includes an employee information DB 21, an understanding level DB 22, and a vocabulary DB 23. The user terminal 3 includes a camera 31, a microphone 32, a keyboard 33, and a question and answer unit 34.

[0020] The user identification unit 11 has the following functions: The user identification unit 11 refers to the employee information DB 21 of the DB server 2 and identifies users (participants in the Web conference) stored in the employee information DB 21. The user identification unit 11 acquires employee information of users participating in the Web conference from the employee information DB 21.

[0021] The emotion analysis unit 12 has the following functions: The emotion analysis unit 12 receives video data generated by the camera 31 of the user terminal 3 from the user terminal 3. The emotion analysis unit 12 analyzes the level of each emotion [joy, trust, fear, surprise, sadness, disgust, anger, and expectation] based on the video data. The emotion analysis unit 12 transmits the analysis results to the understanding level calculation unit 16.

[0022] The speech volume analysis unit 13 has the following functions. The speech volume analysis unit 13 receives, from the user terminal 3, voice data generated by the microphone 32 of the user terminal 3. The speech volume analysis unit 13 analyzes the length of the speech section to analyze the volume of the user's speech. The speech volume analysis unit 13 transmits the analysis result to the understanding level calculation unit 16. The speech volume analysis unit 13 extracts frequently occurring words from the voice data received from all user terminals 3, and transmits the frequently occurring words to the question generation unit 17.

[0023] The keyboard input analysis unit 14 has the following functions: The keyboard input analysis unit 14 receives input data generated by the keyboard 33 of the user terminal 3 from the user terminal 3. The keyboard input analysis unit 14 analyzes the length of keystroke time to analyze the frequency of input by the user. The keyboard input analysis unit 14 transmits the analysis result to the understanding level calculation unit 16.

[0024] The understanding level calculation unit 16 has the following functions. The understanding level calculation unit 16 receives data sent by the sentiment analysis unit 12, the comment volume analysis unit 13, the keyboard input analysis unit 14, and the response processing unit 15, and calculates the level of understanding of the users participating in the web conference. The understanding level calculation unit 16 registers the calculated level of understanding in the understanding level DB 22 of the DB server 2. The understanding level calculation unit 16 determines a threshold from the levels of understanding of all participants and selects a target user to whom a question will be sent. The understanding level calculation unit 16 transmits information about the user terminal 3 of the target user to the question transmission unit 18. The understanding level calculation unit 16 updates the user's level of understanding based on the correct / incorrect data sent by the response processing unit 15. The correct / incorrect data indicates whether the user's response is correct or not. The understanding level calculation unit 16 registers the updated level of understanding in the understanding level DB 22 of the DB server 2.

[0025] The question generation unit 17 has the following functions. The question generation unit 17 receives frequently occurring words from the comment volume analysis unit 13. The question generation unit 17 references the word DB 23 of the DB server 2 and acquires the meanings of the frequently occurring words received from the comment volume analysis unit 13 from the word DB 23. The question generation unit 17 generates question data, which is a character string of a question sentence including the frequently occurring words received from the comment volume analysis unit 13. The question generation unit 17 transmits the generated question data to the question sending unit 18. The question generation unit 17 uses the meanings of the frequently occurring words to generate correct answer data that pairs with the question data, and transmits the correct answer data to the answer processing unit 15.

[0026] The question sending unit 18 has the following functions: The question sending unit 18 receives information about the user terminal 3 of the target user who is sending the question from the understanding level calculation unit 16, and receives question data from the question generation unit 17. The question sending unit 18 sends the question data to the question answering unit 34 of the user terminal 3 of the target user.

[0027] The answer processing unit 15 has the following functions: The answer processing unit 15 receives answer data sent by the question answering unit 34 of the user terminal 3, and receives correct answer data sent by the question generation unit 17. The answer processing unit 15 generates correct / incorrect data based on the answer data and correct answer data, and sends the correct / incorrect data to the understanding level calculation unit 16 and the question answering unit 34. The answer processing unit 15 sends the meanings of frequently occurring words used to generate the question data to the question answering unit 34.

[0028] The employee information DB 21 stores employee information including the employee's job title. Fig. 2 shows an example of employee information stored in the employee information DB 21. The employee information includes each user's ID 211, employee name 212, and job title 213. The employee information DB 21 may be provided by an external service.

[0029] The understanding level DB 22 holds information about the level of understanding of each user regarding a conference. Fig. 3 shows an example of the understanding level information held in the understanding level DB 22. The understanding level information includes an ID 221, an employee name 222, and an understanding level 223 of each user.

[0030] The word DB 23 holds word information. Fig. 4 shows an example of word information held in the word DB 23. The word information includes an ID 231, a word 232, and a meaning 233.

[0031] The camera 31, microphone 32, and keyboard 33 generate reaction data that indicate the reactions of users participating in the web conference.

[0032] The camera 31 has the following functions: The camera 31 captures images of the faces of users participating in a Web conference using the user terminal 3, and generates video data as reaction data. The user terminal 3 transmits the video data to the emotion analysis unit 12 of the Web conference server 1.

[0033] The microphone 32 has the following functions: The microphone 32 collects the voices of users participating in a Web conference using the user terminal 3, and generates voice data as reaction data. The user terminal 3 transmits the voice data to the speech volume analysis unit 13 of the Web conference server 1.

[0034] The keyboard 33 has the following functions: The keyboard 33 is an input device used by users participating in a web conference to input information. The keyboard 33 generates input data as reaction data based on the information input by the user. For example, the input data indicates a character string input by the user. The user terminal 3 transmits the input data to the keyboard input analysis unit 14 of the web conference server 1.

[0035] The user terminal 3 may include an input device other than the keyboard 33. For example, the user terminal 3 may include a button, a touch screen, or the like as an input device.

[0036] The question and answering unit 34 has the following functions: The question and answering unit 34 receives question data sent by the question sending unit 18 of the Web conference server 1, and displays the question and two-choice answer buttons on the display 309. When the user presses one of the two-choice answer buttons, the question and answering unit 34 sends answer data indicating the user's answer to the answer processing unit 15 of the Web conference server 1. The question and answering unit 34 receives the correct / incorrect data and the meanings of frequently occurring words sent by the answer processing unit 15. The question and answering unit 34 displays whether the user's answer is correct or not, and the meanings of the frequently occurring words on the display 309.

[0037] 5 shows the hardware configuration of the Web conference server 1. The Web conference server 1 is a computer equipped with various hardware components, such as a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, other storage devices 104, and a communication device 105. The CPU 101 executes a program stored in the ROM 102 to implement the functions of a user identification unit 11, a sentiment analysis unit 12, a comment volume analysis unit 13, a keyboard input analysis unit 14, a response processing unit 15, a comprehension calculation unit 16, a question generation unit 17, and a question transmission unit 18. The communication device 105 communicates with the DB server 2 and the user terminal 3.

[0038] 6 shows the hardware configuration of the DB server 2. The DB server 2 is a computer equipped with various hardware components such as a CPU 201, a ROM 202, a RAM 203, another storage device 204, and a communication device 205. The storage device 204 stores an employee information DB 21, an understanding level DB 22, and a vocabulary DB 23. The communication device 105 communicates with the Web conference server 1.

[0039] 7 shows the hardware configuration of the user terminal 3. The user terminal 3 is a computer equipped with various hardware components such as a CPU 301, a ROM 302, a RAM 303, another storage device 304, a communication device 305, a camera 306, a microphone 307, a keyboard 308, and a display 309. The CPU 301 executes a program stored in the ROM 302 to realize the function of the question and answer unit 34. The communication device 305 communicates with the Web conference server 1. The camera 306 corresponds to the camera 31. The microphone 307 corresponds to the microphone 32. The keyboard 308 corresponds to the keyboard 33. The display 309 displays question data and the like.

[0040] The programs for realizing the functions of the Web conference server 1, DB server 2, and user terminal 3 are recorded on a computer-readable recording medium (ROM in the above example). "Computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into computer systems. Furthermore, "computer-readable recording medium" also includes devices that retain programs for a certain period of time, such as volatile memory (RAM) inside computer systems that act as servers or clients when the programs are transmitted over a network such as the Internet or over communication lines such as telephone lines.

[0041] The program may also be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by transmission waves in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has the function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may also be a program that realizes part of the above-mentioned functions. Furthermore, the program may be a so-called differential file (differential program) that can realize the above-mentioned functions in combination with a program already recorded in the computer system.

[0042] The following describes the operation of the Web conference system 100. Figures 8, 9, and 10 show an example of the operation of the Web conference system 100. The order in which the processes shown in Figures 8, 9, and 10 are executed is an example, and the order is not limited to this.

[0043] The camera 31 of the user terminal 3 generates video data, and the user terminal 3 transmits the video data generated by the camera 31 to the emotion analysis unit 12 of the Web conference server 1 (step S301). The emotion analysis unit 12 receives the video data (step S101). The transmission and reception of the video data are repeated.

[0044] The microphone 32 of the user terminal 3 generates voice data, and the user terminal 3 transmits the voice data generated by the microphone 32 to the speech volume analysis unit 13 of the Web conference server 1 (step S302). The speech volume analysis unit 13 receives the voice data (step S102). The transmission and reception of the voice data is repeated.

[0045] The keyboard 33 of the user terminal 3 generates input data, and the user terminal 3 transmits the input data generated by the keyboard 33 to the keyboard input analysis unit 14 of the Web conference server 1 (step S303). The keyboard input analysis unit 14 receives the input data (step S103). The transmission and reception of the input data are repeated.

[0046] The emotion analysis unit 12 processes the received video data and analyzes the user's emotions. Normally, when a user understands the content of the conference, positive emotions among joy, anger, sadness, and happiness are strongly expressed. Conversely, when a user does not understand the content of the conference, negative emotions are strongly expressed. The emotion analysis unit 12 transmits the analysis results to the understanding level calculation unit 16 (step S104).

[0047] The speech amount analysis unit 13 processes the received voice data and analyzes the amount of speech made by the user. Specifically, the speech amount analysis unit 13 calculates the time that the microphone 32 is unmuted relative to the elapsed time of the conference, and calculates the user's participation rate in the conference. If the user is actively speaking in the conference, the speech amount analysis unit 13 determines that the user has a sufficient grasp of the content of the conference and that the level of understanding is high. The speech amount analysis unit 13 transmits the analysis result to the understanding level calculation unit 16 (step S105).

[0048] The keyboard input analysis unit 14 processes the received input data and analyzes the frequency of input by the user. If there is little keyboard input during a meeting, the user is concentrating on the meeting. In such a case, the keyboard input analysis unit 14 determines that the level of understanding is high. If there is a lot of keyboard input during a meeting, the user may be performing work other than the content of the meeting and may not be concentrating on the meeting. In such a case, the keyboard input analysis unit 14 determines that the level of understanding is low. The keyboard input analysis unit 14 transmits the analysis result to the understanding level calculation unit 16 (step S106).

[0049] Every five minutes after the start of the conference, the emotion analysis unit 12, the speech volume analysis unit 13, and the keyboard input analysis unit 14 execute the above process. The emotion analysis unit 12 generates an emotion analysis value E t The speech volume analysis unit 13 calculates the speech detection value V as the analysis result of the user's speech volume. t The keyboard input analysis unit 14 calculates the non-keystroke detection value K as an analysis value of the user's input frequency. t Calculate.

[0050] The order of steps S104, S105, and S106 is not limited to the order shown in Fig. 8. Steps S104, S105, and S106 can be executed in any order.

[0051] In step S104, the emotion analysis unit 12 calculates the emotion analysis value E t The details of the process for calculating emotion will be described below. Various emotions can be output depending on the learning model, but in this embodiment, an example will be described in which eight emotions based on Plutchik's wheel of emotions are used. However, a calculation method similar to that of this embodiment can also be applied to learning models that output emotions other than the eight emotions.

[0052] Plutchik's Wheel of Emotions is an emotional model that shows that many emotions can be expressed by combining eight basic emotions: "joy," "trust," "fear," "surprise," "sadness," "disgust," "anger," and "anticipation." Of these basic emotions, "joy" and "trust" are classified as positive emotions, "sadness," "disgust," "anger," and "fear" are classified as negative emotions, and "surprise" and "anticipation" are classified as neutral emotions.

[0053] When a user expresses positive emotions, the level of understanding is defined as high, when a user expresses negative emotions, the level of understanding is low, and when a user expresses neutral emotions, the level of understanding is medium.The emotion analysis value ranges from 0 to 100, and the higher the number, the higher the level of understanding.

[0054] The degree of understanding of a positive emotion is defined as 100, the degree of understanding of a neutral emotion as 50, and the degree of understanding of a negative emotion as 0. The emotion analysis unit 12 calculates the sum of the probabilities classified into each emotion, multiplies the sum of each probability by the degree of understanding corresponding to each emotion, and calculates the sum of the results. Emotion analysis value E t is expressed by the following equation (1). E t = 100 × (sum of positive emotion probabilities) + 50 × (sum of neutral emotion probabilities) + 0 × (sum of negative emotion probabilities) (1)

[0055] For example, if the results of the sentiment analysis are [Happiness 80%, Trust 10%, Fear 1%, Surprise 5%, Sadness 0.5%, Disgust 0.5%, Anger 0.5%, Anticipation 2.5%], categorizing the results of the sentiment analysis into positive, negative, and neutral results as follows: Positive emotion: Joy (80%) + Trust (10%) = 90 Neutral emotion: Surprise (5%) + Anticipation (2.5%) = 7.5 Negative emotions: Sadness (0.5%) + Disgust (0.5%) + Anger (0.5%) + Fear (1%) = 2.5 Sentiment analysis value: 100 x 90 / 100 + 50 x 7.5 / 100 + 0 x 2.5 / 100 = 93.75

[0056] By rounding off the decimal point of the sentiment analysis value, the sentiment analysis unit 12 obtains the sentiment analysis value E t We get 94 as

[0057] In step S105, the speech volume analysis unit 13 calculates the speech detection value V t The details of the process for calculating the voice detection value V are explained below. The time when the microphone 32 is unmuted and the user's speaking time are defined as being synonymous. Also, in a conference, it is defined that a person who speaks a lot understands the contents of the conference, and a person who speaks little does not understand the contents of the conference. The ratio of the cumulative time that the user spoke to the elapsed conference time is defined as the voice detection value V t The voice detection value V t is expressed by the following equation (2).

[0058]

number

[0059] For example, if the speaking time is 10 minutes in a 30-minute meeting, the voice detection value V t becomes 33.

[0060] In step S106, the keyboard input analysis unit 14 calculates the non-keystroke detection value K tThe details of the process for calculating are now described. If there is a lot of keyboard input during a meeting, the user may be performing work other than the content of the meeting. Therefore, if there is a lot of keyboard input during a meeting, it is defined that the user's level of understanding of the meeting is low. On the other hand, if there is little keyboard input, it is defined that the user's level of understanding of the meeting is high.

[0061] The keyboard input analysis unit 14 monitors the state of the keyboard 33 based on the input data. If there is no keyboard input for one second or more, the keyboard input analysis unit 14 determines that the state of the keyboard 33 is a "no keyboard input" state and starts measuring time. When the next keyboard input is detected, the keyboard input analysis unit 14 stops measuring time. The keyboard input analysis unit 14 calculates the ratio of the cumulative time of this "no keyboard input" state to the elapsed time of the conference as a no-keystroke detection value K t The non-keystroke detection value K t is expressed by the following equation (3).

[0062]

number

[0063] For example, if the time spent typing on the keyboard during a 30-minute meeting is 20 minutes, the time spent not typing on the keyboard is 10 minutes. In this case, the non-keystroke detection value K is calculated from the value of equation (3) (10 / 30=33%). t becomes 33.

[0064] The understanding level calculation unit 16 calculates the emotion analysis value E t is received from the emotion analysis unit 12, and the voice detection value V t is received from the speech volume analysis unit 13, and the non-keystroke detection value K t from the keyboard input analysis unit 14. The understanding level calculation unit 16 calculates the emotion analysis value E t , the voice detection value V t , and non-keystroke detection value K t is expressed as a measurement vector, and the absolute value of the measurement vector is the measurement M t (Step S107) The measured value Mt is expressed by the following equation (4).

[0065]

number

[0066] It is assumed that the higher the position of a conference participant, the greater the amount of prior knowledge. Therefore, the understanding level calculation unit 16 calculates the measurement value M t In this embodiment, the understanding level calculation unit 16 uses the job title of the employee as the attribute of the employee.

[0067] The user identification unit 11 accesses the employee information DB 21 in the DB server 2. The user identification unit 11 holds the ID of each user participating in the Web conference and transmits the ID to the DB server 2. The DB server 2 acquires the job title of the user having the ID received from the user identification unit 11 from the employee information DB 21 and transmits the job title to the user identification unit 11 (step S211). The user identification unit 11 receives the job title transmitted by the DB server 2 and transmits the job title and the ID to the understanding level calculation unit 16 (step S111).

[0068] The understanding level calculation unit 16 accesses the understanding level DB 22 of the DB server 2. At this time, the understanding level calculation unit 16 transmits the ID received from the user identification unit 11 to the DB server 2. The DB server 2 acquires the understanding level of the user having the ID received from the understanding level calculation unit 16 from the understanding level DB 22, and transmits the understanding level to the user identification unit 11 (step S212). The understanding level calculation unit 16 receives the understanding level transmitted by the DB server 2 (step S112).

[0069] The understanding level DB 22 holds the understanding level previously calculated by the understanding level calculation unit 16. Before the understanding level calculation unit 16 calculates the understanding level for the first time, the understanding level held in the understanding level DB 22 is an initial value (for example, 0).

[0070] The understanding level calculation unit 16 calculates the measurement value M tThe understanding level calculation unit 16 adds a position correction value C according to the position to the measurement value M t By adding the previous understanding level to the above, each user's understanding level for judgment J t is calculated (step S113).

[0071] Comprehension level J for evaluation t is the previous understanding level P t-1 , the measured value M t By using the position correction value C (first time only), it is expressed as the following equation (5). J t =P t-1 +M t +C(First Time Only) ···(5)

[0072] The understanding level calculation unit 16 calculates the understanding level J for judgment. t Specifically, the understanding level calculation unit 16 multiplies the median of the understanding levels for determination of all users participating in the conference by a magnification factor to calculate the threshold value Th t For example, the magnification is 0.7, but is not limited to this. t Determine the comprehension level J t is the threshold Th t The understanding level calculation unit 16 selects a user who has a score less than the above as a target user. The understanding level calculation unit 16 transmits information for identifying the user terminal 3 of the target user to the question transmission unit 18 (step S114).

[0073] The speech volume analysis unit 13 performs speech recognition on the voice data received from the user terminal 3 and converts the voice data into character string data. Thereafter, the speech volume analysis unit 13 counts the number of times each word appears at five-minute intervals and extracts the word that appears most frequently in the five-minute period (frequent word). The speech volume analysis unit 13 performs this process by using the voice data received from the user terminals 3 of all users participating in the conference. Frequent words are words that appear most frequently in the speech of all users participating in the conference. The speech volume analysis unit 13 sends the extracted frequent words to the question generation unit 17 (step S115).

[0074] The question generator 17 generates question data and correct answer data (step S116) by executing the process shown in Fig. 11. Details of step S116 will be described with reference to Fig. 11.

[0075] The question generation unit 17 accesses the word DB 23 of the DB server 2. At this time, the question generation unit 17 transmits the frequently occurring words received from the message amount analysis unit 13 to the DB server 2 (step S1161). The DB server 2 obtains the meanings of the frequently occurring words received from the question generation unit 17 from the word DB 23, and transmits the meanings to the question generation unit 17 (step S213 in FIG. 9).

[0076] The question generation unit 17 receives the meanings transmitted by the DB server 2. The question generation unit 17 generates question data in the form of a predetermined fixed phrase using the frequently occurring words received from the comment volume analysis unit 13 and the meanings received from the DB server 2. The question generation unit 17 may generate question data by using generation AI (Artificial Intelligence). The question generation unit 17 also generates correct answer data for the question by using the meanings received from the DB server 2. The question generation unit 17 transmits the question data to the question transmission unit 18, and transmits the correct answer data and the meanings of the frequently occurring words used to generate the question data to the answer processing unit 15 (step S1162).

[0077] For example, the above-mentioned template is a string such as "XX (frequently occurring word) is XX (word meaning)." The template may be changed or added depending on the organization conducting the web conference. To enable users to easily respond to questions during the conference, the questions are closed questions that can be answered with two choices: "yes" or "no." When generating question data, the question generation unit 17 uses the meaning of another word in the word DB 23 as "XX (word meaning)" with a 50% probability. The correct answer data is true / false data indicating whether the meaning of a frequently occurring word was used as "XX (word meaning)."

[0078] 10, the operation of the Web conferencing system 100 from step S116 onwards will be described. The question sending unit 18 receives information about the user terminal 3 of the target user who will send the question from the understanding level calculation unit 16, and receives the question data from the question generation unit 17. The question sending unit 18 sends the question data to the question answering unit 34 of the user terminal 3 of the target user (step S121).

[0079] The question answering unit 34 receives the question data sent by the question sending unit 18 and displays the question and two-choice answer buttons on the display 309 (step S321). The user answers the question with "yes" or "no." When the user presses one of the two-choice answer buttons, the question answering unit 34 generates answer data indicating the user's answer and sends the answer data to the answer processing unit 15 of the Web conference server 1 (step S322). The answer processing unit 15 receives the answer data (step S122).

[0080] The answer processing unit 15 compares the answer data received from the question answering unit 34 with the correct answer data received from the question generation unit 17, and generates correct / incorrect data. The correct / incorrect data is true / false value data. That is, if the answer data and the correct answer data match, the correct / incorrect data indicates true, and if the answer data and the correct answer data do not match, the correct / incorrect data indicates false. The answer processing unit 15 transmits the correct / incorrect data to the understanding level calculation unit 16 (step S123).

[0081] The understanding level calculation unit 16 receives the correct / incorrect data from the answer processing unit 15, and updates the user's understanding level based on the correct / incorrect data (step S124).

[0082] An example of the process in which the understanding level calculation unit 16 updates the understanding level will be described with reference to Fig. 12. In this example, there are two user terminals 3, and the user of one user terminal 3 (Nichiden Goro) answers the question correctly, while the user of the other user terminal 3 (Nichiden Rokuro) answers the question incorrectly. The answer processing unit 15 receives answer data sent from the question answering units 34 of the two user terminals 3.

[0083] The answer processing unit 15 compares the correct answer data sent by the question generation unit 17 with the answer data sent from the question answering unit 34, and generates correct / incorrect data. Then, the answer processing unit 15 sends the correct / incorrect data to the understanding level calculation unit 16. The understanding level for judgment of Nichiden Goro is, for example, 30, and the understanding level for judgment of Nichiden Rokuro is, for example, 20.

[0084] The understanding level calculation unit 16 receives the correct / incorrect data from the answer processing unit 15. The understanding level calculation unit 16 calculates the understanding level by correcting the understanding level for determination of the target user who sent the question. Understanding level P t is the level of understanding for judgment J t and the question correction value Q t By using the above, it is expressed as the following equation (6). P t =J t +Q t ···(6)

[0085] If the correct / incorrect data is true, the understanding level calculation unit 16 calculates the understanding level J for judgment. t 1 or more questions correction value Q t If the correct / incorrect data is false, the understanding level calculation unit 16 calculates the understanding level for judgment J t In the example shown in Figure 12, since Nichiden Goro answered the question correctly, his understanding level P t is the level of understanding for judgment J t (e.g. 30) and the question correction value Q t (For example, 10) and the sum (40). In the example shown in Figure 12, Mr. Nichiden Rokuro did not answer the question correctly, so his understanding level P t is the level of understanding for judgment J t (e.g., 20). In this example, the query correction value Q t is 10, but this may be changed depending on the usage scenario.

[0086] In the above example, the understanding level of the user who did not answer the question correctly remains unchanged from the understanding level for determination. The understanding level calculation unit 16 may calculate the understanding level by decreasing the understanding level for determination of the user who did not answer the question correctly.

[0087] 10, the operation of the Web conference system 100 from step S124 onwards will be described. The understanding level calculation unit 16 accesses the understanding level DB 22 of the DB server 2. At this time, the understanding level calculation unit 16 transmits the ID received from the user identification unit 11 and the understanding level calculated in step S124 to the DB server 2 (step S125). The DB server 2 searches the understanding level DB 22 for the understanding level of the user having the ID received from the understanding level calculation unit 16, and updates the understanding level with the understanding level received from the understanding level calculation unit 16 (step S221).

[0088] The answer processing unit 15 transmits the correct / incorrect data and the meanings of the frequently occurring words to the question answering unit 34 of the user terminal 3 (step S126). The question answering unit 34 receives the correct / incorrect data and the meanings of the frequently occurring words, and displays whether the user's answer is correct or not and the meanings of the frequently occurring words on the display 309 (step S323).

[0089] The answer processing unit 15 may transmit the meaning of the frequently occurring word to the question answering unit 34 regardless of whether the user's answer is correct or not. Alternatively, the answer processing unit 15 may not transmit the meaning of the frequently occurring word to the question answering unit 34 when the user's answer is correct, and may transmit the meaning of the frequently occurring word to the question answering unit 34 when the user's answer is incorrect.

[0090] A specific example of the operation of the Web conference system 100 will be described. A Web conference is held with multiple participants. Each user terminal 3 transmits video data, audio data, and input data to the Web conference server 1. The Web conference server 1 calculates the understanding level for assessment based on the video data, audio data, and input data received from each user terminal 3.

[0091] The Web conference server 1 determines a threshold value based on each user's level of understanding for judgment, does nothing for users whose level of understanding for judgment is equal to or greater than the threshold, and asks questions about words that appear in the conference to users whose level of understanding for judgment is less than the threshold. The users asked the questions answer "yes" or "no." If the user's answer is correct, the Web conference server 1 calculates the user's level of understanding and stores it in the understanding level DB 22 of the DB server 2.

[0092] In the following example, the Web conference server 1 calculates the understanding level for assessment, and then executes a processing loop every five minutes from the start of the conference, which includes sending question data to the user terminal 3 and calculating the understanding level based on the user's answers. The following shows the flow up to 15 minutes after the start of the conference.

[0093] First, a web conference is held with the participation of three people: a new employee, NEC employee A, a supervisor, NEC employee B, and a division manager, NEC employee C. Each user terminal 3 transmits video data, audio data, and input data to the web conference server 1. The web conference server 1 calculates measurement values based on the video data, audio data, and input data received from each user terminal 3, and calculates the comprehension level for assessment based on the measurement values and each user's attributes.

[0094] Fig. 13 shows an example of the level of understanding of each user five minutes after the start of the conference and before the Web conference server 1 sends question data to the user terminal 3. Fig. 13 shows each user's name N10, previous level of understanding P10, current measurement value M10, job title correction value C10, and judgment level of understanding J10 at this time.

[0095] When five minutes have passed since the start of the conference, the understanding level calculation unit 16 calculates the understanding level for judgment for the first time, so the previous understanding level P10 is 0. t , the voice detection value V t , and the non-keystroke detection value K t The measured value M10 is calculated based on this.

[0096] The emotion analysis unit 12 analyzes the video data generated by the camera 31 of the user terminal 3 and calculates an emotion analysis value E t Calculate the sentiment analysis value E t is the sentiment analysis value at the point t minutes have passed since the start of the meeting. tA The symbols in the following explanation are expressed in the same manner.

[0097] Below, the sentiment analysis value E of NEC A tA The emotions of NEC employee A are expressed as follows: Nihon Electric A: [Joy: 10%, Trust: 5%, Fear: 2%, Surprise: 5%, Sadness: 60%, Disgust: 10%, Anger: 3%, Anticipation: 5%]

[0098] Apply the above sentiment to equation (1). Positive emotion: Joy (10%) + Trust (5%) = 15 Neutral emotion: Surprise (5%) + Anticipation (5%) = 10 Negative emotions: Sadness (60%) + Disgust (10%) + Anger (3%) + Fear (2%) = 75 Sentiment analysis value = 100 x 15 / 100 + 50 x 10 / 100 + 0 x 75 / 100 = 20

[0099] Based on the above, the sentiment analysis value E 5A becomes 20.

[0100] The speech volume analysis unit 13 analyzes the speech volume based on the voice data generated by the microphone 32 of the user terminal 3, and calculates the voice detection value V t The process by which the message volume analysis unit 13 analyzes the message volume will be described with reference to FIG.

[0101] FIG. 14 shows an example of voice data. The horizontal direction of FIG. 14 indicates time, and the vertical direction of FIG. 14 indicates the amplitude of the voice. The voice data indicates the volume of the voice emitted by the user at each time. The total duration of the two speeches (the time when the microphone 32 was unmuted) by NEC employee A is 2 minutes, and the elapsed time of the conference is 5 minutes. By applying these to equation (2), the voice detection value V 5Abecomes 40 (=2 / 5).

[0102] The keyboard input analysis unit 14 analyzes the frequency of input by the user based on the input data generated by the keyboard 33 of the user terminal 3, and calculates a non-keystroke detection value K t The process of analyzing the input frequency of the user by the keyboard input analysis unit 14 will be described with reference to FIG.

[0103] FIG. 15 shows an example of a user's keyboard input. The horizontal direction in FIG. 15 indicates time. The voice data indicates the characters input by the user at each time. By applying the total non-keyboard input time (2 minutes) of NEC employee A and the elapsed meeting time (5 minutes) to equation (3), the non-keyboard detection value K 5A becomes 40 (=2 / 5).

[0104] The understanding level calculation unit 16 calculates the emotion analysis value E t , the voice detection value V t , and the non-keystroke detection value K t is expressed as a measurement vector, and the absolute value of the measurement vector is the measurement M t Specifically, the understanding level calculation unit 16 calculates the emotion analysis value E of the person A of the NEC Corporation as follows. 5A , the voice detection value V 5A , and the non-keystroke detection value K 5A By applying this to equation (4), the measured value M 5A The understanding level calculation unit 16 executes the same process as above for the JCP B and JCP C.

[0105] The measurement vectors and measurements of NEC A, NEC B, and NEC C are shown below. Measurement vector of NEC A: [20, 40, 40], measurement value M 5A :60 Measurement vector of NEC B: [90, 42, 68], measurement value M 5B :120 Measurement vector of NEC C: [20,18,30], measurement value M 5C :40

[0106] The understanding level calculation unit 16 adds the position correction value C to the measurement value calculated as above only the first time. The understanding level calculation unit 16 acquires the position of each user from the employee information DB 21 of the DB server 2 and determines the correction value for that position.

[0107] 16 shows an example of employee names 212 and job titles 213 in employee information held in the employee information DB 21. The understanding level calculation unit 16 acquires the job titles 213 associated with the employee names 212 of each of JCPC employee A, JCPC employee B, and JCPC employee C. JCPC employee A's job title 213 is new employee, JCPC employee B's job title 213 is chief, and JCPC employee C's job title 213 is division manager.

[0108] FIG. 17 shows an example of a correction value for each position. A correction value C11 is associated in advance with a position T10. The understanding level calculation unit 16 refers to the correction value C11 associated with each of the positions T10 of JCPC employee A, JCPC employee B, and JCPC employee C. The correction value C11 for JCPC employee A, who is a new employee, is 5, the correction value C11 for JCPC employee B, who is a supervisor, is 20, and the correction value C11 for JCPC employee C, who is a division manager, is 200. These correction values C11 are the same as the position correction value C10 shown in FIG. 13.

[0109] The understanding level calculation unit 16 calculates the previous understanding level P t-1 , the measured value M t , and the position correction value C are applied to equation (5), and the judgment understanding level J t Calculate the three people's understanding level J t is shown below. NEC A-san: Last time's understanding level P 0A (0) + current measurement value M 5A (60) + Position Correction Value C A (5)=65 Nihon Denki B-san: Last time's understanding level P 0B (0) + current measurement value M 5B (120) + Position Correction Value C B (20)=140 NEC C-san: Last time's understanding level P 0C (0) + current measurement value M 5C (40) + Position Correction Value CC (200)=240

[0110] The understanding level calculation unit 16 calculates the threshold Th by multiplying the median of the understanding levels for judgment of all users participating in the conference. t In this example, the magnification is 0.7. Therefore, the threshold value Th t This is 98, which is 0.7 times the level of understanding (140) used for judgment by NEC B.

[0111] The understanding level calculation unit 16 calculates the understanding level J for judgment. t is the threshold Th t The user who has a lower level of understanding than the threshold Th is selected as the target user to send a question. t Since the number is smaller than (98), the target user to whom the question is to be sent is NEC Corporation A. The Web conference server 1 does not send the question data to the user terminals 3 of NEC Corporation B and NEC Corporation C.

[0112] The question generation unit 17 acquires the meanings of frequently occurring words from the word DB 23 of the DB server 2. FIG. 18 shows an example of a word 232 and a meaning 233 in the word information held in the word DB 23. When the frequently occurring word is "UcaaS," the question generation unit 17 acquires the meaning 233 associated with "UcaaS." The question generation unit 17 generates question data by using the frequently occurring word and its meaning with a generation AI. The question generation unit 17 also generates correct answer data.

[0113] Examples of question data, correct answer data, and meanings of frequently occurring words are shown below. Question data: "UCaaS is a type of cloud computing service that uses virtualization technology to provide digital infrastructure such as hardware resources (CPU / memory / storage) on demand via the Internet." Correct answer: "No" Common term meaning: "UCaaS is a cloud-based solution that consolidates all of a company's business communications needs into a single solution, hosted on the cloud by a single vendor."

[0114] The question generation unit 17 transmits the question data to the question transmission unit 18, and transmits the correct answer data and the meanings of the frequently occurring words used to generate the question data to the answer processing unit 15. The question transmission unit 18 transmits the question data to the question answering unit 34 of the user terminal 3 of the target user, NEC Corporation A. The question answering unit 34 receives the question data transmitted by the question transmission unit 18, and displays the question and answer buttons on the display 309.

[0115] 19 shows an example of the screen of the display 309 of the user terminal 3. The display 309 displays a question QU10, a "Yes" button B10, and a "No" button B11 on a screen SC10.

[0116] For example, NEC employee A presses the "No" button B11 to select "No" as the answer to the question. The question answering unit 34 sends answer data indicating "No" to the answer processing unit 15 of the Web conference server 1. The answer processing unit 15 compares the correct answer data with the answer data and generates correct / incorrect data.

[0117] The answer processing unit 15 transmits the correct / incorrect data and the meanings of the frequently occurring words to the question answering unit 34 of the user terminal 3. The question answering unit 34 receives the correct / incorrect data and the meanings of the frequently occurring words, and displays on the display 309 whether the user's answer is correct or not, as well as the meanings of the frequently occurring words.

[0118] 20 shows an example of the screen of the display 309 of the user terminal 3. The display 309 displays on the screen SC10 a character string CH10 indicating whether the user's answer is correct or not, and a sentence TX10 indicating the meaning of frequently occurring words. Since NEC Corporation A selected "No" as the answer, which is correct, the character string CH10 indicates that NEC Corporation A's answer is correct. By checking the sentence TX10, NEC Corporation A can deepen his understanding of the words related to the content of the meeting.

[0119] The understanding level calculation unit 16 calculates the understanding level J for judgment. t and the question correction value Q tBy applying this to equation (6), the understanding level P t If the user's answer is correct, the question correction value Q t is greater than or equal to 1. In this example, the question correction value Q t is 10. If the user's answer is incorrect or the question is not answered, the question correction value Q t is 0.

[0120] Since the answer of NEC A was correct, the understanding calculation unit 16 calculates the question correction value Q t (10) is used to judge the level of understanding of Mr. A of NEC Corporation. t (65). Since no questions were asked to NEC B and NEC C, the comprehension calculation unit 16 calculates the question correction value Q t (0) is the level of understanding J for judging NEC B and NEC C. t Add to.

[0121] 3 people's understanding level for judgment J t is shown below. NEC A: Understanding Level J 5A (65) + Question Correction Value Q 5A (10)=75 NEC B: Understanding Level J 5B (140) + Question Correction Value Q 5A (0)=140 NEC C: Understanding Level J 5C (240) + Question Correction Value Q 5A (0)=240

[0122] Figure 21 shows an example of the level of understanding of each user five minutes after the start of the conference, when the Web conference server 1 has received each user's answer. Figure 21 shows each user's name N10, previous level of understanding P10, current measurement value M10, job title correction value C10, judgment level of understanding J10, question correction value Q10, and level of understanding P11 at this time.

[0123] The understanding level calculation unit 16 transmits the understanding level calculated as described above to the DB server 2. The DB server 2 updates the understanding level in the understanding level DB 22 with the understanding level received from the understanding level calculation unit 16.

[0124] 22 shows an example of employee names 222 and levels of understanding 223 in the updated level of understanding information held in the level of understanding DB 22 five minutes after the start of the meeting. The levels of understanding of JEPCO employee A, JEPCO employee B, and JEPCO employee C are 75, 140, and 240, respectively.

[0125] The understanding level calculation unit 16 executes the above-mentioned process and calculates the measurement value M t For example, the measured values of NEC A, NEC B, and NEC C are 90, 110, and 40, respectively.

[0126] The understanding level calculation unit 16 calculates the previous understanding level P t-1 , the measured value M t , and the position correction value C are applied to equation (5), and the judgment understanding level J t The understanding level calculation unit 16 calculates the position correction value C only for the first time after the start of the meeting. t From the second time onwards, the position correction value C is added to the measurement value M t Do not add to.

[0127] 3 people's understanding level for judgment J t is shown below. NEC A-san: Last time's understanding level P 5A (75) + current measurement value M 10A (90)=165 Nihon Denki B-san: Last time's understanding level P 5B (140) + current measurement value M 10B (110)=250 NEC C-san: Last time's understanding level P 5C (240) + current measurement value M 10C (40)=280

[0128] Fig. 23 shows an example of the level of understanding of each user 10 minutes after the start of the conference and before the Web conference server 1 sends question data to the user terminal 3. Fig. 23 shows each user's name N10, previous level of understanding P11, current measurement value M11, job title correction value C11, and judgment level of understanding J11 at this time.

[0129] The previous level of understanding P11 is the level of understanding five minutes after the start of the meeting. The levels of understanding P11 of NEC employees A, B, and C are 75, 140, and 240, respectively. As shown above, the measured values M11 of NEC employees A, B, and C are 90, 110, and 40, respectively. The position correction value C11 is not added when calculating the level of understanding from the second time onwards. As shown above, the levels of understanding J11 for judgment of NEC employees A, B, and C are 165, 250, and 280.

[0130] The understanding level calculation unit 16 calculates the threshold Th by multiplying the median of the understanding levels for judgment of all users participating in the conference. t In this example, the magnification is 0.7. Therefore, the threshold value Th t This is 175, which is 0.7 times the level of understanding (250) used for judgment by NEC B.

[0131] The understanding level calculation unit 16 calculates the understanding level J for judgment. t is the threshold Th t The user whose understanding level is less than the threshold value Th is selected as the target user to send a question. t Since the number is smaller than (175), the target user to whom the question is to be sent is NEC Corporation A. The Web conference server 1 does not send the question data to the user terminals 3 of NEC Corporation B and NEC Corporation C.

[0132] The understanding level calculation unit 16 calculates the understanding level J for judgment. t and the question correction value Q t By applying this to equation (6), the understanding level P t Since the answer of NEC person A was correct, the understanding calculation unit 16 calculates the question correction value Q t (10) is used to judge the level of understanding of Mr. A of NEC Corporation. t Since no questions were asked to NEC B and NEC C, the comprehension calculation unit 16 adds the question correction value Q t (0) is the level of understanding J for judging NEC B and NEC C. t Add to.

[0133] 3 people's understanding level for judgment Jt is shown below. NEC A: Understanding Level J 10A (165) + Question Correction Value Q 10A (10)=175 NEC B: Understanding Level J 10B (250) + Question Correction Value Q 10A (0)=250 NEC C: Understanding Level J 10C (280) + Question Correction Value Q 10A (0)=280

[0134] Figure 24 shows an example of the level of understanding of each user 10 minutes after the start of the conference, when the Web conference server 1 has received each user's answer. Figure 24 shows each user's name N10, previous level of understanding P11, current measurement value M11, job title correction value C11, judgment level of understanding J11, question correction value Q11, and level of understanding P12 at this time.

[0135] The understanding level calculation unit 16 transmits the understanding level calculated as described above to the DB server 2. The DB server 2 updates the understanding level in the understanding level DB 22 with the understanding level received from the understanding level calculation unit 16.

[0136] 25 shows an example of employee names 222 and levels of understanding 223 in the updated understanding level information held in the understanding level DB 22 after 10 minutes have passed since the start of the meeting. The levels of understanding of JEPCO employee A, JEPCO employee B, and JEPCO employee C are 175, 250, and 280, respectively.

[0137] The understanding level calculation unit 16 executes the above-mentioned process and calculates the measurement value M t For example, the measured values of NEC employees A, B, and C are 100, 90, and 40, respectively.

[0138] The understanding level calculation unit 16 calculates the previous understanding level P t-1 , the measured value M t , and the position correction value C are applied to equation (5), and the judgment understanding level J t The understanding level calculation unit 16 calculates the position correction value C only for the first time after the start of the meeting. tFrom the second time onwards, the position correction value C is added to the measurement value M t Do not add to.

[0139] 3 people's understanding level for judgment J t is shown below. NEC A-san: Last time's understanding level P 10A (175) + current measurement value M 15A (100)=275 Nihon Denki B-san: Last time's understanding level P 10B (250) + current measurement value M 15B (90)=340 NEC C-san: Last time's understanding level P 10C (280) + current measurement value M 15C (40)=320

[0140] Fig. 26 shows an example of the level of understanding of each user 15 minutes after the start of the conference and before the Web conference server 1 sends question data to the user terminal 3. Fig. 26 shows each user's name N10, previous level of understanding P12, current measurement value M12, job title correction value C11, and judgment level of understanding J12 at this time.

[0141] The previous level of understanding P12 is the level of understanding 10 minutes after the start of the meeting. The levels of understanding P12 of NEC employees A, B, and C are 175, 250, and 280, respectively. As shown above, the measured values M12 of NEC employees A, B, and C are 100, 90, and 40, respectively. The position correction value C11 is not added when calculating the level of understanding from the second time onwards. As shown above, the levels of understanding J12 for judgment of NEC employees A, B, and C are 275, 340, and 320.

[0142] The understanding level calculation unit 16 calculates the threshold Th by multiplying the median of the understanding levels for judgment of all users participating in the conference. t In this example, the magnification is 0.7. Therefore, the threshold value Th t This is 224, which is 0.7 times the level of understanding (320) used for judgment by NEC C.

[0143] The understanding level calculation unit 16 calculates the understanding level J for judgment. t is the threshold Th t The users whose understanding level is less than the threshold Th are selected as the target users to send questions to. t Since the number of questions is larger than (175), the Web conference server 1 does not transmit the question data to the user terminals 3 of the NEC Corporation A, the NEC Corporation B, and the NEC Corporation C.

[0144] The understanding level calculation unit 16 calculates the understanding level J for judgment. t and the question correction value Q t By applying this to equation (6), the understanding level P t Since no questions were asked to NEC personnel A, NEC personnel B, and NEC personnel C, the understanding degree calculation unit 16 calculates the question correction value Q t (0) is the level of understanding J for judging NEC A, NEC B, and NEC C. t Add to.

[0145] 3 people's understanding level for judgment J t is shown below. NEC A: Understanding Level J 15A (275) + Question Correction Value Q 15A (0)=275 NEC B: Understanding Level J 15B (340) + Question Correction Value Q 15A (0)=340 NEC C: Understanding Level J 15C (320) + Question Correction Value Q 15A (0)=320

[0146] Figure 27 shows an example of the level of understanding of each user 15 minutes after the start of the conference, when the Web conference server 1 has received each user's answer. Figure 27 shows each user's name N10, previous level of understanding P12, current measurement value M12, job title correction value C11, judgment level of understanding J12, question correction value Q12, and level of understanding P13 at this time.

[0147] The understanding level calculation unit 16 transmits the understanding level calculated as described above to the DB server 2. The DB server 2 updates the understanding level in the understanding level DB 22 with the understanding level received from the understanding level calculation unit 16.

[0148] 28 shows an example of employee names 222 and levels of understanding 223 in the updated level of understanding information held in the level of understanding DB 22 15 minutes after the start of the meeting. The levels of understanding of JEPCO employee A, JEPCO employee B, and JEPCO employee C are 275, 340, and 320, respectively.

[0149] After this, the Web conference system 100 continues to execute the same processes as those described above.

[0150] In the above example, the Web conference server 1 calculates the level of understanding every time a predetermined time (5 minutes) has elapsed. The Web conference server 1 may change the interval at which it calculates the level of understanding to any time.

[0151] The Web conference server 1 of this embodiment sends questions to confirm the understanding of words used in a Web conference to the user terminals 3 of users whose level of understanding is lower than a preset standard. The Web conference server 1 also generates information about the words based on the users' answers and sends that information to the user terminals 3 of users whose level of understanding is lower than the standard. As a result, the Web conference server 1 can improve the level of understanding of users participating in a Web conference.

[0152] The Web conference server 1 automatically asks questions about words. Therefore, even if a user is not aware of the meaning of a word, the Web conference server 1 can improve the user's understanding. Furthermore, the Web conference server 1 can ask questions without interrupting the progress of the conference.

[0153] The Web conference server 1 asks questions at an appropriate frequency depending on the level of understanding. Therefore, compared to a system that asks questions periodically, disruption of the Web conference due to an excessive number of questions is suppressed.

[0154] The Web conference server 1 calculates the level of understanding based on the results of analyzing the data acquired from the user terminal 3, and then updates the level of understanding based on the user's answers to the questions. The Web conference server 1 can update the level of understanding in real time as the Web conference progresses.

[0155] The Web conference server 1 analyzes the user's emotions based on the video data generated by the camera 31 of the user terminal 3. The Web conference server 1 can estimate the user's level of understanding based on the user's emotions.

[0156] The Web conference server 1 analyzes the amount of speech made by the user based on the voice data generated by the microphone 32 of the user terminal 3. The Web conference server 1 can estimate the level of understanding based on the amount of speech made by the user.

[0157] The Web conference server 1 analyzes the frequency of input by the user based on input data generated by the keyboard 33, which is an input device of the user terminal 3. The Web conference server 1 can estimate the level of understanding based on the frequency of input by the user.

[0158] <Second embodiment> An embodiment of the present disclosure will be described below with reference to the drawings. FIG. 29 shows the functional configuration of a Web conference system 100a of this embodiment. The Web conference system 100a includes a Web conference server 1, a DB server 2, and a user terminal 3a. The user terminal 3 in the Web conference system 100 shown in FIG. 1 is replaced with the user terminal 3a. The user terminal 3a includes a camera 31, a microphone 32, a keyboard 33, a question and answer unit 34, and a chat system 35.

[0159] The answer processing unit 15 has the following added functions: The answer processing unit 15 sends an instruction to display a "Question" button to the question and answering unit 34. The answer processing unit 15 receives chat post data and an instruction to summarize the chat post data from the question and answering unit 34. The answer processing unit 15 interprets the chat post data received from the question and answering units 34 of all user terminals 3 as the meaning of words and summarizes them. The answer processing unit 15 generates summary data and sends the summary data to the question and answering unit 34. The answer processing unit 15 receives the edited summary data from the question and answering unit 34 and registers it in the word DB 23.

[0160] The question and answering unit 34 has the following added functions: The question and answering unit 34 receives the "Question" button display instruction sent by the answer processing unit 15, and displays the "Question" button on the display 309. When a user presses the "Question" button, the question and answering unit 34 sends a chat start instruction to the chat system 35 to start a chat with users participating in the conference. The question and answering unit 34 receives chat post data and a chat post data summarization instruction sent by the chat system 35, and sends the chat post data and the chat post data summarization instruction to the answer processing unit 15. The question and answering unit 34 receives summary data sent by the answer processing unit 15, and sends the summary data to the chat system 35. The question and answering unit 34 receives edited summary data sent by the chat system 35, and sends the edited summary data to the answer processing unit 15.

[0161] The chat system 35 has the following functions. The chat system 35 receives a chat start instruction sent by the question and answering unit 34 and starts a chat with users participating in the conference. The chat system 35 posts text information on the chat and sends chat post data including the posted text information to the question and answering unit 34. The chat system 35 displays a "Register word" button on the display 309. When a user presses the "Register word" button, the chat system 35 sends a chat post data summary instruction to the question and answering unit 34.

[0162] The chat system 35 receives the summary data sent by the question and answering unit 34 and displays the summary data on the display 309. The chat system 35 displays an "Edit" button on the display 309 for editing the summary data. When the user presses the "Edit" button, the chat system 35 edits the summary data. The chat system 35 displays a "Register" button on the display 309 for registering the edited summary data in the word DB 23 of the DB server 2. When the user presses the "Register" button, the chat system 35 transmits the edited summary data to the question and answering unit 34.

[0163] The question generator 17 generates question data by executing the process shown in Fig. 30. Fig. 11 is changed to Fig. 30. The process shown in Fig. 30 will be described in detail.

[0164] The question generator 17 accesses the word DB 23 of the DB server 2. At this time, the question generator 17 transmits the frequently occurring words received from the message volume analyzer 13 to the DB server 2 (step S1161).

[0165] The DB server 2 searches the word DB 23 for the frequently occurring word received from the question generation unit 17. If the frequently occurring word is registered in the word DB 23, the DB server 2 obtains the meaning of the frequently occurring word from the word DB 23 and transmits the meaning to the question generation unit 17. If the frequently occurring word is not registered in the word DB 23, the DB server 2 transmits information indicating this to the question generation unit 17.

[0166] The question generation unit 17 determines whether or not the frequently-used word is registered in the word DB 23 based on the information received from the DB server 2. When the meaning of the frequently-used word is received from the DB server 2, the question generation unit 17 determines that the frequently-used word is registered in the word DB 23. When information indicating that the frequently-used word is not registered in the word DB 23 is received from the DB server 2, the question generation unit 17 determines that the frequently-used word is not registered in the word DB 23 (step S1163).

[0167] If the frequently occurring word is registered in the word DB 23, the question generation unit 17 uses the frequently occurring word received from the comment amount analysis unit 13 and the meaning received from the DB server 2 to generate question data in the form of a predetermined fixed phrase. The question generation unit 17 also generates correct answer data for the question by using the meaning received from the DB server 2. The question generation unit 17 transmits the question data to the question transmission unit 18, and transmits the correct answer data and the meaning of the frequently occurring word used to generate the question data to the answer processing unit 15 (step S1162).

[0168] If the frequently occurring word is not registered in the word DB 23, the question generation unit 17 determines whether the frequently occurring word is a general term or an internal company term. The question generation unit 17 makes this determination using a generation AI that learns information on the Internet and learns natural language. The question generation unit 17 asks the generation AI a question with two choices, "yes" or "no," indicating whether the AI understands the meaning of the frequently occurring word.

[0169] A question to the generation AI has a format such as, for example, "Can you tell me the meaning of the word XX? Please answer 'yes' or 'no'" (XX is a frequently occurring word). The wording used in the question may be changed to wording suitable for the generation AI. If the generation AI answers "yes", the question generation unit 17 determines that the frequently occurring word is a general term and that the generation AI can understand the meaning of the frequently occurring word. If the generation AI answers "no", the question generation unit 17 determines that the frequently occurring word is an in-house term and that the generation AI cannot understand the meaning of the frequently occurring word (step S1164).

[0170] If the generation AI can understand the meaning of the frequently occurring word, the generation AI notifies the question generation unit 17 of the meaning of the frequently occurring word acquired via the Internet. The question generation unit 17 generates question data and correct answer data by using the generation AI. The question generation unit 17 transmits the question data to the question transmission unit 18, and transmits the correct answer data and the meaning of the frequently occurring word to the answer processing unit 15 (step S1165).

[0171] If the generation AI cannot understand the meaning of the frequently occurring word, the question generation unit 17 generates question data using a fixed phrase as shown in the following example. The question generation unit 17 transmits the question data to the question transmission unit 18 (step S1166). The fixed phrase may be changed depending on the usage scenario. Standard phrase: "Do you know the meaning of ○○ (frequently used word)?"

[0172] If the generation AI cannot understand the meaning of the frequently occurring words, the question generation unit 17 does not generate correct answer data and does not transmit the correct answer data and the meaning of the frequently occurring words to the answer processing unit 15. In this case, the answer processing unit 15 does not retain the correct answer data and the meaning of the frequently occurring words.

[0173] The question generation unit 17 may search the Internet for frequently occurring words without using a generation AI. If a homepage or a post on a social networking service (SNS) containing a frequently occurring word is found, the question generation unit 17 may extract the meaning of the frequently occurring word from the homepage or the post on the SNS. For example, the question generation unit 17 may acquire, as the meaning of the frequently occurring word, a sentence following a sentence containing the character string "What is XX (frequently occurring word)?" In this case, the question generation unit 17 may execute the same process as in step S1165. If a homepage or a post on a social networking service (SNS) containing a frequently occurring word is not found, the question generation unit 17 may execute the same process as in step S1166.

[0174] The question sending unit 18 sends the question data to the question answering unit 34 of the user terminal 3. The question answering unit 34 receives the question data sent by the question sending unit 18 and displays the question and multiple-choice answer buttons on the display 309. If the answer processing unit 15 holds the correct answer data, the question answering unit 34 displays the question QU10, a "Yes" button B10, and a "No" button B11 on the display 309, as in the example shown in FIG.

[0175] FIG. 31 shows an example of the screen of the display 309 of the user terminal 3 when the answer processing unit 15 does not hold correct answer data. The display 309 displays a question QU11, a "Yes" button B10, and a "No" button B11 on the screen SC10. The question QU11 is a question generated by using a fixed phrase. When the user presses the "Yes" button B10 or the "No" button B11, the question answering unit 34 sends the answer data to the answer processing unit 15. The answer processing unit 15 sends the answer data to the understanding level calculation unit 16 instead of correct / incorrect data.

[0176] The understanding level calculation unit 16 judges the user's answer based on the answer data. When the user presses the "Yes" button B10, the understanding level calculation unit 16 calculates the understanding level J for judgment. t 1 or more questions correction value Q t By adding t If the user presses the "No" button B11, the understanding level calculation unit 16 calculates the understanding level J for judgment. t Without changing the understanding P t Use as.

[0177] Regardless of whether the user presses the "Yes" button B10 or the "No" button B11, the answer processing unit 15 sends a "Question" button display instruction to the question and answering unit 34. The question and answering unit 34 receives the "Question" button display instruction and displays the "Question" button on the display 309.

[0178] 32 shows an example of the screen of the display 309 of the user terminal 3 when the question and answering unit 34 receives an instruction to display the "Ask" button. The display 309 displays a sentence TX11 indicating the meaning of the frequently occurring word, a "Close" button B12, and a "Ask" button B13 on the screen SC10.

[0179] If the answer processing unit 15 does not hold correct answer data, the answer processing unit 15 does not generate correct / incorrect data. Instead of transmitting correct / incorrect data and the meanings of frequently occurring words to the question answering unit 34, the answer processing unit 15 transmits a fixed phrase to the question answering unit 34. The question answering unit 34 displays the fixed phrase as a sentence TX11 on the display 309.

[0180] When the user presses the "Close" button B12, the text TX11, the "Close" button B12, and the "Ask" button B13 disappear. Depending on the usage scenario, the "Close" button B12 may not be displayed. When the user presses the "Ask" button B13, the question and answer unit 34 sends a chat start instruction to the chat system 35.

[0181] The chat system 35 receives the chat start instruction from the question and answer unit 34 and starts a chat to ask all users participating in the conference about the meaning of the word. Figure 33 shows an example of the screen on the display 309 of the user terminal 3 after the chat system 35 has started the chat.

[0182] The chat system 35 makes a post PS10 saying, "Please tell me the meaning of XX (word)." In response to the post PS10, other users post answers related to the word (posts PS11 and PS12). The chat system 35 appropriately transmits the data posted to this chat to the question and answer unit 34, and the question and answer unit 34 transmits the data as chat posting data to the answer processing unit 15.

[0183] When a chat starts, the chat system 35 displays a "Register word" button B14 on the display 309. If a user who pressed the "Ask" button B13 determines that they have sufficiently understood the meaning of the word from posts by other users, they press the "Register word" button B14. When the user presses the "Register word" button B14, the chat system 35 sends an instruction to summarize the chat post data to the question and answer unit 34.

[0184] The question and answer unit 34 receives the chat post data summarization instruction sent by the chat system 35 and sends the chat post data summarization instruction to the answer processing unit 15. The answer processing unit 15 receives the chat post data summarization instruction and uses the generation AI to interpret the chat post data of all users participating in the conference as word meanings and summarize them. The answer processing unit 15 generates summary data and sends the summary data to the question and answering unit 34. The summary data includes words and their summarized meanings.

[0185] The question and answer unit 34 receives the summary data sent by the answer processing unit 15 and sends the summary data to the chat system 35. The chat system 35 receives the summary data. Figure 34 shows an example of the screen of the display 309 of the user terminal 3 when the chat system 35 receives the summary data.

[0186] Display 309 displays summary data AB10, an "Edit" button B15, and a "Register" button B16 on screen SC10. The user can edit summary data AB10 by pressing the "Edit" button B15. For example, the user can correct an incorrect expression or add additional information. When the user presses the "Register" button B16, chat system 35 generates edited summary data by adding edits to summary data AB10 and transmits the edited summary data to question and answer unit 34. The edited summary data includes words and summarized meanings of the words.

[0187] The question and answering unit 34 receives the edited summary data sent by the chat system 35 and sends the edited summary data to the answer processing unit 15. The answer processing unit 15 receives the edited summary data sent by the question and answering unit 34 and registers the words and their summarized meanings in the word DB 23 of the DB server 2. This allows the Web conferencing system 100a to use the registered words when generating questions from the next time onwards.

[0188] A specific example of the operation of the Web conference system 100a will be described. A Web conference consisting of multiple people is held. Each user terminal 3 transmits video data, audio data, and input data to the Web conference server 1. The Web conference server 1 calculates the understanding level for assessment based on the video data, audio data, and input data received from each user terminal 3.

[0189] The Web conference server 1 determines a threshold value based on each user's level of understanding for judgment, does nothing for users whose level of understanding for judgment is equal to or greater than the threshold, and asks questions about words that appear in the conference to users whose level of understanding for judgment is less than the threshold. The Web conference server 1 executes the above process at five-minute intervals. Below, we will explain an example in which frequently occurring words are not registered in the word DB 23 five minutes and ten minutes after the start of the conference.

[0190] First, a web conference is held with the participation of three people: a new employee, NEC employee A, a supervisor, NEC employee B, and a division manager, NEC employee C. Each user terminal 3 transmits video data, audio data, and input data to the web conference server 1. The web conference server 1 calculates measurement values based on the video data, audio data, and input data received from each user terminal 3, and calculates the comprehension level for assessment based on the measurement values and each user's attributes.

[0191] Five minutes after the start of the web conference, the understanding level for judgment of NEC employee A falls below the threshold. The question generation unit 17 executes the process shown in FIG. 30 to generate a question about "PaaS," which was received as a frequently occurring word from the speech volume analysis unit 13. The question generation unit 17 accesses the word DB 23 and attempts to obtain the meaning of "PaaS." Since the meaning of "PaaS" is not registered in the word DB 23, the question generation unit 17 obtains the meaning of the word via the Internet by using a generation AI. The question generation unit 17 generates question data by using the frequently occurring words, their meanings, and the generation AI. The question generation unit 17 also generates correct answer data.

[0192] Examples of question data, correct answer data, and meanings of frequently occurring words are shown below. Question data: "PaaS is a type of cloud computing service that uses virtualization technology to provide digital infrastructure such as hardware resources (CPU / memory / storage) on demand via the Internet." Response data: "No" Commonly used words: "PaaS" refers to the provision of platform functions for application execution, such as virtualized application servers and databases, as a service over the Internet."

[0193] The question generation unit 17 transmits the question data to the question transmission unit 18, and transmits the correct answer data and the meanings of the frequently occurring words used to generate the question data to the answer processing unit 15. The question transmission unit 18 transmits the question data to the question answering unit 34 of the user terminal 3 of the target user, NEC Corporation A. The question answering unit 34 receives the question data transmitted by the question transmission unit 18, and displays the question and answer buttons on the display 309.

[0194] 35 shows an example of the screen of the display 309 of the user terminal 3. The display 309 displays a question QU12, a "Yes" button B10, and a "No" button B11 on a screen SC10.

[0195] For example, NEC employee A presses the "No" button B11 to select "No" as the answer to the question. The question answering unit 34 sends answer data indicating "No" to the answer processing unit 15 of the Web conference server 1. The answer processing unit 15 compares the correct answer data with the answer data and generates correct / incorrect data.

[0196] The answer processing unit 15 transmits the correct / incorrect data and the meanings of the frequently occurring words to the question answering unit 34 of the user terminal 3. The question answering unit 34 receives the correct / incorrect data and the meanings of the frequently occurring words, and displays on the display 309 whether the user's answer is correct or not, as well as the meanings of the frequently occurring words.

[0197] The understanding level calculation unit 16 calculates the understanding level and transmits the understanding level to the DB server 2. The DB server 2 updates the understanding level in the understanding level DB 22 with the understanding level received from the understanding level calculation unit 16.

[0198] Ten minutes after the start of the web conference, the understanding level for judgment of NEC employee A falls below the threshold again. The question generation unit 17 executes the process shown in FIG. 30 to generate a question about "SV95," which was received as a frequently occurring word from the speech volume analysis unit 13. The question generation unit 17 accesses the word DB 23 and attempts to obtain the meaning of "SV95." The meaning of "SV95" is not registered in the word DB 23, and "SV95" is an internal company term that does not exist on the Internet.

[0199] The question generation unit 17 does not generate answer data, but transmits question data of a fixed phrase, "Do you know the meaning of 'SV95'?" to the question transmission unit 18. The question transmission unit 18 transmits the question data to the question answering unit 34 of the user terminal 3 of the target user, NEC Corporation A. The question answering unit 34 receives the question data transmitted by the question transmission unit 18, and displays the question and answer buttons on the display 309.

[0200] For example, NEC employee A presses the "No" button to select "No" as the answer to the question. The question answering unit 34 transmits answer data indicating "No" to the answer processing unit 15 of the Web conference server 1. The answer processing unit 15 receives the answer data and transmits it to the understanding level calculation unit 16. Because the user pressed the "No" button, the understanding level calculation unit 16 updates the understanding level with the understanding level for assessment.

[0201] The answer processing unit 15 sends an instruction to display the "Question" button to the question and answering unit 34. The question and answering unit 34 receives the instruction to display the "Question" button and displays the "Question" button on the display 309.

[0202] When NEC employee A presses the "Ask a question" button, the question and answering unit 34 sends a chat start instruction to the chat system 35. The chat system 35 starts the chat and posts PS10, saying "Please tell me the meaning of SV95," as shown in FIG. 33. In response to post PS10, other users post answers related to the word (posts PS11 and PS12). The chat system 35 appropriately sends the data posted to this chat to the question and answering unit 34, and the question and answering unit 34 sends the data to the answer processing unit 15 as chat posting data.

[0203] When the user A of NEC Corporation fully understands the meaning of the word, he / she presses the “Register word” button B14 shown in Fig. 33. When the user presses the “Register word” button B14, the chat system 35 sends an instruction to summarize the chat posting data to the question and answer section 34.

[0204] The question and answering unit 34 transmits an instruction to summarize the chat posted data to the answer processing unit 15. The answer processing unit 15 summarizes the chat posted data to generate summary data. The answer processing unit 15 transmits the summary data to the question and answering unit 34.

[0205] The question and answer unit 34 receives the summary data sent by the answer processing unit 15 and sends the summary data to the chat system 35. The chat system 35 receives the summary data and displays summary data AB10, an "Edit" button B15, and a "Register" button B16 shown in FIG. 34 on the display 309.

[0206] Although NEC employee A can edit the summary data by pressing the "Edit" button B15, he decides that editing is not necessary and presses the "Register" button B16 without editing. When NEC employee A presses the "Register" button B16, the chat system 35 generates edited summary data based on the summary data AB10 and transmits the edited summary data to the question and answer section 34.

[0207] The question and answering section 34 transmits the edited summary data to the answer processing section 15. The answer processing section 15 registers the words indicated by the edited summary data and the summarized meanings of the words in the word DB 23.

[0208] 36 shows an example of a word 232 and a meaning 233 in the word information held in the word DB 23. A word W10 indicating "SV95" and a meaning M10 of "SV95" are newly registered.

[0209] As described above, the Web conferencing system 100 can build a word DB 23 according to the content of the conference. As the number of conferences increases, data in the word DB 23 accumulates, and the number of times users ask questions via chat decreases. This allows users to smoothly understand unknown words that appear in the conference, improving the quality of the conference.

[0210] If a frequently occurring word is not registered in the word DB 23, the question generator 17 acquires the meaning of the frequently occurring word via the Internet and generates question data based on the acquired meaning. Even if a frequently occurring word is not registered in the word DB 23, if its meaning can be acquired from information on the Internet, the Web conference server 1 can ask a question without interrupting the progress of the conference.

[0211] <Third embodiment> An embodiment of the present disclosure will be described below with reference to the drawings. FIG. 37 shows the functional configuration of an information processing device 4 according to this embodiment. The information processing device 4 includes a data analysis unit 41, a level of understanding calculation unit 42, a question generation unit 43, a question transmission unit 44, a response reception unit 45, and a response processing unit 46. The data analysis unit 41 receives response data indicating responses of users participating in a web conference from the user terminals used by the users in the web conference and analyzes the response data. The level of understanding calculation unit 42 calculates the level of understanding of the users based on the analysis of the response data by the data analysis unit 41. The question generation unit 43 generates questions to confirm the understanding of words used in the web conference. The question transmission unit 44 transmits questions to user terminals of users whose levels of understanding are lower than a preset standard. The response reception unit 45 receives answers to the questions from the user terminals. The response processing unit 46 generates information about the words based on the answers and transmits the information to the user terminals of users whose levels of understanding are lower than the standard.

[0212] By having the above configuration, the information processing device 4 can improve the level of understanding of users participating in a Web conference.

[0213] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0214] The above-described embodiment may be used in the field of education. In recent years, online classes have become common in educational institutions such as schools and cram schools. In online classes, teachers teach students in remote locations via a web conferencing system. Online classes are categorized into live streaming classes that involve two-way interaction in real time, and on-demand classes that distribute pre-recorded lessons.

[0215] For example, the above-described embodiment can be used for live-streamed online classes. Live-streamed online classes are generally taught by one teacher to multiple students, and the teacher must pay attention to all of the students. However, it is difficult for the teacher to provide support to each student without disrupting the progress of the class and within time constraints. There are cases where students are unable to keep up with the class unless they are supported by the teacher. Applying the above-described embodiment to online classes improves the students' understanding of the class, providing benefits to both the teacher and the students.

[0216] Some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes.

[0217] (Appendix 1) a data analysis means for receiving reaction data indicating reactions of users participating in a web conference from user terminals used by the users in the web conference and analyzing the reaction data; an understanding level calculation means for calculating the user's understanding level based on the result of analyzing the reaction data; A question generation means for generating questions to confirm understanding of words used in the web conference; a question sending means for sending the question to a user terminal of a user whose understanding level is lower than a preset standard; answer receiving means for receiving an answer to the question from the user terminal; an answer processing means for generating information about the word based on the answer and transmitting the information to the user terminal of the user whose understanding level is lower than the standard; Equipped with Information processing device.

[0218] (Appendix 2) the understanding level calculation means updates the understanding level based on the answer. 2. The information processing device according to claim 1.

[0219] (Appendix 3) the data analysis means receives video data generated by a camera included in the user terminal as the reaction data, and analyzes the user's emotions based on the video data; 3. The information processing device according to claim 1 or 2.

[0220] (Appendix 4) the data analysis means receives voice data generated by a microphone provided in the user terminal as the reaction data, and analyzes the amount of speech made by the user based on the voice data; 4. An information processing device according to any one of claims 1 to 3.

[0221] (Appendix 5) the data analysis means receives input data input by the user to the user terminal as the reaction data, and analyzes the input frequency of the user based on the input data; 5. An information processing device according to any one of appendices 1 to 4.

[0222] (Appendix 6) The system further comprises a word extraction means for receiving voice data generated by a microphone provided in the user terminal and extracting words included in the user's speech based on the voice data, If the extracted word is registered in a database that stores words and their meanings, the question generation means generates the question based on the meaning of the word. 6. An information processing device according to any one of appendices 1 to 5.

[0223] (Appendix 7) If the extracted word is not registered in the database, the question generation means acquires the meaning of the word via the Internet, generates the question based on the acquired meaning, and updates the database. 7. The information processing device according to claim 6.

[0224] (Appendix 8) If the meaning of the word cannot be obtained via the Internet, the answer processing means updates the database based on the meaning of the word obtained by the user terminal from another user through chat. 8. The information processing device according to claim 7.

[0225] (Appendix 9) An information processing device according to any one of Supplementary Notes 1 to 8; the user terminal that transmits the reaction data and the answer to the information processing device and receives the question and the information from the information processing device; Equipped with Information processing system.

[0226] (Appendix 10) Receiving reaction data indicating reactions of users participating in a web conference from user terminals used by the users in the web conference; Analyzing the reaction data; calculating a level of understanding of the user based on a result of analyzing the reaction data; generating questions to confirm understanding of words used in the web conference; transmitting the question to a user terminal of a user whose understanding level is lower than a preset standard; receiving an answer to the question from the user terminal; generating information about the word based on the answer; transmitting the information to the user terminal of the user whose understanding level is lower than the standard; An information processing method for an information processing device.

[0227] (Appendix 11) updating the understanding level based on the response; 11. The information processing method according to claim 10.

[0228] (Appendix 12) receiving video data generated by a camera included in the user terminal as the reaction data; Analyzing the user's emotions based on the video data; 12. The information processing method according to claim 10 or 11.

[0229] (Appendix 13) receiving, as the reaction data, voice data generated by a microphone provided in the user terminal; analyzing the amount of speech of the user based on the voice data; 13. An information processing method according to any one of appendices 10 to 12.

[0230] (Appendix 14) receiving input data input by the user to the user terminal as the reaction data; Analyzing the frequency of input by the user based on the input data; 14. An information processing method according to any one of appendices 10 to 13.

[0231] (Appendix 15) receiving voice data generated by a microphone provided in the user terminal; extracting words contained in the user's speech based on the voice data; If the extracted word is registered in a database that stores words and their meanings, the question is generated based on the meaning of the word. 15. An information processing method according to any one of appendices 10 to 14.

[0232] (Appendix 16) If the extracted word is not registered in the database, the meaning of the word is obtained via the Internet; generating the question based on the obtained meaning; and updating the database; 16. The information processing method according to claim 15.

[0233] (Appendix 17) If the meaning of the word cannot be obtained via the Internet, the database is updated based on the meaning of the word obtained by the user terminal from another user through chat. 17. The information processing device according to claim 16.

[0234] (Appendix 18) A program for causing a computer to execute the information processing method according to any one of appendices 10 to 17. [Explanation of symbols]

[0235] 100,100a Web conferencing system 1 Web conference server 2 DB Server 3,3a User terminal 4. Information processing equipment 11 User Identification Unit 12 Sentiment Analysis Department 13 Speech volume analysis section 14 Keyboard input analysis section 15. Response Processing Section 16 Comprehension calculation part 17 Question generation part 18 Question Submission Department 21 Employee Information DB 22 Comprehension DB 23 Word Database 31 Camera 32. Mike 33 keyboard 34 Question and answer section 35 Chat System 41 Data Analysis Methods 42 Understanding calculation method 43 Question generation means 44 Question submission method 45 Response receiving means 46 Response Processing Method

Claims

1. a data analysis means for receiving reaction data indicating reactions of users participating in a web conference from user terminals used by the users in the web conference and analyzing the reaction data; an understanding level calculation means for calculating the user's understanding level based on the result of analyzing the reaction data; a question generation means for generating questions to confirm understanding of words used in the Web conference; a question sending means for sending the question to a user terminal of a user whose understanding level is lower than a preset standard; answer receiving means for receiving an answer to the question from the user terminal; an answer processing means for generating information about the word based on the answer and transmitting the information to the user terminal of the user whose understanding level is lower than the standard; Equipped with Information processing device.

2. the understanding level calculation means updates the understanding level based on the answer. The information processing device according to claim 1 .

3. the data analysis means receives video data generated by a camera included in the user terminal as the reaction data, and analyzes the user's emotions based on the video data; 3. The information processing device according to claim 1.

4. the data analysis means receives voice data generated by a microphone provided in the user terminal as the reaction data, and analyzes the amount of speech made by the user based on the voice data; 3. The information processing device according to claim 1.

5. the data analysis means receives input data input by the user to the user terminal as the reaction data, and analyzes the input frequency of the user based on the input data; 3. The information processing device according to claim 1.

6. The system further comprises a word extraction means for receiving voice data generated by a microphone provided in the user terminal and extracting words included in the user's speech based on the voice data, If the extracted word is registered in a database that stores words and their meanings, the question generation means generates the question based on the meaning of the word.

3. The information processing device according to claim 1.

7. If the extracted word is not registered in the database, the question generation means acquires the meaning of the word via the Internet, generates the question based on the acquired meaning, and updates the database. The information processing device according to claim 6 .

8. If the meaning of the word cannot be obtained via the Internet, the answer processing means updates the database based on the meaning of the word obtained by the user terminal from another user through chat. The information processing device according to claim 7 .

9. The information processing device according to claim 1 or 2; the user terminal that transmits the reaction data and the answer to the information processing device and receives the question and the information from the information processing device; Equipped with Information processing system.

10. Receiving reaction data indicating reactions of users participating in a Web conference from user terminals used by the users in the Web conference; Analyzing the reaction data; calculating a level of understanding of the user based on a result of analyzing the reaction data; generating questions to confirm understanding of words used in the web conference; transmitting the question to a user terminal of a user whose understanding level is lower than a preset standard; receiving an answer to the question from the user terminal; generating information about the word based on the answer; transmitting the information to the user terminal of the user whose understanding level is lower than the standard; An information processing method for an information processing device.

11. On the computer, Receiving reaction data indicating reactions of users participating in a Web conference from user terminals used by the users in the Web conference; Analyzing the reaction data; calculating a level of understanding of the user based on a result of analyzing the reaction data; generating questions to confirm understanding of words used in the web conference; transmitting the question to a user terminal of a user whose understanding level is lower than a preset standard; receiving an answer to the question from the user terminal; generating information about the word based on the answer; The information is transmitted to the user terminal of the user whose understanding level is lower than the standard. A program for performing information processing.

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