Information processing device and information processing method

Abstract

An information processing device (10) is provided with: a phase estimation unit (12) for estimating the phase of a discussion on the basis of statement content data indicating the content of statements made by respective participants in the discussion; a progress calculation unit (13) for calculating an index value pertaining to at least one index, said index value being determined as the degree of progress of the discussion in accordance with the estimated phase; a state estimation unit (14) for estimating the state of the discussion on the basis of the calculated index value; and a determination unit (15) for determining the content of facilitation on the basis of an evaluation pertaining to the estimated state of the discussion.

Description

Information Processing Apparatus and Information Processing Method

[0001] The present disclosure relates to an information processing apparatus and an information processing method.

[0002] Technologies related to facilitation, a technique for smoothly proceeding with meetings and improving the significance of meetings for participants, are known. A "good meeting" in facilitation refers to a state where a conclusion is reached with the consent of all participants within a predetermined time. The role of a facilitator, who implements facilitation, is not to directly present various ideas themselves to achieve a good meeting, but rather to elicit opinions from participants to facilitate smooth discussion and organize the discussion such that all participants can agree. Implementing the above-described facilitation requires multiple advanced skills, for example, perceiving the discussion status, digging deeper into ideas, presenting new perspectives, and organizing the discussion. Therefore, the concentration of burden caused by relying on a specific person with such skills is a problem, and it is also difficult to guarantee the quality of the meeting.

[0003] Patent Document 1 below describes a technology that picks out topics that have not been discussed in depth (i.e., topics with a high level of abstraction) from the discourse structure of discussion content, and determines content for a facilitator's question (e.g., prompting content to be more specific).

[0004] Japanese Patent No. 7360655

[0005] However, in order to achieve a good meeting, it is necessary not only to materialize ideas that have already been proposed as in Patent Document 1, but also to conduct more comprehensive discussion, and implement facilitation from multifaceted perspectives such as reaching a conclusion agreed upon by all participants within a predetermined time.

[0006] Furthermore, when facilitating a discussion, the perspectives (indicators) to focus on regarding the discussion's progress differ depending on the phase of the discussion, as follows: For example, in the divergent phase, the goal is to present a greater variety of ideas, so attention should be paid to the number of ideas and the number of different types of ideas. However, in the convergence phase, attention should be paid to whether the opinions are converging rather than the number of ideas presented. In the clarification phase, the focus should be on whether there are any gaps or omissions in the analysis of the factors involved in the problem, rather than the number of factors presented.

[0007] Based on the above, this disclosure aims to ensure that even when the phase of discussion changes during a meeting, participants can grasp the status of the discussion from an appropriate perspective according to the changing phase, thereby enabling efficient and effective meetings.

[0008] The information processing device relating to this disclosure includes: a phase estimation unit that estimates the phase of a discussion based on statement data showing the content of statements made by each participant in the discussion; a progress calculation unit that calculates an index value for at least one indicator to be determined as the progress of the discussion according to the estimated phase; a situation estimation unit that estimates the status of the discussion based on the calculated index value; and a determination unit that determines the content of facilitation based on an evaluation of the estimated status of the discussion.

[0009] According to this disclosure, even if the phase of discussion changes during a meeting, it is possible to grasp the status of the discussion from an appropriate perspective corresponding to the changing phase, and to efficiently conduct a good meeting.

[0010] This is a functional block diagram of the information processing device. This is a diagram showing examples of prompts used for phase estimation. This is a diagram to explain the function of the phase estimation unit. This is a diagram showing examples of prompts used to determine whether an agenda item is reasonable or not. This is a diagram to explain the calculation of teamwork scores. This is a flowchart of the processing performed by the information processing device. This is a diagram showing examples of prompts used to generate facilitation content in the convergence phase. This is a diagram to explain examples of processing in the divergence phase or clarification phase by the decision unit. This is a diagram showing a modified configuration of the information processing device. This is a diagram showing an example of the hardware configuration of the information processing device.

[0011] Hereinafter, an embodiment of the information processing apparatus and information processing method relating to this disclosure will be described with reference to the drawings.

[0012] [Configuration of the Information Processing Device] Figure 1 shows a configuration diagram of the information processing device 10. As shown in Figure 1, the information processing device 10 includes an acquisition unit 11, a phase estimation unit 12, a progress calculation unit 13, a status estimation unit 14, a determination unit 15, and an output unit 16 as components for realizing the functions related to this disclosure. The functions of each unit will be described below.

[0013] The acquisition unit 11 is a functional unit that acquires speech content data indicating the content of each participant's statements from the audio data of the discussion, and includes an audio acquisition unit 11A and a speech recognition unit 11B. The "speech content data" is, for example, text data for each participant indicating the content of each participant's statements. The audio acquisition unit 11A acquires the audio data of the discussion collected by microphones set up in the conference room, the speech recognition unit 11B acquires audio data for each participant by identifying speakers based on existing technology using the acquired audio data, and acquires speech content data for each participant by converting the acquired audio data for each participant into text data.

[0014] The phase estimation unit 12 is a functional unit that estimates the phase of the discussion based on the content data of each participant's statements obtained. Specifically, the phase estimation unit 12 estimates which of the following phases of the discussion it falls into: - A divergent phase focused on generating more ideas; - A convergent phase focused on converging opinions; - An elucidation phase focused on analyzing the factors of the problem. As one estimation method, the phase estimation unit 12 may input prompts, as exemplified in Figure 2, from the content of the utterance data into a large language model (LLM) built into the information processing device 10 or running on an external server, and obtain an appropriate phase estimation result from the LLM.

[0015] The progress calculation unit 13 is a functional unit that calculates indicator values ​​for indicators that should be determined as the progress of the discussion according to the estimated phase. Specifically, as shown in Figure 3, the progress calculation unit 13 calculates the number of factors, the number of categories, and the degree of factor abstraction as the progress of the discussion when the estimated phase is the clarification phase, the number of ideas, the number of categories, and the degree of idea abstraction as the progress of the discussion, and the degree of convergence of opinions in the discussion when the estimated phase is the convergence phase.

[0016] The following is an example of how to calculate the individual indicator values ​​mentioned above that relate to the progress of the discussion.

[0017] Regarding the "number of ideas," the progress calculation unit 13 extracts ideas included in the statement content data (i.e., ideas that have come up in the discussion so far) and calculates the number of extracted ideas as the number of ideas. For example, the progress calculation unit 13 may send a prompt to the aforementioned LLM to inquire about the number of ideas included in the statement content data, along with the statement content data itself, and calculate the number of ideas responded to by the LLM as the number of ideas. However, the ideas targeted here may be limited to only promising ideas. Specifically, as shown in Figure 4, the LLM may be inquired about using a prompt that extracts only promising ideas for the agenda, and the ideas extracted by the LLM may be targeted as promising ideas. The "number of factors" may also be calculated using the same method as the "number of ideas" described above.

[0018] Regarding the "number of categories," the progress calculation unit 13 extracts ideas included in the utterance data (i.e., ideas that have emerged in previous discussions), categorizes the extracted ideas according to predetermined criteria, and calculates the number of categories obtained as the number of categories. For example, the phase estimation unit 12 may, similar to the number of ideas described above, send a prompt to the LLM to inquire about the number of categories included in the utterance data, along with the utterance data itself, and calculate the number of categories responded to by the LLM as the number of categories. Alternatively, the number of categories may be calculated by inputting the target utterance data into a machine learning model trained by machine learning, where the utterance data is the explanatory variable and the categories or the number of categories is the dependent variable, and obtaining the output categories or the number of categories. When calculating the number of categories, the aforementioned method may be used to narrow down the target ideas to only those with promising potential.

[0019] Regarding the "level of abstraction of an idea," the progress calculation unit 13 estimates it, for example, by following these steps: (1) The progress calculation unit 13 divides a sentence related to a certain idea, included in the spoken content data of each participant, into multiple words by morphological analysis; (2) It calculates the co-occurrence probability of each divided word with a word list included in a dictionary containing words with multiple meanings for word sense disambiguation (WSD); (3) Then, for each sentence related to the idea included in the spoken content data, it repeats (1) and (2) above to calculate the co-occurrence probability of each word in the entire sentence related to the idea, calculates the average value of the obtained co-occurrence probabilities, and calculates the obtained average value as the level of abstraction of the idea. Note that the "level of abstraction" may also be information indicating whether the idea is sufficiently specific (or abstract) in relation to the topic, and specifically may be a numerical value in the range of 0 to 100, or information indicating which level it is among several predetermined levels. Note that the "level of abstraction of factors" may also be calculated using the same method as the idea abstraction described above.

[0020] Regarding the "degree of opinion convergence," the progress calculation unit 13 vectorizes the content of the statements using methods such as TF-IDF and BERT embedding, clusters the resulting vectors into multiple clusters using methods such as K-means and Hierarchical Clustering, and calculates the degree of opinion convergence in the discussion based on the reduction in the number of clusters or the bias in the number of clusters. Specifically, the degree of opinion convergence is calculated by following a series of steps including (a) vectorization of the content of the statements, (b) clustering of the vectors, and (c) evaluation of the degree of opinion convergence. The following will explain using a specific example. Here, the following is assumed as an example of discussion data including the agenda and the log of the statements in the discussion. Topic: "Pricing for the new product" Log of discussion comments: Comment 1: "I think we should set the price low, especially to attract early customers." Comment 2: "I agree with lowering the price, but that could lower the profit margin." Comment 3: "Considering the profit margin, shouldn't we also consider setting the price a little higher?" Comment 4: "As a strategy to attract early customers, how about distributing discount coupons?" Comment 5: "That's a good idea. I think it's smart to attract early customers with discounts."

[0021] In the hypothetical example described above, in "(a) Vectorization of the utterance content," the progress calculation unit 13 first tokenizes the utterance content. For example, tokenizing utterance 1 above yields the following: ["I", "set", "low", "price", "should", "I think", "especially", "to acquire", "initial customers"] Next, the progress calculation unit 13 removes stop words. Here, it removes frequently occurring but low-information words (e.g., "I", "is", "but", etc.), and then vectorizes them using TF-IDF and an arbitrary machine learning model (e.g., BERT).

[0022] In the next step, "(b) Vector Clustering," the progress calculation unit 13 classifies (clusters) the content of the statements into several clusters using methods such as K-means and Hierarchical Clustering. For example, the content of statements 1 to 5 above is classified (clustered) into the following three clusters. Here, the number of elements is always 2. Cluster 1: "Set price low" "Acquire initial customers" Cluster 2: "Profit margin" "Set price high" Cluster 3: "Discount coupon" "Initial customer strategy"

[0023] In the next step, "(c) Evaluation of the degree of convergence of opinions," the progress calculation unit 13 checks whether the discussion is converging based on whether the number of clusters has decreased or whether there is a large bias in the number of clusters. For example, whether the number of clusters has decreased can be determined by the value of (current number of clusters) / (number of clusters at the start of the discussion), and whether there is a large bias in the number of clusters can be determined by the value of (variance of the number of elements in each cluster at the current time) / (variance of the number of elements in each cluster at the start of the discussion).

[0024] Returning to the explanation of Figure 1, the situation estimation unit 14 is a functional unit that estimates the state of the discussion based on the calculated index values. Specifically, the situation estimation unit 14 estimates the following scores related to the state of the discussion based on the calculated index values: "teamwork degree" based on the distribution of participant statements during the discussion, "statement degree" indicating the degree to which important statements are made among the statements of all participants, and "agenda relevance degree" indicating the degree to which statements relevant to the agenda are made within a certain time. In this embodiment, an example is shown in which all three scores are estimated, but at least one of the above three may be estimated. The calculation of the above three scores will be explained below.

[0025] Regarding the "degree of teamwork," the situation estimation unit 14 calculates it based on the distribution of participant statements during the discussion, using a statistical measure called the coefficient of variation, with a higher value in the range of 0.0 to 1.0 indicating a higher degree of compatibility. The coefficient of variation is the value obtained by dividing the standard deviation by the mean, and is a unitless numerical value used to relatively evaluate the variability of data with different units, and the relationship between data and variability relative to the mean.

[0026] As shown in Figure 5, (i) The situation estimation unit 14 calculates the average number of times each participant has spoken. If participant A has spoken 3 times, participant B has spoken 8 times, and participant C has spoken 7 times, as shown in Figure 5, the average number of times everyone has spoken is calculated to be "6 times". (ii) The situation estimation unit 14 calculates the standard deviation σ from the variance of each participant's number of speaking times relative to the calculated average. In the example in Figure 5, the variance of each participant's number of speaking times relative to the average is "4.666...", so the standard deviation σ is calculated to be "2.160...". (iii) Then, the situation estimation unit 14 calculates the coefficient of variation using the following formula (1), and as shown in Figure 5, "36%" is obtained. Coefficient of variation = (standard deviation / mean) (1) (iv) Furthermore, the situation estimation unit 14 calculates the degree of teamwork using the following formula (2), and as shown in Figure 5, "64%" is obtained. Teamwork score = 100 - coefficient of variation (2) If the calculated value exceeds 100%, the teamwork score is set to 100%, and if the calculated value is less than 0%, the teamwork score is set to 0%.

[0027] Next, regarding "speaker level," the situation estimation unit 14 calculates the speaker level, which indicates the degree to which each participant's statement is important, using the following procedure: (i) As a preprocessing step, the situation estimation unit 14 performs tokenization (word segmentation) and normalization (unification of character types, case conversion) for each participant's statement. (ii) Then, the situation estimation unit 14 calculates the speaker level for each participant's statement from the information obtained in (i) above. As for the calculation method here, existing methods for calculating the importance of words appearing in a document, such as TF-IDF (Term Frequency-Inverse Document Frequency) and Okapi BM25, may be used.

[0028] Furthermore, the "relevance of the agenda" is calculated by the situation estimation unit 14 using, for example, the following first method, second method, etc.

[0029] In the first method, the situation estimation unit 14 determines whether or not there have been any statements related to the purpose of the agenda within a certain period of time. For example, the situation estimation unit 14 may provide the aforementioned LLM with statement content data and information on the purpose of the agenda, and send a prompt instructing it to "determine whether or not the various statements that can be grasped from the statement content data are related to the purpose of the agenda, and to determine the degree of relevance of the statements to the purpose of the agenda in the overall discussion within a predetermined numerical range (for example, a range of 0.0 to 1.0: the higher the number, the higher the degree of relevance)," and estimate the degree of relevance answered by the LLM as the agenda relevance.

[0030] In the second method, the situation estimation unit 14 determines whether there are any statements that are relevant to the topic within a certain time. Specifically, the situation estimation unit 14 (i) vectorizes the topic and each statement, (ii) calculates the similarity (e.g., cosine similarity) between the vectorized topic and each vectorized statement, and (iii) calculates the minimum value among the multiple similarities calculated as the topic relevance. It should be noted that using the minimum value as described above has the advantage of being able to confirm whether a certain theme relevance is maintained as a whole statement, even if some statements deviate from the topic.

[0031] The following is a concrete example of the second method. Here, we assume the following as an example of discussion data, including the topic and the log of comments made during the discussion: Topic: "The progress of AI technology and its social impact" Log of comments made during the discussion: Comment a: "AI technology is progressing rapidly." Comment b: "Data privacy issues are becoming increasingly important." Comment c: "Technological development of self-driving cars is progressing." Comment d: "New regulations for privacy protection are needed."

[0032] The situation estimation unit 14 vectorizes the above agenda items and each statement, and calculates the cosine similarity between the vectorized agenda items and each vectorized statement using existing technology. Let's assume that the following cosine similarities are obtained: Similarity related to statement a: 0.777764051839938 Similarity related to statement b: 0.7738009682636184 Similarity related to statement c: 0.7976624298419579 Similarity related to statement d: 0.7404025807062681 Here, the situation estimation unit 14 calculates the minimum value among the multiple calculated similarities as the agenda relevance. In the above example, the similarity related to statement d is the minimum value, so an agenda relevance of 74% is obtained.

[0033] Returning to the explanation of Figure 1, the decision unit 15 is a functional unit that determines the content of the facilitation based on an evaluation of the estimated state of the discussion. The decision processing by the decision unit 15 will be described in detail later, but it performs the following processing which differs for each phase. When the discussion phase is the convergence phase, the decision unit 15 uses the degree of convergence of opinions A1 calculated by the progress calculation unit 13, the degree of teamwork A2 estimated by the situation estimation unit 14, and the degree of topic relevance A3 to calculate evaluation values ​​for the progress of the discussion, namely the progress of convergence B1 (B1=A1), the degree of teamwork B2 (B2=A2), and the degree of topic relevance B3 (B3=A3), and then determines the content of the facilitation based on the calculated evaluation values. On the other hand, if the discussion phase is a divergent phase or a clarifying phase, the decision unit 15 evaluates the state of the discussion from four perspectives, based on the estimated state of the discussion, for example, (a) the degree of specificity of the ideas, (b) the degree of comprehensiveness of the discussion, (c) the degree of participant satisfaction, and (d) the degree of relevance to the agenda. The details will be described later.

[0034] The output unit 16 is a functional unit that outputs the content of the facilitation determined by the determination unit 15. In this case, "output" can take various forms, such as display output, print output, and data transmission to an external device 10.

[0035] [Regarding the processing performed in the information processing device] The processing performed in the information processing device 10 (processing related to the information processing method of this disclosure) will be described below in accordance with the flowchart in Figure 6. For example, when a user (operator) of the information processing device 10 inputs a predetermined execution start command, the processing in Figure 6 is started in the information processing device 10.

[0036] First, the voice acquisition unit 11A acquires audio data of the discussion collected by microphones set up in the conference room (step S1 in Figure 2). Then, the speech recognition unit 11B acquires audio data for each participant by identifying the speaker based on existing technology using the acquired audio data, and acquires content data of each participant's statements by converting the acquired audio data for each participant into text data based on existing speech recognition technology (step S2).

[0037] Next, the phase estimation unit 12 estimates which of the following phases the discussion falls into, based on the speech content data showing the content of each participant's speech (step S3). These phases are: - Divergent phase, which focuses on generating more ideas; - Convergent phase, which focuses on converging opinions; - Clarification phase, which focuses on analyzing the factors of the problem. For example, as mentioned above, the phase estimation unit 12 inputs the prompts exemplified in Figure 2 into the LLM based on the content of the speech data, and obtains an appropriate phase estimation result from the LLM.

[0038] Next, the progress calculation unit 13 calculates indicator values ​​for the indicators that should be determined as the progress of the discussion according to the estimated phase (step S4). Specifically, as shown in Figure 3, if the estimated phase is the clarification phase, the progress calculation unit 13 calculates the number of factors, the number of categories, and the degree of factor abstraction as the progress of the discussion; if the estimated phase is the divergence phase, it calculates the number of ideas, the number of categories, and the degree of idea abstraction as the progress of the discussion; and if the estimated phase is the convergence phase, it calculates the degree of opinion convergence as the progress of the discussion. The methods for calculating each indicator value are as described above, and redundant explanations are omitted here.

[0039] Next, the situation estimation unit 14 estimates the state of the discussion based on the calculated index values ​​(step S5). Specifically, the situation estimation unit 14 estimates the following as scores related to the state of the discussion: - Teamwork level based on the distribution of participant comments during the discussion, - Contribution level indicating the degree to which important comments are made among all participants' comments, and - Agenda relevance level indicating the degree to which comments relevant to the agenda are made within a certain time. The estimation methods for each score are as described above, and therefore, redundant explanations are omitted here.

[0040] Next, the decision unit 15 evaluates the status of the discussion based on the estimated score regarding the status of the discussion (step S6), and determines the content of the facilitation based on the evaluation result (step S7). Since the processing in steps S6 and S7 differs depending on the discussion phase, they will be explained individually below.

[0041] If the discussion phase is in the convergence phase, the decision unit 15 uses the degree of opinion convergence A1 calculated by the progress calculation unit 13, the degree of teamwork A2 estimated by the situation estimation unit 14, and the degree of topic relevance A3 to calculate the progress of convergence B1 (B1=A1), the degree of teamwork B2 (B2=A2), and the degree of topic relevance B3 (B3=A3) as evaluation values ​​for evaluating the discussion situation. The thresholds t1 to t3 for evaluating the above evaluation values ​​B1 to B3 are set in advance using training data or the like. The decision unit 15 evaluates whether the discussion situation is appropriate from the perspective of each evaluation value B1 to B3, depending on whether each evaluation value B1 to B3 exceeds the corresponding threshold (step S6). At this time, evaluation values ​​that do not exceed the corresponding threshold are selected as "missing features".

[0042] Furthermore, the decision unit 15 determines the content of the facilitation according to the situation for one of the selected evaluation values, for example, as follows (step S7): If the missing feature is "Convergence Progress B1", it presents a new perspective for selection and asks questions to solicit opinions from that perspective on the multiple options that have gathered votes. If the missing feature is "Teamwork B2", it asks questions to check if there are any points that can be noticed based on the opinions of others. If the missing feature is "Relevance to the Agenda B3", it presents comments to summarize the opinions and ideas that have been expressed so far.

[0043] Alternatively, the decision unit 15 may obtain the facilitation content from the LLM by querying it using, for example, the prompt shown in Figure 7 (an example of a prompt used when the convergence progress B1 is not good). If there are multiple evaluation values ​​selected as "missing features," one evaluation value should be selected according to a predetermined priority order (for example, the priority order of evaluation values ​​B3 → B2 → B1).

[0044] On the other hand, when the discussion phase is a divergence phase or a clarification phase, the determining unit 15, based on scores indicating the degree related to the estimated discussion status (idea abstraction level X1, number of ideas X2, number of categories X3, teamwork level X4, and agenda relevance X5), obtains, as evaluation values, (a) idea concreteness Y1 evaluating the concreteness of ideas, (b) discussion comprehensiveness Y2 evaluating the comprehensiveness of the discussion, (c) all participants' satisfaction Y3 evaluating the satisfaction of participants, and (d) agenda relevance Y4 evaluating agenda relevance, and evaluates whether the discussion is in an appropriate situation from the viewpoint related to each evaluation value depending on whether or not the four obtained evaluation values Y1 to Y4 exceed a predetermined threshold for each evaluation value (step S6). At this time, an evaluation value that does not exceed the corresponding threshold is selected as a "missing feature".

[0045] Furthermore, the determining unit 15 determines the content of facilitation according to one evaluation value selected as a missing feature (step S7). If there are a plurality of selected evaluation values, the determining unit 15 selects one evaluation value based on a predetermined priority order, and determines the content of facilitation according to the selected one evaluation value. Hereinafter, an example of processing of steps S6 and S7 when the discussion phase is a divergence phase or a clarification phase will be described.

[0046] As an example, the determining unit 15 calculates (a) the evaluation value "idea concreteness Y1" obtained by evaluating the concreteness of an idea according to the following formula (3). Y1=1-X1 (3) As shown in FIG. 8, when X1=0.7, X2=3, X3=12, X4=0.8, and X5=0.7, idea concreteness Y1=1-0.7=0.3 is calculated.

[0047] Further, the determining unit 15 calculates (b) the evaluation value "discussion comprehensiveness Y2" obtained by evaluating the comprehensiveness of a discussion according to the following formula (4). Y2=X2+X3 (4) From the numerical example (X1 to X5) in FIG. 8 described above, discussion comprehensiveness Y2=3+12=15 is calculated. Note that the above calculation method (addition of X2 and X3) is an example, and another calculation method such as multiplication of X2 and X3 may be employed.

[0048] Furthermore, the determining unit 15 (c) adopts the teamwork degree X4 obtained in step S5 as the evaluation value "consensus degree of all members Y3" obtained by evaluating the consensus degree of participants. That is, Y3=X4=0.8.

[0049] Similarly, the determining unit 15 (d) adopts the agenda relevance degree X5 obtained in step S5 as the evaluation value "agenda relevance degree Y4" obtained by evaluating agenda relevance. That is, Y4=X5=0.7.

[0050] Then, the determining unit 15 determines whether each of the four evaluation values Y1 to Y4 exceeds the corresponding threshold values t1 to t4, and selects an evaluation value that does not exceed the corresponding threshold as a "missing feature". Here, as shown in FIG. 8, the threshold t1=0.5 for the evaluation value Y1, the threshold t2=20 for the evaluation value Y2, the threshold t3=0.7 for the evaluation value Y3, and the threshold t4=0.5 for the evaluation value Y4 are predetermined in advance through prior machine learning or the like.

[0051] Furthermore, the determining unit 15 determines the content of facilitation according to the selected evaluation values, that is, the "missing features", for example, as follows. Note that the content of facilitation may be generated based on a reply from an LLM by inquiring of the LLM. ・If the missing feature is the idea concreteness Y1, pose a question to encourage in-depth exploration of ideas that have been proposed in previous discussions. ・If the missing feature is the discussion comprehensiveness Y2, present a new perspective and pose a question to encourage the proposal of new ideas. ・If the missing feature is the consensus degree of all members Y3, pose a question to confirm whether any participant has new insights based on other people's opinions. ・If the missing feature is the agenda relevance degree Y4, present a comment to reorganize the opinions and ideas that have been proposed so far.

[0052] In the numerical example in Figure 8, evaluation values ​​Y1 and Y2 are selected as "missing features" because they did not exceed the corresponding thresholds t1 and t2, respectively, out of the four evaluation values. If multiple evaluation values ​​are selected in this way, evaluation value Y1 is selected because it has a higher priority than evaluation value Y2, according to a predetermined priority order (for example, evaluation value Y4 → Y1 → Y3 → Y2). In this case, the decision unit 15 may decide on the content of the facilitation to include questions that encourage further exploration of the ideas currently being discussed, such as, "Regarding the ideas currently being discussed, what are your thoughts on point XX?"

[0053] The "priority order of evaluation values ​​Y4 → Y1 → Y3 → Y2" in the above example follows the following principles: ・For topic relevance Y4, the first priority was to produce an output related to the topic. The idea was that it is important to produce as much output as possible, regardless of its nature. ・For idea specificity Y1, the second priority was that the output be specific. The idea was that even if the output focuses on only one perspective or is biased towards someone's opinion, the more specific the output, the easier it is to move the discussion forward. ・For everyone's satisfaction Y3, the third priority was that all participants be satisfied. Even if the output focuses on only one perspective or the idea is excellent, since discussion is a collaborative effort involving multiple participants, it is desirable that the output be as satisfactory as possible for all participants, without being biased towards anyone's opinion. ・For comprehensiveness of discussion Y2, the fourth priority was that the discussion be comprehensive. The idea was that the more diverse the perspectives considered, the better the idea.

[0054] Returning to the flowchart in Figure 2, in the final step S8, the output unit 16 outputs the facilitation content determined by the determination unit 15. This allows the user of the information processing device 10 to recognize the determined facilitation content.

[0055] According to the embodiments described above, by focusing on the fact that different perspectives should be considered regarding the state of the discussion depending on the phase of the discussion, even if the phase of the discussion changes during the meeting, it is possible to grasp the state of the discussion from an appropriate perspective corresponding to the changing phase, to appropriately perform facilitation according to the situation, and to efficiently conduct a good meeting.

[0056] It should be noted that the information processing device 10 is not limited to the configuration shown in Figure 1, and other configurations can also be adopted. For example, the information processing device 10 shown in Figure 9 may have a configuration that does not include the acquisition unit 11 shown in Figure 1. In this case, the information processing device 10 can achieve the same functions as the embodiment described above and obtain the same effects by acquiring speech content data for each participant from an external server 20 equipped with the functional parts of the acquisition unit 11 (speech acquisition unit 20A, speech recognition unit 20B) shown in Figure 1.

[0057] Furthermore, in the above embodiment, when selecting an evaluation value that requires improvement in step S7 (determination of facilitation content) in Figure 6, an example is shown in which, if there are multiple evaluation values ​​that did not exceed the threshold, one evaluation value is selected according to a predetermined priority order. However, the method of selection is not limited to this, and other processing forms may be adopted as follows. That is, if there are multiple evaluation values ​​that did not exceed the threshold, one evaluation value may be selected based on the degree of deviation from the threshold (i.e., the extent to which it fell below the threshold). In the numerical example in Figure 8, as follows: If Y1 = 0.3, Y1 / t1 = 0.3 / 0.5 = 0.6 If Y2 = 15, Y2 / t2 = 15 / 20 = 0.75 The ratio of the evaluation value to the threshold may be calculated, and the one with the smallest ratio (in the above example, "evaluation value Y1") may be selected. In this case, the higher the degree of deviation from the threshold, the more likely an evaluation value is to be selected as an "evaluation value that requires improvement," thus having the effect of selecting an appropriate evaluation value according to the degree of deviation from the threshold (the extent to which it fell below the threshold).

[0058] The gist of this disclosure is found in the following [1] to

[10] . [1] An information processing device comprising: a phase estimation unit that estimates the phase of a discussion based on statement data showing the content of statements made by each participant in the discussion; a progress calculation unit that calculates an index value for at least one index to be determined as the progress of the discussion according to the estimated phase; a situation estimation unit that estimates the state of the discussion based on the calculated index value; and a decision unit that determines the content of facilitation based on an evaluation of the estimated state of the discussion. [2] The information processing device according to [1], wherein the phase estimation unit estimates whether the phase of the discussion is a divergent phase focused on generating more ideas, a convergence phase focused on converging opinions, or an elucidation phase focused on analyzing the factors of the problem. [3] The information processing device according to [2], wherein the progress calculation unit calculates the degree of convergence of opinions in the discussion as the progress of the discussion when the estimated phase is the convergence phase. [4] The progress calculation unit clusters the vectors obtained by vectorizing the content of the statements into multiple clusters, and calculates the degree of convergence of opinions in the discussion based on the decrease in the number of clusters or the bias in the number of clusters, as described in [3]. [5] The situation estimation unit estimates at least one of the following as an evaluation value regarding the situation of the discussion based on the calculated index value: the degree of teamwork based on the distribution of participant statements during the discussion, the degree of statement use indicating the degree to which important statements are made among the statements of all participants, or the degree of topic relevance indicating the degree to which statements relevant to the topic are made within a certain period of time, as described in any one of [2] to [4]. [6] The situation estimation unit calculates the degree of teamwork using the coefficient of variation, which is a statistic, as described in [5]. [7] The information processing apparatus according to [5] or [6], wherein the situation estimation unit vectorizes the agenda and each statement, calculates the similarity between the vectorized agenda and each vectorized statement, and calculates the minimum value of the calculated multiple similarities as the agenda relevance.[8] The information processing device according to any one of [5] to [7], wherein, when the phase is the convergence phase, the decision unit calculates evaluation values ​​for the degree of convergence, the degree of teamwork, and the degree of relevance to the agenda based on the degree of convergence of opinions, the degree of teamwork, and the degree of relevance to the agenda, and determines the content of the facilitation based on the calculated evaluation values. [9] The information processing device according to [8], wherein, when there are multiple calculated evaluation values, the decision unit selects one evaluation value based on a predetermined priority order, and determines the content of the facilitation based on the selected evaluation value.

[10] An information processing method comprising: an information processing device estimating the phase of a discussion based on statement data showing the content of statements made by each participant in the discussion; an information processing device calculating an index value for at least one indicator to be determined as the progress of the discussion according to the estimated phase; an information processing device estimating the status of the discussion based on the calculated index value; and an information processing device determining the content of facilitation based on an evaluation of the estimated status of the discussion.

[0059] [Explanation of terms, explanation of hardware configuration (Figure 10), etc.] The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired, wireless, etc.). A functional block may be realized by combining the above one device or the above multiple devices with software.

[0060] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0061] For example, the information processing device in one embodiment of the present disclosure may function as a computer that performs processing of the information processing method of the present disclosure. Figure 10 is a diagram showing an example of the hardware configuration of the information processing device 10 according to one embodiment of the present disclosure. The above-described information processing device 10 may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0062] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the information processing device 10 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0063] Each function in the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0064] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control units, arithmetic units, registers, etc.

[0065] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the acquisition unit 11 of the information processing device 10 may be implemented by a control program stored in the memory 1002 and operated on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0066] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0067] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of the memory 1002 and the storage 1003.

[0068] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD).

[0069] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0070] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0071] Furthermore, the information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0072] The notification of information is not limited to the embodiments described herein and may be carried out by other means. For example, the notification of information may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0073] Each aspect / embodiment described in this disclosure refers to LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (where x is, for example, an integer or decimal)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20 may apply to at least one system utilizing UWB (Ultra-WideBand), Bluetooth®, or other appropriate systems, and to next-generation systems extended, modified, created, or defined based thereon. Alternatively, multiple systems may be applied in combination (e.g., a combination of at least one of LTE and LTE-A with 5G).

[0074] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0075] The specific operations described in this disclosure as being performed by a base station may, in some cases, be performed by its upper node. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal can be performed by the base station and at least one other network node (for example, an MME or S-GW, but not limited to these). Although the above example illustrates the case where there is one other network node besides the base station, it may also be a combination of multiple other network nodes (for example, an MME and an S-GW).

[0076] Information can be output from a higher layer (or lower layer) to a lower layer (or higher layer). Input and output may also occur via multiple network nodes.

[0077] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be sent to other devices.

[0078] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0079] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0080] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure may be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Accordingly, the descriptions in the present disclosure are for illustrative purposes only and are not intended to be restrictive in any way.

[0081] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0082] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0083] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0084] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and symbol may be a signal (signaling). Also, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc.

[0085] The terms “system” and “network” as used in this disclosure are interchangeable.

[0086] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or corresponding other information. For example, wireless resources may be indicated by an index.

[0087] The names used for the parameters described above are not restrictive in any way. Furthermore, mathematical formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0088] In this disclosure, terms such as “Base Station (BS),” “wireless base station,” “fixed station,” “NodeB,” “eNodeB (eNB),” “gNodeB (gNB),” “access point,” “transmission point,” “reception point,” “transmission / reception point,” “cell,” “sector,” “cell group,” “carrier,” and “component carrier” may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

[0089] A base station can accommodate one or more (e.g., three) cells. If a base station accommodates multiple cells, the entire coverage area of ​​the base station can be divided into multiple smaller areas, each of which may also be provided with communication services by a base station subsystem (e.g., a Remote Radio Head (RRH)). The terms “cell” or “sector” refer to part or all of the coverage area of ​​at least one of the base station and / or base station subsystems that provide communication services in that coverage.

[0090] In this disclosure, the transmission of information by a base station to a terminal may be interpreted as the base station instructing the terminal to perform control or operation based on the information.

[0091] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0092] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or several other appropriate terms.

[0093] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a communication device, etc. At least one of the base station and the mobile station may be a device mounted on a mobile body, the mobile body itself, etc.

[0094] The term "moving object" refers to any object that can move, regardless of its speed. This also includes cases where the moving object is stationary. The term "moving object" includes, but is not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones (registered trademarks), multicopters, quadcopters, balloons, and anything carried on them.

[0095] Furthermore, the mobile entity may be one that autonomously drives based on operational commands. It may be a vehicle (e.g., a car, an airplane), an unmanned mobile entity (e.g., a drone, an autonomous vehicle), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may be a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an IoT (Internet of Things) device such as a sensor.

[0096] Furthermore, the term "base station" in this disclosure may be interpreted as "user terminal." For example, the various aspects / embodiments of this disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, D2D (Device-to-Device), V2X (Vehicle-to-Everything), etc.). Also, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to terminal-to-terminal communication (for example, "side"). For example, uplink channel, downlink channel, etc., may be interpreted as side channel.

[0097] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0098] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0099] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0100] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0101] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0102] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0103] In this disclosure, if articles are added through translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0104] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0105] 10... Information processing device, 11... Acquisition unit, 11A, 20A... Voice acquisition unit, 11B, 20B... Voice recognition unit, 12... Phase estimation unit, 13... Progress calculation unit, 14... Situation estimation unit, 15... Decision unit, 16... Output unit, 20... External server, 1001... Processor, 1002... Memory, 1003... Storage, 1004... Communication device, 1005... Input device, 1006... Output device, 1007... Bus.

Claims

1. An information processing device comprising: a phase estimation unit that estimates the phase of a discussion based on content data showing the content of each participant's statements in the discussion; a progress calculation unit that calculates an index value for at least one index to be determined as the progress of the discussion according to the estimated phase; a situation estimation unit that estimates the state of the discussion based on the calculated index value; and a decision unit that determines the content of facilitation based on an evaluation of the estimated state of the discussion.

2. The information processing apparatus according to claim 1, wherein the phase estimation unit estimates whether the phase of the discussion is a divergent phase focused on generating more ideas, a convergent phase focused on converging opinions, or an elucidation phase focused on analyzing the factors of the problem.

3. The information processing apparatus according to claim 2, wherein the progress calculation unit calculates the degree of convergence of opinions in the discussion as the progress of the discussion when the estimated phase is the convergence phase.

4. The progress calculation unit clusters the vectors obtained by vectorizing the content of the statements into multiple clusters, and calculates the degree of convergence of opinions in the discussion based on the decrease in the number of clusters or the bias in the number of clusters, as described in claim 3.

5. The information processing device according to claim 2, wherein the situation estimation unit estimates, based on the calculated index value, at least one of the following as an evaluation value regarding the situation of the discussion: a degree of teamwork based on the distribution of participant statements during the discussion, a degree of statement indicating the degree to which important statements are made among the statements made by all participants, or a degree of topic relevance indicating the degree to which statements related to the topic are made within a certain period of time.

6. The information processing apparatus according to claim 5, wherein the situation estimation unit calculates the degree of teamwork using the coefficient of variation, which is a statistical quantity.

7. The information processing apparatus according to claim 5, wherein the situation estimation unit vectorizes the agenda and each statement, calculates the similarity between the vectorized agenda and each vectorized statement, and calculates the minimum value among the calculated multiple similarities as the agenda relevance.

8. The information processing apparatus according to claim 5, wherein, when the phase is the convergence phase, the decision unit calculates evaluation values ​​for the degree of convergence, the degree of teamwork, and the degree of relevance to the agenda based on the degree of convergence of opinions, the degree of teamwork, and the degree of relevance to the agenda, and determines the content of the facilitation based on the calculated evaluation values.

9. If there are multiple calculated evaluation values, the determination unit selects one evaluation value based on a predetermined priority order, and determines the content of the facilitation based on the selected evaluation value, the information processing apparatus according to claim 8.

10. An information processing method comprising: a step of an information processing device estimating the phase of a discussion based on statement data showing the content of statements made by each participant in the discussion; a step of the information processing device calculating an index value for at least one indicator to be determined as the progress of the discussion according to the estimated phase; a step of the information processing device estimating the status of the discussion based on the calculated index value; and a step of the information processing device determining the content of facilitation based on an evaluation of the estimated status of the discussion.

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