Information processing device, method, program, and system

Abstract

To support the improvement of skills required as a mentor to a user participating in a conference as the mentor.SOLUTION: According to one aspect of the present disclosure, a program makes a computer function as means for: acquiring conversation information in a conference between a first user of a role of a mentor and a second user of a role of a mentee; calculating a feature quantity on the basis of the conversation information in a unit time in each unit time in the conference; generating feedback information for encouraging the first user to change a conversational behavior on the basis of the feature quantity and standard information on predetermined standards; and presenting the feedback information to the first user.SELECTED DRAWING: Figure 4

Description

Technical Field

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

Background Art

[0002] In various fields, in order to improve the skills of a target person, attempts have been made to evaluate the actions of the target person and promote behavioral changes.

[0003] Patent Document 1 discloses a technical idea intended to grasp information according to the situation of a meeting in real time.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Meetings are held for various purposes, and the roles expected of participants are also diverse. In the technical idea of Patent Document 1, although an evaluation result is output, there is a possibility that the evaluation result may not be appropriate feedback depending on the purpose of the meeting and the role expected of the participants.

[0006] An object of the present disclosure is to support improvement of skills required of a user who participated in a meeting as a mentor.

Means for Solving the Problems

[0007] A program according to an aspect of the present disclosure causes a computer to function as means for acquiring conversation information in a meeting between a first user in the role of a mentor and a second user in the role of a mentee, means for calculating a feature amount based on the conversation information for each unit time in the meeting, means for generating feedback information for prompting a change in conversation behavior to the first user based on the feature amount and specification information regarding a predetermined norm, and means for presenting the feedback information to the first user.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Modes for Carrying Out the Invention

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In the drawings for explaining the embodiment, the same components are generally denoted by the same reference numerals, and the repeated description thereof will be omitted.

[0010] In the following description, when giving a common description of a plurality of elements of the same kind, a common reference numeral such as "99" may be used. On the other hand, when giving an individual description of these elements, a reference numeral with a suffix added to the common reference numeral such as "99-1" or "99-2" may be used.

[0011] (1) Configuration of the information processing system The configuration of the information processing system will be described. FIG. 1 is a block diagram showing the configuration of the information processing system of the present embodiment.

[0012] As shown in FIG. 1, the information processing system 1 includes a client device 10 and a server 30. The client device 10 and the server 30 are connected via a network (for example, the Internet or an intranet) NW.

[0013] The client device 10 is an example of an information processing device. The client device 10 is, for example, a smartphone, a tablet terminal, or a personal computer. The user of the client device 10 is, for example, a conference participant, but is not limited thereto. For example, when a conference is held via a video conferencing tool, each participant may use an individual client device 10, or all or some of the participants may use a common client device 10. Alternatively, when the conference is held with the participants face to face, the client device 10 may be used to upload the data obtained by recording the conversation in the conference to the server 30.

[0014] The server 30 is an example of an information processing device. The server 30 is, for example, a server computer.

[0015] (1-1) Configuration of the client device The configuration of the client device will be described. FIG. 2 is a block diagram showing the configuration of the client device of the present embodiment.

[0016] As shown in FIG. 2, the client device 10 includes a storage device 11, a processor 12, an input / output interface 13, and a communication interface 14. The client device 10 is connected to a display 21.

[0017] The storage device 11 is configured to store programs and data. The storage device 11 is, for example, a combination of a ROM (Read Only Memory), a RAM (Random Access Memory), and a storage (e.g., flash memory or hard disk).

[0018] The program includes, for example, the following programs. · Program of an OS (Operating System) · Program of an application that executes information processing (e.g., web browser)

[0019] The data includes, for example, the following data. · Database referred to in information processing · Data obtained by executing information processing (i.e., execution result of information processing)

[0020] The processor 12 is a computer that realizes the functions of the client device 10 by starting the programs stored in the storage device 11. The processor 12 is, for example, at least one of the following. · CPU (Central Processing Unit) · GPU (Graphic Processing Unit) · ASIC (Application Specific Integrated Circuit) · FPGA (Field Programmable Gate Array)

[0021] The input / output interface 13 is configured to acquire information (for example, a user's instruction) from an input device connected to the client device 10 and output information (for example, an image) to an output device connected to the client device 10.

[0022] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display 21, a speaker, or a combination thereof.

[0023] The communication interface 14 is configured to control communication between the client device 10 and an external device (for example, the server 30).

[0024] The display 21 is configured to display an image (a still image or a moving image). The display 21 is, for example, a liquid crystal display or an organic EL display.

[0025] (1-2) Configuration of the server The configuration of the server will be described. FIG. 3 is a block diagram showing the configuration of the server of the present embodiment.

[0026] As shown in FIG. 3, the server 30 includes a storage device 31, a processor 32, an input / output interface 33, and a communication interface 34.

[0027] The storage device 31 is configured to store programs and data. The storage device 31 is, for example, a combination of a ROM, a RAM, and a storage (for example, a flash memory or a hard disk).

[0028] The program includes, for example, the following programs. · An OS program · A program of an application that executes information processing

[0029] The data includes, for example, the following data. ·Database referenced in information processing ·Execution result of information processing

[0030] The processor 32 is a computer that realizes the functions of the server 30 by starting the program stored in the storage device 31. The processor 32 is, for example, at least one of the following. ·CPU ·GPU ·ASIC ·FPGA

[0031] The input / output interface 33 is configured to acquire information (for example, a user's instruction) from an input device connected to the server 30 and output information (for example, an image) to an output device connected to the server 30.

[0032] The input device is, for example, a keyboard, a pointing device, a touch panel, or a combination thereof. The output device is, for example, a display.

[0033] The communication interface 34 is configured to control communication between the server 30 and an external device (for example, the client device 10).

[0034] (2) One aspect of the embodiment One aspect of this embodiment will be described. FIG. 4 is an explanatory diagram of one aspect of this embodiment.

[0035] As shown in FIG. 4, the user US1 in the role of mentor and the user US2 in the role of mentee hold a meeting. Note that the number of users in the role of mentee is not limited to one and may be plural. That is, this embodiment is also applicable to a one-to-many meeting between a mentor and mentees.

[0036] Server 30 acquires conversation information in a meeting between user US1 and user US2. Details of the conversation information will be described later. Note that server 30 may acquire conversation information from the client device 10-1 of user US1, the client device 10-2 of user US2, or both, or may acquire conversation information from an external device such as a video conferencing server (not shown). That is, the information source of the conversation information is arbitrary.

[0037] Server 30 calculates a feature amount for each unit time in the meeting based on the conversation information for that unit time. The feature amount is an index of the characteristics of the conversation behavior of the mentor, the mentee, or both. The feature amount can be used as it is as an improvement index for conversation behavior, or can also be used for the type determination of conversation behavior described later.

[0038] Server 30 determines whether the conversation information for a unit time corresponds to a desired type of conversation behavior based on the calculated feature amount. For example, a coaching type of conversation behavior can be defined as the desired type of conversation behavior.

[0039] Server 30 generates feedback information for prompting the transformation (improvement) of the conversation behavior for user US1 based on the calculated feature amount and the norm information regarding a predetermined norm, or based on the determination result as to whether the conversation information for each unit time corresponds to a desired type of conversation behavior. For example, server 30 collates the feature amount with the norm information to identify the conversation behavior that user US1 should improve, and generates feedback information so as to include information regarding the direction of improvement of the conversation behavior.

[0040] The norm is defined based on points to be aware of in order to approach an ideal conversation behavior and theoretical knowledge for improving the way of speaking, etc. The norm can be expressed, for example, as a preferable range of the feature amount, or a preferable range of the occurrence situation of a desired type of conversation behavior.

[0041] Server 30 presents the generated feedback information to user US1 by outputting it to the client device 10-1, for example, after the meeting ends.

[0042] As a result, the user US1 is prompted to review his or her own conversation behavior with reference to the feedback information and improve the conversation behavior. That is, it is possible to support the improvement of the skills required for the user US1 who participated in the meeting as a mentor.

[0043] (3) Database The database of this embodiment will be described. The following database is stored in the storage device 31.

[0044] (3-1) Norm database The norm database of this embodiment will be described. FIG. 5 is a diagram showing the data structure of the norm database of this embodiment.

[0045] Norm information is stored in the norm database. The norm information is information regarding norms for determining the content of the feedback information to be presented to the user who serves as a mentor.

[0046] The norm database can basically be designed in advance. The norm database does not have to be updated, but new norm information may be added, or existing norm information may be changed or deleted as appropriate.

[0047] As shown in FIG. 5, the norm database includes an "ID" field, a "maturity" field, and a "norm details" field. Each field is associated with each other.

[0048] A norm ID is stored in the "ID" field. The norm ID uniquely identifies the norm.

[0049] Target maturity information is stored in the "maturity" field. The target maturity information is information regarding the maturity of the user to whom the norm (hereinafter referred to as the "target norm") identified by the corresponding norm ID is applied.

[0050] The maturity of a user may be defined, for example, in three stages. The first stage aims to make a user in the role of a mentor aware of the current state of their conversation behavior, and feedback is provided focusing on the pros and cons of current meetings and the elements of the user's average or characteristic conversation behavior. In the second stage, the aim is to have a user in the role of a mentor practice conversation behaviors generally regarded as good, and feedback is provided focusing on the elements of conversation behavior that can be improved solely through the user's spontaneous changes. In the third stage, the aim is to have a user in the role of a mentor practice conversation behaviors adapted to the other party (mentee), and feedback is provided focusing on the elements of conversation behavior that require changes in line with the other party's conversation.

[0051] Norm details information is stored in the "Norm Details" field. The norm details information is information regarding the details of the target norm and can be expressed, for example, by parameters related to the target norm and their numerical ranges.

[0052] (3-2) User Database The user database of this embodiment will be described. FIG. 6 is a diagram showing the data structure of the user database of this embodiment.

[0053] User information is stored in the user database. The user information is information regarding a user in the role of a mentor. The user database is appropriately updated by the server 30. That is, the server 30 appropriately adds new user information, or changes or deletes existing user information.

[0054] As shown in FIG. 6, the user database includes an "ID" field, a "User Name" field, a "Maturity" field, and a "Feature History" field. Each field is associated with each other.

[0055] A user ID is stored in the "ID" field. The user ID uniquely identifies the user.

[0056] In the "Username" field, username information is stored. The username information is information regarding the name of a user identified by the corresponding user ID (hereinafter referred to as the "target user").

[0057] In the "Maturity" field, maturity information is stored. The maturity information is information regarding the maturity of the target user as a mentor.

[0058] In the "Feature History" field, feature history information is stored. The feature history information is information regarding the history of features calculated in meetings in which the target user participated in the role of a mentor in the past.

[0059] The server 30 can determine the maturity of the target user based on the feature history information. Alternatively, a human evaluator may determine the maturity of the target user, and the server 30 may set the maturity information according to the determination result. In this case, the "Feature History" field may become unnecessary.

[0060] (4) Information Processing The information processing of this embodiment will be described.

[0061] (4-1) Conversation Behavior Diagnosis Processing The conversation behavior diagnosis processing of this embodiment will be described. FIG. 7 is a flowchart of the conversation behavior diagnosis processing of this embodiment. FIG. 8 is a diagram showing an example of a screen displayed in the conversation behavior diagnosis processing of this embodiment. FIG. 9 is a diagram showing an example of a screen displayed in the conversation behavior diagnosis processing of this embodiment.

[0062] As shown in FIG. 7, the server 30 executes acquisition of conversation information (S130). Specifically, the server 30 acquires conversation information in a meeting between a first user in the role of a mentor and a second user in the role of a mentee. The server 30 may acquire real-time conversation information from the client device 10 or an external device (e.g., a video conferencing server) during the meeting, or may acquire conversation information obtained by recording during the meeting from the client device 10 or an external device (e.g., a video conferencing server) after the meeting ends. When acquiring real-time conversation information, the server 30 may repeatedly execute the processes of steps S130 to S132.

[0063] The conversation information acquired in step S130 can include at least the following information. · Speaker identification information · Speech time information

[0064] The speaker identification information identifies who (at least, which role participant) made each speech that occurred in the meeting.

[0065] The speech time information is information regarding the occurrence time of each speech in the meeting. Also, the duration of the corresponding speech can be specified from the speech time information.

[0066] Furthermore, the conversation information obtained in step S130 can include voice feature information. This voice feature information relates to the voice features of each speech that occurred in the meeting, but is preferably limited to a range in which the content of the speech cannot be restored to a recognizable level (e.g., only the frequency information of the voice). That is, some elements may be removed from the voice feature information so that the content of the speech cannot be substantially restored. Thereby, regardless of the confidentiality of the topics handled in the meeting, the present embodiment is easier to apply. However, as an option, the voice signal corresponding to the speech may be acquired as it is, the speech content may be restored by performing speech recognition, and the restored text may be used as an object for feature extraction. Thereby, improvement of conversation behavior can also be promoted from the viewpoint of language expressions such as the way of speaking of the user in the role of a mentor.

[0067] After step S130, the server 30 executes calculation of feature quantities (S131). Specifically, the server 30 refers to the conversation information obtained in step S130 and calculates feature quantities. The feature quantities can include indicators related to at least one of the following. · Feature quantities related to the way of speaking by the user in the role of mentor (for example, the ratio of speaking time, the speaking time per utterance, or the speaking speed, etc.) · Feature quantities related to non-verbal communication by the user in the role of mentor (for example, the number of nods) · Feature quantities related to the silence of the user in the role of mentor (for example, the ratio of silence time, the silence time per silence, etc.) · Feature quantities related to the way of speaking, non-verbal communication, or silence of the user in the role of mentee · Composite indicators obtained by performing calculations using a plurality of the above indicators Note that the server 30 may select the feature quantities to be calculated according to the maturity of the user in the role of mentor as a mentor. For example, the server 30 may calculate only the feature quantities related to the norms corresponding to the maturity level below the maturity level of the user.

[0068] After step S131, the server 30 executes determination of the type of conversation behavior (S132). Specifically, the server 30 determines whether the conversation information per unit time corresponds to the desired type of conversation behavior based on the feature quantities calculated in step S131. As a first example of determination of the type of conversation behavior (S132), the server 30 determines whether the conversation information per unit time is the desired type of conversation behavior. As a second example of determination of the type of conversation behavior (S132), the server 30 determines which of the plurality of types of conversation behaviors including the desired type the conversation information per unit time corresponds to. For example, the server 30 may perform the determination by applying a predetermined rule or a learned model to the feature quantities. The types of conversation behavior can include coaching, teaching, chatting, discussion, etc. The desired type of conversation behavior is, for example, coaching. In addition, when the result of the type determination of the conversation behavior is not involved in the norm corresponding to the maturity level below the user's maturity level, the server 30 may omit the type determination of the conversation behavior (S132).

[0069] After step S132, the server 30 executes generation of feedback information (S133). Specifically, the server 30 generates feedback information that prompts the user in the role of mentor to change the conversation behavior based on the feature amount calculated in step S131, the result of the determination in step S132, or a combination thereof.

[0070] In the first example of generation of feedback information (S133), the server 30 generates feedback information regarding the occurrence status of the desired type of conversation behavior in the meeting based on the result of the determination in step S132. As an example, the server 30 may generate the feedback information so as to include information representing the time change of the desired type of conversation behavior in the meeting. As another example, the server 30 may generate the feedback information so as to include information that enables comparison of the occurrence status of the desired type of conversation behavior and the occurrence status of other types of conversation behavior during a predetermined period (for example, the whole) of the meeting.

[0071] In the second example of generation of feedback information (S133), after the meeting ends, the server 30 conducts a questionnaire regarding the achievement status of the desired type of conversation behavior to the user in the role of mentor via the client device 10. On the other hand, the server 30 specifies the actual occurrence status of the desired type of conversation behavior in the meeting based on the result of the determination in step S132. The server 30 generates the feedback information so as to include information that enables comparison of the achievement status answered by the user in the role of mentor in the questionnaire and the occurrence status of the desired type of conversation behavior.

[0072] In the third example of generating feedback information (S133), the server 30 identifies a conversation behavior that requests improvement from the user in the role of a mentor based on the feature amount calculated in step S131 and the norm information (norm database (FIG. 5)), and generates feedback information so as to include information indicating the conversation behavior. Here, the server 30 may identify the maturity of the user as a mentor, for example, by referring to a user database (FIG. 6). Then, the server 30 may identify a conversation behavior that requests improvement from the user based on the feature amount and the norm information associated with a maturity level equal to or lower than the identified maturity level.

[0073] The fourth example of generating feedback information (S133) is a combination of a plurality of the above first to third examples.

[0074] After step S133, the server 30 executes output of feedback information (S134). Specifically, the server 30 transmits the feedback information generated in step S133 to the client device 10. The client device 10 receives the feedback information and presents the feedback information to the user in the role of a mentor. For example, the client device 10 outputs an image or voice based on the feedback information from the display 21 or the speaker. The feedback information may include data of the image or voice itself, or may include data for generating the image or voice. For example, the client device 10 may display the screen of FIG. 8 or FIG. 9 on the display 21.

[0075] The screen of FIG. 8 includes objects J21 to J22. The object J21 displays a graph representing the ratio of the occurrence status of a desired type of conversation behavior and the occurrence status of other types of conversation behavior during a predetermined period (for example, the whole) of the meeting.

[0076] Object J22 displays a message that prompts the user in the role of mentor to change their behavior. This message can include, for example, information that conveys the difference between the occurrence status of the desired type of conversation behavior in a meeting and the achievement status that the user answered in the questionnaire, and information that conveys the achievement goal of the desired type of conversation behavior in the next meeting.

[0077] The screen of FIG. 9 includes objects J23 to J24. Object J23 displays a graph that represents the temporal change of the desired type of conversation behavior in a meeting.

[0078] Object J24 displays a message that prompts the user in the role of mentor to change their behavior. This message can include, for example, information that conveys the conversation behavior that requests improvement from the user and the direction of improvement of the conversation behavior.

[0079] (5) Parentheses As described above, the server 30 of the present embodiment acquires conversation information in a meeting between a first user in the role of mentor and a second user in the role of mentee, and calculates a feature amount based on the conversation information in each unit time in the meeting for each unit time. The server 30 generates feedback information that prompts the first user to change their conversation behavior based on the feature amount and the norm information regarding a predetermined norm, and presents the feedback information to the first user. Thereby, the first user reviews their own conversation behavior with reference to the feedback information and is prompted to improve their conversation behavior. That is, it is possible to support the improvement of the skills required as a mentor for the first user who participated in the meeting as a mentor.

[0080] The conversation information may include the identification information of the speaker and the speech time information. Thereby, an appropriate feature amount can be calculated.

[0081] The feature amount may include an index related to the duration of the speech. Thereby, feedback information regarding the duration of the speech can be generated.

[0082] The conversation information may further include information regarding the voice characteristics of the utterance, and the information may be limited to a range where the content of the utterance cannot be restored at a recognizable level. This makes it easier to use the meeting data collected in a meeting dealing with highly confidential topics.

[0083] The feature amount may include an index regarding the speaking speed. Thereby, feedback information regarding the speaking speed can be generated.

[0084] The server 30 may determine whether the conversation information per unit time corresponds to a desired type of conversation behavior based on the feature amount. The server 30 may generate feedback information so as to include information representing the time change regarding the occurrence status of the desired type of conversation behavior in the meeting. This makes it easier for the first user to recognize how well the conversation behavior taken by the user himself / herself in the meeting is evaluated.

[0085] The server 30 may determine whether the conversation information per unit time corresponds to a desired type of conversation behavior based on the feature amount. The server 30 may generate feedback information so as to include information that can compare the occurrence status of the desired type of conversation behavior and the occurrence status of other types of conversation behavior during a predetermined period of the meeting. This makes it easier for the first user to recognize how well the conversation behavior taken by the user himself / herself during a predetermined period of the meeting is comprehensively evaluated.

[0086] The server 30 may determine whether the conversation information per unit time corresponds to a desired type of conversation behavior based on the feature amount, and may conduct a questionnaire regarding the achievement status of the desired type of conversation behavior with the first user after the end of the meeting. The server 30 may generate feedback information so as to include information that can compare the response of the first user to the questionnaire and the occurrence status of the desired type of conversation behavior. This enables the first user to recognize the gap between the subjective evaluation and the objective evaluation of how well the user himself / herself has taken suitable conversation behavior, and is more likely to promote behavior modification.

[0087] The server 30 may identify a conversation behavior that requests improvement from the first user based on the feature amount and the norm information, and generate feedback information including information indicating the conversation behavior. Thereby, it becomes easier for the first user to be aware of points to be noted in future conversation behaviors.

[0088] The server 30 may identify the maturity of the first user as a mentor. Each piece of norm information may be associated with the maturity as a mentor. The server 30 may identify a conversation behavior that requests improvement from the first user based on the feature amount and the norm information associated with a maturity equal to or lower than the maturity corresponding to the maturity of the first user as a mentor. Thereby, it is possible to gradually promote behavioral modification according to the maturity of the first user.

[0089] (6) Other modifications The storage device 11 may be connected to the client device 10 via the network NW. The display 21 may be integrated with the client device 10. The storage device 31 may be connected to the server 30 via the network NW.

[0090] Each step of the above information processing can be executed by either the client device 10 or the server 30. Also, in the above description, an example in which each step is executed in a specific order in each process is shown, but the execution order of each step is not limited to the example described as long as there is no dependency relationship.

[0091] As described above, the embodiments of the present invention have been described in detail, but the scope of the present invention is not limited to the above embodiments. Also, the above embodiments can be variously improved and modified without departing from the gist of the present invention. Also, the above embodiments and modifications can be combined.

Explanation of reference numerals

[0092] 1: Information processing system 10: Client device 11: Storage device 12: Processor 13: Input / Output Interface 14: Communication Interface 21: Display 30: Server 31: Memory Device 32: Processor 33: Input / Output Interface 34: Communication Interface

Claims

1. A computer, means for obtaining conversation information in a meeting between a first user in the role of a mentor and a second user in the role of a mentee; means for calculating a feature amount based on the conversation information for each unit time in the meeting; means for generating feedback information for prompting a change in conversation behavior for the first user based on the feature amount and norm information regarding a predetermined norm; means for presenting the feedback information to the first user; A program that causes it to function as such.

2. The conversation information includes speaker identification information and speech time information, The program according to claim 1.

3. The feature amount includes an index related to the duration of speech, The program according to claim 2.

4. The conversation information further includes information regarding the voice characteristics of the speech, and the information is limited to a range where the content of the speech cannot be restored at a level where it can be recognized, The program according to claim 2.

5. The feature amount includes an index related to the speech rate, The program according to claim 4.

6. Cause the computer to function as means for determining whether the conversation information for the unit time corresponds to a desired type of conversation behavior based on the feature amount, The means for generating the feedback information generates the feedback information so as to include information representing a time change regarding the occurrence situation of the desired type of conversation behavior in the meeting, The program according to any one of claims 1 to 5.

7. Cause the computer to function as means for determining whether the conversation information per unit time corresponds to a desired type of conversation behavior based on the feature amount. The means for generating the feedback information generates the feedback information so as to include information that can compare the occurrence status of the desired type of conversation behavior and the occurrence status of other types of conversation behavior during a predetermined period of the meeting. The program according to any one of claims 1 to 5.

8. Cause the computer to Function as means for determining whether the conversation information per unit time corresponds to a desired type of conversation behavior based on the feature amount. Function as means for conducting a questionnaire on the achievement status of the desired type of conversation behavior to the first user after the end of the meeting. And function as The means for generating the feedback information generates the feedback information so as to include information that can compare the answer of the first user to the questionnaire and the occurrence status of the desired type of conversation behavior. The program according to any one of claims 1 to 5.

9. The means for generating the feedback information identifies a conversation behavior that requests improvement from the first user based on the feature amount and the norm information, and generates the feedback information so as to include information indicating the conversation behavior. The program according to any one of claims 1 to 5.

10. Further cause the computer to function as means for identifying the maturity level of the first user as a mentor. Each of the norm information is associated with the maturity level as the mentor. The means for generating the feedback information identifies a conversation behavior that requests improvement from the first user based on the feature amount and the norm information associated with a maturity level equal to or lower than the maturity level corresponding to the maturity level of the first user as a mentor. The program according to claim 9.

11. Means for obtaining conversation information in a meeting between a first user in the role of a mentor and a second user in the role of a mentee; Means for calculating a feature amount based on the conversation information for each unit time in the meeting for each unit time in the meeting; Means for generating feedback information for prompting a change in conversation behavior to the first user based on the feature amount and norm information regarding a predetermined norm; Means for presenting the feedback information to the first user An information processing apparatus comprising:

12. A computer performs Steps of obtaining conversation information in a meeting between a first user in the role of a mentor and a second user in the role of a mentee; Steps of calculating a feature amount based on the conversation information for each unit time in the meeting for each unit time in the meeting; Steps of generating feedback information for prompting a change in conversation behavior to the first user based on the feature amount and norm information regarding a predetermined norm; Steps of presenting the feedback information to the first user A method for executing.

13. A system including a plurality of computers, Means for obtaining conversation information in a meeting between a first user in the role of a mentor and a second user in the role of a mentee; Means for calculating a feature amount based on the conversation information for each unit time in the meeting for each unit time in the meeting; Means for generating feedback information for prompting a change in conversation behavior to the first user based on the feature amount and norm information regarding a predetermined norm; Means for presenting the feedback information to the first user A system comprising:

Images