Information processing device, information processing method, and program

Abstract

Supporting user actions. [Solution] The information processing device 2 includes an acquisition unit 100 that acquires reflection information regarding the user's reflection on the user's behavior, an identification unit 102 that identifies the user's qualities based on the reflection information, and an output unit 106 that outputs support information generated based on reference information that associates, for each of one or more qualities, the achievements of one or more other users who possess the quality, and information regarding the user's qualities identified by the identification unit 102.

Description

[Technical Field]

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

[0002] Conventionally, there are known techniques for supporting reflection by users. For example, Patent Document 1 discloses a technique for enabling users to accurately and efficiently reflect on their actions or thoughts without causing variations. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-064236 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 cannot fully support user behavior. For example, there is room for further study regarding the information to be output to support user behavior. For example, there is room for further study regarding obtaining information regarding the user's awareness of thoughts and / or behavior on a specific theme, and improving the user's motivation and accuracy of behavior based on that information.

[0005] The present disclosure provides an information processing device, an information processing method, and a program that can support user actions. [Means for solving the problem]

[0006] An information processing device according to one aspect of the present disclosure includes an acquisition unit that acquires reflection information regarding a user's reflection on the user's behavior, an identification unit that identifies the user's qualities based on the reflection information, and an output unit that outputs support information generated based on reference information that associates, for each of one or more qualities, the achievements of one or more other users who possess the quality, and information regarding the user's qualities identified by the identification unit.

[0007] An information processing method according to another aspect of the present disclosure includes a computer acquiring reflection information regarding a user's reflection on the user's behavior, identifying the user's qualities based on the reflection information, and outputting support information generated based on reference information in which each of one or more qualities is associated with the achievements of one or more other users who possess the quality, and information regarding the user's qualities identified by the identification unit.

[0008] A program according to another aspect of the present disclosure causes a computer to acquire reflection information regarding a user's reflection on the user's behavior, identify the user's qualities based on the reflection information, and output support information generated based on reference information in which each of one or more qualities is associated with the achievements of one or more other users who possess the quality, and information regarding the user's qualities identified by the identification unit. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to support the user's actions. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram for explaining an overview of a system 1 according to the present embodiment. [Figure 2] 1 is a diagram illustrating an example of a functional configuration of a system 1 according to an embodiment of the present invention. [Figure 3]FIG. 2 is a diagram for explaining an example of the operation of the system 1 according to the present embodiment. [Figure 4] FIG. 2 is a diagram for explaining an example of the operation of the system 1 according to the present embodiment. [Figure 5] FIG. 2 is a diagram for explaining an example of the operation of the system 1 according to the present embodiment. [Figure 6] FIG. 2 is a diagram for explaining an example of the operation of the system 1 according to the present embodiment. [Figure 7] FIG. 2 is a diagram for explaining an example of the operation of the system 1 according to the present embodiment. [Figure 8] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 9] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 10] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 11] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 12] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 13] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 14] 10A and 10B are diagrams for explaining an example of a display screen of the terminal device 3 according to the present embodiment. [Figure 15] FIG. 2 is a diagram illustrating an example of the hardware configuration of each device included in the system 1 according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] 1. Overview In order to increase a subject's accuracy in a behavior, it is important to have the subject reflect on that behavior and to motivate the subject to undertake that behavior. Increasing a subject's accuracy in a behavior may include, for example, increasing the possibility and / or tendency of the subject to perform that behavior, thereby increasing the efficiency of improvement in that behavior. For example, in order to increase students' learning efficiency in school education, it is important to have students reflect on their learning. This is because even if multiple students study in the same way, the amount and quality of learning they gain may differ depending on whether or not they reflect on their learning. Furthermore, in order to increase students' learning efficiency in school education, it is equally important to motivate students to study.

[0012] Furthermore, while there are various methods for motivating a target person's behavior, particularly when motivating a target person's behavior through the encouragement of others, advice that focuses on the target person's qualities and / or advice based on objective data, rather than simply offering encouraging words, can be more effective in motivating the target person. For example, when a teacher motivates a student to study, advice based on the student's qualities (e.g., "they can work tenaciously" and "they bounce back quickly even when depressed") and / or advice based on objective data such as the success rates of their predecessors, can make the student more likely to approach their studies in a positive manner.

[0013] However, in the conventional technology, sufficient consideration has not been given to supporting the subject's reflection and motivating the subject's behavior, and there are cases where the subject's behavior cannot be sufficiently supported. In particular, in the field of education, the importance of support including individual support and / or care for each student is increasing against the backdrop of the diversification and / or multi-level nature of students, but it has sometimes been difficult to provide sufficient support due to the difficulty of transferring know-how between teachers and the so-called work style reform as an environment.

[0014] A system 1 according to this embodiment (hereinafter simply referred to as "system 1") can solve the problem associated with this example. Below, an overview of the operation of system 1 will be described with reference to FIG. 1. Note that, in this embodiment, an example will be described in which system 1 supports the learning of users who are students, but the scope of application of system 1 is not limited to this.

[0015] The system 1 includes a terminal device 3 used by a user and an information processing device 2 that communicates with the terminal device 3. First, the terminal device 3 accepts input of reflection information from the user and transmits the reflection information to the information processing device 2 (S1). The reflection information includes information related to the user's reflection on learning. In this embodiment, the user who inputs the reflection information is referred to as the "target user." The information processing device 2 receives the reflection information.

[0016] Next, the information processing device 2 identifies the qualities of the target user based on the received retrospective information (S2). "High conceptualization ability" is an example of the qualities of the target user. In this embodiment, the conceptualization ability includes the ability to grasp the relationship between different items.

[0017] Next, the information processing device 2 refers to information (hereinafter referred to as "reference information") in which, for each of one or more qualities, the entrance exam results of one or more other users who have the same qualities are associated, and acquires information regarding the entrance exam statistics of other users who have qualities that are common and / or similar to the qualities of the target user (S3). Note that the entrance exam results are an example of the achievements of other users. In this embodiment, other users with associated entrance exam results are referred to as "senior users." An example of reference information may be information indicating what qualities each of multiple senior users has and what universities they were accepted into (an example of entrance exam results).

[0018] As described above, the quality of the target user is identified as "high conceptualization ability." In the example of FIG. 1, senior users who have the quality of "high conceptualization ability" are "Senior A" and "Senior D," with "Senior A" associated with the entrance exam result "passed into XX University" and "Senior D" associated with the entrance exam result "passed into XX University." In the example of FIG. 1, the table of reference information is omitted, but according to the reference information, it is assumed that 120 senior users who have the quality of "high conceptualization ability" are associated with the entrance exam result "passed into XX University," and 300 senior users are associated with the entrance exam result "passed into XX University." The information processing device 2 acquires this information by referring to the reference information.

[0019] Next, the information processing device 2 generates support information based on the acquired information (S4). In the example of Fig. 1, the information processing device 2 generates support information indicating that the target user has a quality of "high conceptualization ability" and that among senior users who have this quality, 120 have the entrance exam result of "passed to XX University" and 300 have the entrance exam result of "passed to XX University".

[0020] Next, the information processing device 2 transmits the generated support information to the terminal device 3 (S5). The terminal device 3 receives the support information and can display it on its display screen.

[0021] To summarize the above, the information processing device 2 acquires reflection information regarding the target user's reflection on the target user's behavior (see S1), identifies the target user's qualities based on the reflection information (see S2), and outputs support information generated based on reference information in which the achievements of one or more senior users who possess each of the one or more qualities are associated with the quality, and information regarding the target user's qualities (see S3 to S5).

[0022] The information processing device 2 outputs support information generated based on the qualities of the target user and reference information that associates the qualities and achievements of senior users. As a result, in one example, the target user is provided with advice based on the qualities of the target user and / or advice based on objective data. In another example, the target user inputs reflection information in order to obtain the advice. As a result, the information processing device 2 can support, for example, the user in reflecting on an action and in starting that action.

[0023] The detailed configuration and operation of the system 1 will be described below with reference to FIGS.

[0024] 2. Functional Configuration The functional configuration of system 1 of this embodiment will be described with reference to Fig. 2. System 1 includes an information processing device 2, a terminal device 3, an LLM (Large Language Model) server device 4, a database server device 5, and a communication network 6. The information processing device 2, the LLM server device 4, the database server device 5, and the terminal device 3 are configured to be able to communicate with each other via the communication network 6.

[0025] 2.1 Terminal Device 3 The terminal device 3 is a communication device used by the target user. The terminal device 3 may be, for example, a smartphone, a personal computer, a tablet device, or a wearable device. The terminal device 3 includes an input interface, an output interface, and a communication interface. The input interface is an interface through which the terminal device 3 receives input from the target user. The input interface may be a touch panel, a microphone, a camera, a keyboard, a mouse, or the like. The output interface is an interface through which information is transmitted to the target user using images, sounds, or the like. The output interface may be a display (which may also serve as a touch panel), a speaker, or the like. The communication interface is an interface through which communication with other devices is realized via the communication network 6. The communication interface may be a wireless communication interface or a wired communication interface. The terminal device 3 may be able to access services provided by the information processing device 2 via, for example, a web browser, or may be able to access the services by installing dedicated software.

[0026] 2.2 Database Server Device 5 The database server device 5 may store reference information. The database server device 5 may be configured to extract at least a portion of the reference information in response to an inquiry (which may also be called a query) from another device and return the result to the other device. In one example, based on an inquiry including information about a quality, the database server device 5 extracts the entrance exam results of senior users who have the quality from the reference information and returns the result to the device that originated the inquiry.

[0027] 2.3 LLM Server Device 4 The LLM server device 4 is a device that provides services using LLM. The LLM may be a deep learning model that has hundreds of millions of parameters and has learned data related to natural languages ​​from hundreds of gigabytes or more. An example of an LLM is gpt-4o. In one example, the LLM server device 4 provides services using LLM via an API (Application Programming Interface).

[0028] In one embodiment, the LLM server device 4 receives an instruction (which may also be called a prompt) from another device and returns a response in accordance with the instruction to the other device. In one example, both the instruction and the response are text.

[0029] 2.4 Information processing device 2 The information processing device 2 executes at least a part of the processing related to supporting the behavior of the target user. In one embodiment, the information processing device 2 is a server device in the case where the terminal device 3 is a client device. In one embodiment, the information processing device 2 is a cloud server device. Note that the information processing device 2 may be, for example, a device including one or more virtual or physical web server devices and one or more virtual or physical database server devices.

[0030] The information processing device 2 includes a control unit 10, a storage unit 12, a network interface unit 14, and a bus 16. The control unit 10, the storage unit 12, and the network interface unit 14 are electrically connected via the bus 16.

[0031] 2.4.1 Control unit 10 The control unit 10 can function as an acquisition unit 100, an identification unit 102, a determination unit 104, an output unit 106, and a memory control unit 108 by executing various programs stored in the memory unit 12. Based on these functions, the control unit 10 can realize the configurations exemplarily described below.

[0032] 2.4.1.1 Basic Configuration As described with reference to FIG. 1 , in one embodiment, the acquisition unit 100 acquires reflection information regarding reflections by the target user on the target user's behavior. The identification unit 102 identifies the qualities of the target user based on the reflection information acquired by the acquisition unit 100. The output unit 106 outputs support information generated based on reference information in which the entrance exam results of one or more senior users who have each of the one or more qualities are associated with the one or more qualities, and information on the qualities of the target user identified by the identification unit 102.

[0033] In one example, the acquisition unit 100 acquires retrospective information input by the target user to the terminal device 3. In another example, the acquisition unit 100 acquires retrospective information stored in a server or the like of the school attended by the target user. The retrospective information may include, for example, at least one of text data, audio data, image data, and data in other formats.

[0034] Note that outputting the support information by the output unit 106 includes displaying, on the terminal device 3, information for displaying the support information.

[0035] 2.4.1.2 Configuration for acquiring retrospective information In one embodiment, the acquisition of the retrospective information by the acquisition unit 100 includes the following processes (a1) to (a4). (a1) Obtaining first review information regarding the first review entered by the target user. (a2) Determining comments for the first reflection based on the first reflection information. (a3) Outputting comments to the target user. (a4) After outputting the comment, obtain second reflection information regarding the second reflection entered by the target user. According to this configuration, the target user can input the second review information by referring to the comment, and therefore, the review can be supported more efficiently.

[0036] The retrospective information may include first retrospective information and second retrospective information. Therefore, the identification unit 102 may identify the qualities of the target user based on the first retrospective information and the second retrospective information.

[0037] Regarding (a2) above, the comments on the first reflection may include suggestions for enriching the target user's reflection. The suggestions for enriching the target user's reflection may be, for example, suggestions to encourage the target user to enumerate specific examples, suggestions to encourage the target user to analyze their own learning from different perspectives, suggestions to encourage the target user to supplement at least one of the 5W1H (when, where, who, what, why, and how), etc.

[0038] Regarding (a2) above, the comment on the first review may include an impression of the target user on the review. The impression of the target user on the review may be, for example, a phrase praising the target user.

[0039] Regarding (a2) above, when the first review information includes a predetermined phrase, the acquiring unit 100 may determine a comment corresponding to the predetermined phrase. For example, when the review information includes the phrase "everyday" (an example of a predetermined phrase), the acquiring unit 100 may acquire a comment "From what time to what time?" (an example of a comment corresponding to the predetermined phrase). In this case, the acquiring unit 100 may refer to a table or the like in which a comment corresponding to each of a plurality of phrases is associated with the phrase.

[0040] Regarding (a2) above, the acquisition unit 100 may determine the comment by inputting the first review information into a machine learning model. In one example, the machine learning model is configured by inputting multiple pieces of training data (teacher data) including combinations of review information for learning and comments corresponding to the review information for learning. Note that the machine learning model here may be an LLM hosted on the LLM server device 4, or may be a machine learning model other than an LLM, for example, hosted on the information processing device 2.

[0041] Regarding (a2) above, in one embodiment, determining a comment for the first review includes inputting an instruction (hereinafter referred to as a "review comment generation instruction") including information for determining a comment based on the first review information to the LLM. This configuration allows the comment to be determined more adaptively in accordance with the first review information.

[0042] Inputting the retrospective comment generation instruction into the LLM may include sending the retrospective comment generation instruction to the LLM server device 4. That is, the acquisition unit 100 may determine the comment by sending the retrospective comment generation instruction to the LLM server device 4 and receiving a response thereto.

[0043] 2.4.1.3 Structure of the Qualifications The qualities identified by the identification unit 102 may include qualities that can be strengths in the target user's learning. For example, the qualities identified by the identification unit 102 may be qualities related to the target user's study habits (such as "having the ability to persist"), qualities related to how the target user approaches learning (such as "not leaving things they don't understand unanswered"), and qualities related to learning aptitude (such as "being good at understanding the relationships between things"). The identification unit 102 may identify multiple strengths for the target user.

[0044] When the retrospective information includes a predetermined phrase, the identification unit 102 may identify a quality corresponding to the predetermined phrase as a quality possessed by the target user. For example, when the retrospective information includes the phrase "everyday" (an example of a predetermined phrase), the identification unit 102 may identify a quality of "persistence" (an example of a quality corresponding to the predetermined phrase) as a quality possessed by the target user. In this case, the identification unit 102 may refer to a data structure (e.g., a table) in which each of a plurality of phrases is associated with a quality corresponding to the phrase. For example, the data structure may be set in advance by an administrator of the information processing device 2 or the like and stored in the storage unit 12 or the like.

[0045] The identification unit 102 can identify the qualities of the target user by inputting the review information into a machine learning model. In one example, the machine learning model is configured by inputting multiple pieces of learning data (teacher data) including combinations of review information for learning and qualities identified based on the review information for learning. Note that the machine learning model here may be an LLM hosted on the LLM server device 4, or may be a machine learning model other than an LLM, for example, hosted on the information processing device 2.

[0046] In one embodiment, the identification unit 102 identifies the qualities of the target user by inputting, to the LLM, an instruction (hereinafter referred to as a "qualification identification instruction") including information for identifying the qualities of the target user based on the retrospective information acquired by the acquisition unit 100. This configuration makes it possible to identify qualities more adaptively based on the retrospective information.

[0047] Identifying the qualities of the target user by inputting a quality identification instruction into the LLM may include sending the quality identification instruction to the LLM server device 4 and identifying the qualities of the target user based on the response. An example of a quality identification instruction may include reflective information and the text "This reflective information was entered by the target user. Based on this reflective information, please list the qualities that you believe the target user possesses."

[0048] In one example, the quality specification instruction may include an instruction to select a quality that the target user is likely to possess from a data structure (for example, a table) in which multiple qualities are defined. In another example, the quality specification instruction may include an instruction to have the LLM freely determine (generate) qualities that the target user is likely to possess based on the review information acquired by the acquisition unit 100.

[0049] 2.4.1.4 Configuration for generating support information The output unit 106 outputs support information generated based on the reference information and information related to the qualities of the target user. Note that the output unit 106 can refer to the reference information by communicating with the database server device 5.

[0050] FIG. 3 is a diagram illustrating an example of reference information. In the example of FIG. 3, each senior user is associated with at least one of three qualities: "high conceptualization ability," "high stress tolerance," and "always calm." For example, "senior A" is associated with the quality of "high conceptualization ability." This indicates that "senior A" has the quality of "high conceptualization ability." Similarly, "senior B" is associated with the quality of "high stress tolerance" and the quality of "always calm." "senior C" is associated with the quality of "always calm." "senior D" is associated with the quality of "high conceptualization ability" and the quality of "always calm."

[0051] Furthermore, in the example of Figure 3, each senior user is associated with information indicating the university they were accepted into as an example of their entrance exam results. For example, "Senior A" is associated with the entrance exam result "Passed to XX University." This indicates that "Senior A" has the entrance exam result of being accepted into "XX University." Similarly, "Senior B" is associated with the entrance exam result "Passed to XX University." "Senior C" is associated with the entrance exam result "Passed to △△ University." "Senior D" is associated with the entrance exam result "Passed to □□ University."

[0052] The support information may include information regarding statistics of entrance exam results of senior users who have qualities common and / or similar to the qualities of the target user. In one example, if the target user has the quality of "high conceptualization ability," the output unit 106 may extract a group of senior users who have the quality of "high conceptualization ability" from the reference information and analyze the statistics of entrance exam results of the senior users included in the group. This allows the output unit 106 to output support information that statistically indicates what universities senior users who have the quality of "high conceptualization ability" have been accepted into. The support information may also include information regarding statistics of entrance exam results of senior users who have qualities different from the qualities of the target user.

[0053] If multiple qualities of the target user are identified, the support information may include information regarding statistics of entrance exam results of senior users who have qualities common and / or similar to at least one of the multiple qualities. In one example, if the target user has both the qualities of “high conceptualization ability” and “high stress tolerance,” the output unit 106 may extract a group of senior users who have the quality of “high conceptualization ability” and a group of senior users who have the quality of “high stress tolerance” from the reference information, and analyze the statistics of the entrance exam results of the senior users included in these groups. This allows the output unit 106 to output support information statistically indicating which universities senior users who have the qualities of “high conceptualization ability” and / or “high stress tolerance” have been accepted into. In this case, the entrance exam results of senior users with “high conceptualization ability” and senior users with “high stress tolerance” may be displayed in a manner that allows them to distinguish each other (e.g., separately).

[0054] The output unit 106 may output support information further based on the career path desired by the target user. For example, if the target user wishes to enter "XX University," and among senior users who share the same qualifications as the target user, 120 have the entrance exam result "passed to XX University," 300 have the entrance exam result "passed to XX University," and 240 have the entrance exam result "passed to XX University," the output unit 106 according to one example may output support information indicating that there are 120 senior users who have the entrance exam result "passed to XX University," without indicating that there are 300 senior users who have the entrance exam result "passed to XX University" and that there are 240 senior users who have the entrance exam result "passed to XX University." In another example, the output unit 106 may output support information emphasizing that there are 120 senior users who have the entrance exam result "Passed to XX University," compared to the fact that there are 300 senior users who have the entrance exam result "Passed to XX University" and the fact that there are 240 senior users who have the entrance exam result "Passed to □□ University."

[0055] The output unit 106 may output support information further based on school information about the school to which the target user belongs. The school information may include, for example, information about the location of the school and statistics about the career paths of the school's graduates. In one example, if the target user attends a high school located in a specified prefecture, the output unit 106 may output support information including the entrance exam results of senior users who have the same qualifications as the target user and who went on to a university located in the specified prefecture. In another example, if there are statistics showing that many senior users from the high school attended by the target user have gone on to "admitted to XX University" or "admitted to XX University," the output unit 106 may output support information including the entrance exam results of senior users who have the same qualifications as the target user and who went on to "admitted to XX University" or "admitted to XX University."

[0056] The output unit 106 may output support information further based on the academic ability of the target user. For example, if the academic ability of a target user attending high school falls within a specific academic ability band among multiple academic ability bands, the output unit 106 may output support information including the entrance exam results of senior users who have qualities in common with the target user and who have advanced to a university in the specific academic ability band. For example, academic ability may be classified into 15 levels, including S1 to S3, A1 to A3, B1 to B3, C1 to C3, and D1 to D3, based on the learning achievement zones in the Benesse Comprehensive Academic Achievement Test (registered trademark).

[0057] 2.4.1.5 Structure for determining goals In one embodiment, the output unit 106 further outputs, based on the review information, suggestion information regarding a goal of an action to be suggested to the target user. This configuration makes it easier to smoothly utilize the review by the target user in the next study.

[0058] Outputting the proposed information may mean transmitting information for displaying the proposed information on the terminal device 3, or may mean transmitting information for displaying the proposed information on a terminal device used by a teacher who instructs the target user.

[0059] The goal suggested to the target user may be a goal including a specific action for the target user to perform. The goal may indicate, for example, when, where, what, and how the target user should perform the action.

[0060] The goal suggested to the target user may be a goal related to strengthening the target user's weaknesses identified based on the reflection information. In one example, if the reflection information includes text data such as "I couldn't speak English," the suggested information may include a suggestion to secure or extend time for studying English.

[0061] The goal suggested to the target user may be a goal related to further developing the target user's strengths identified based on the reflection information. In one example, if the reflection information includes text data such as "I've mastered English," the suggested information may include a suggestion to use learning materials at a higher level regarding English.

[0062] The goal suggested to the target user may be a goal related to improving or maintaining the target user's study habits identified based on the reflection information. For example, if the reflection information includes text data such as "I was only able to study three days a week," the suggestion information may include a suggestion to set aside four days a week for study.

[0063] The goal to be proposed to the target user may be determined by the LLM. In one example, the output unit 106 may determine the goal to be proposed to the target user by inputting instructions (hereinafter referred to as "suggestion generation instructions") including information for determining the goal to be proposed based on the retrospective information to the LLM.

[0064] The suggestion information may include information about multiple goals suggested to the target user. For example, if the review information includes text data such as "I could only study three days a week, so I couldn't solve as many English problems as I thought I would," the suggestion information may include a suggestion to set aside four days a week for study and a suggestion to extend the time spent studying English. There may be three or more candidate goals to be suggested.

[0065] In one embodiment, the acquisition unit 100 further acquires goal information regarding a goal of the behavior input by the target user after the output unit 106 outputs the suggestion information. This configuration can more efficiently motivate the target user to perform the next learning. The storage control unit 108 can store the goal information in association with the target user.

[0066] In one example, the terminal device 3 accepts goal information input from the target user in a free-entry format. In this case, the terminal device 3 can display, for example, a free-entry field together with suggested information, and accept the target user's input of goal information via the free-entry field. Note that the goal information accepted by the terminal device 3 here may include specific and detailed information regarding the behavioral goal. The specific and detailed information regarding the behavioral goal may include information regarding when, where, what, and how the target user will perform the behavior.

[0067] In another example, the terminal device 3 accepts the input of goal information from the target user in a multiple-choice format. In this case, the terminal device 3 can display, for example, multiple goal candidates included in the proposal information in a multiple-choice format and accept the input of goal information from the target user via the multiple-choice options.

[0068] In one embodiment, the acquisition of the target information by the acquisition unit 100 includes the following processes (b1) to (b4). (b1) Obtaining first goal information regarding a first goal input by the target user. (b2) Determining comments on the primary goal based on the primary goal information. (b3) Outputting the comments to the target user. (b4) After outputting the comment, obtain second goal information regarding the second goal input by the target user. According to this configuration, the target user can input second goal information by referring to the comment, which can assist the target user in inputting a more substantial goal.

[0069] In this case, the goal information may include first goal information and second goal information. The storage control unit 108 may store the first goal information and the second goal information in association with the target user.

[0070] Regarding (b2) above, the comment on the first goal may include a suggestion for enriching the target user's goal. The suggestion for enriching the target user's goal may be, for example, a suggestion encouraging the user to list specific examples, a suggestion encouraging the user to set his or her own goal from a different perspective, or a suggestion encouraging the user to supplement at least one of the 5W1H (when, where, who, what, why, and how).

[0071] Regarding (b2) above, the comment on the first goal may include the target user's thoughts on the goal. The target user's thoughts on the goal may be, for example, a phrase praising the target user.

[0072] Regarding (b2) above, when the first goal information includes a predetermined phrase, the acquisition unit 100 may determine a comment corresponding to the predetermined phrase. For example, when the goal information includes the phrase "everyday" (an example of a predetermined phrase), the acquisition unit 100 may acquire the comment "How?" (an example of a comment corresponding to the predetermined phrase). In this case, the acquisition unit 100 may refer to a table or the like in which each of a plurality of phrases is associated with a comment corresponding to the phrase.

[0073] Regarding (b2) above, the acquisition unit 100 can determine the comment by inputting the first goal information into a machine learning model. In one example, the machine learning model is configured by inputting multiple pieces of training data (teacher data) including combinations of learning goal information and comments corresponding to the learning goal information. Note that the machine learning model here may be an LLM hosted on the LLM server device 4, or may be a machine learning model other than an LLM, for example, hosted on the information processing device 2.

[0074] Regarding (a2) above, in one embodiment, determining a comment for the first goal includes inputting an instruction (hereinafter referred to as a "goal comment generation instruction") including information for determining a comment based on the first goal information to the LLM. This configuration allows the comment to be determined more adaptively based on the goal information.

[0075] Inputting the target comment generation instruction into the LLM may include transmitting the target comment generation instruction to the LLM server device 4. That is, the acquisition unit 100 may determine the comment by transmitting the target comment generation instruction to the LLM server device 4 and receiving a response thereto.

[0076] 2.4.1.6 Reference Information Creation and Update Functions In one embodiment, the determination unit 104 determines whether one or more senior users have each of one or more qualities based on answers to predetermined questions by each of the one or more senior users. With this configuration, at least a portion of the reference information can be generated based on the answers to the predetermined questions by the senior users.

[0077] FIG. 4 is a diagram illustrating an example of the operation of the determination unit 104. The table in the upper part of FIG. 4 records the answers of each senior user to each question. Note that "1" to "5" in the table are numerical values ​​corresponding to "not at all applicable" to "extremely applicable." "Senior A" answers "2," "5," and "2" to the questions "I don't panic even when I don't understand a problem," "I'm quick to grasp the relationships between what I've learned," and "I don't get discouraged even when I get poor test results." Similarly, "Senior B" answers "1," "4," and "5" to each question. "Senior C" answers "5," "2," and "4" to each question. "Senior D" answers "5," "5," and "3" to each question.

[0078] The table at the bottom of Figure 4 shows the relationship between each question and a quality. The "O" in this table indicates the quality that a senior user who answered "5" to a certain question is determined to have. For example, according to this table, a senior user who answered "5" to the question "quickly grasps the relationships between what they have learned" is determined to have the quality of "high conceptualization ability."

[0079] According to the upper table of FIG. 4, "Senior A" answered "5" to the question, "Are you quick to grasp the relationships between what you have learned?". According to the lower table of FIG. 4, the senior user who answered "5" to this question is determined to have the quality of "high conceptualization ability." Therefore, the determination unit 104 determines that "Senior A" has the quality of "high conceptualization ability."

[0080] Furthermore, according to the upper table of FIG. 4, "Senior B" answered "5" to the question "Don't get depressed even if you get a bad test result." Then, according to the lower table of FIG. 4, the senior user who answered "5" to this question is determined to have the qualities of "high stress tolerance" and "always calm." Therefore, the determination unit 104 determines that "Senior B" has the qualities of "high stress tolerance" and "always calm."

[0081] Also, according to the table in the upper part of FIG. 4, "Senior C" answered "5" to the question "I don't panic even when I come across a problem I don't understand." Then, according to the table in the lower part of FIG. 4, the senior user who answered "5" to this question is determined to have the quality of "always calm." Therefore, the determination unit 104 determines that "Senior C" has the quality of "always calm."

[0082] Furthermore, according to the upper table of FIG. 4, "Senior D" answered "5" to both the question "I don't panic even when I come across a problem I don't understand" and the question "I'm quick to grasp the relationships between what I've learned." According to the lower table of FIG. 4, the senior user who answered "5" to these questions is determined to have the qualities of "always calm" and "high conceptualization ability." Therefore, the determination unit 104 determines that "Senior D" has the qualities of "always calm" and "high conceptualization ability."

[0083] The relationship between answers to questions and the qualities determined based on the answers is not limited to the example shown in Figure 4. For example, one quality may be associated with a combination of multiple answers, or a quality may be associated with the content and / or level of a certain answer.

[0084] In one embodiment, the determination unit 104 determines whether one or more senior users have each of one or more qualities based on the entrance exam results of the one or more senior users. With this configuration, at least a part of the reference information can be generated based on the entrance exam results of the senior users.

[0085] Fig. 5 is a diagram illustrating another example of the operation of the determination unit 104. The table in the upper part of Fig. 5 records the entrance exam results of each senior user. According to this table, "senior A" has the entrance exam result "passed XX University". "senior B" has the entrance exam results "passed XX University" and "passed △△ University". "senior C" has the entrance exam result "passed △△ University". "senior D" has the entrance exam results "passed XX University" and "passed △△ University".

[0086] The table at the bottom of Figure 5 shows the relationship between each entrance exam result and a quality. The "O" in this table indicates the quality that a senior user with a certain entrance exam result is determined to have. For example, according to this table, a senior user with an entrance exam result of "passed into XX University" is determined to have the quality of "high conceptualization ability."

[0087] According to the upper table of FIG. 5, "Senior A" has an entrance exam result of "passed into XX University." Then, according to the lower table of FIG. 5, the senior user who has this entrance exam result is determined to have the quality of "high conceptualization ability." Therefore, the determination unit 104 determines that "Senior A" has the quality of "high conceptualization ability."

[0088] Furthermore, according to the upper table of FIG. 5, "Senior B" has the entrance exam results of "passed XX University" and "passed △△ University." According to the lower table of FIG. 5, senior users who have these entrance exam results are determined to have the qualities of "high stress tolerance" and "always calm." Therefore, the determination unit 104 determines that "Senior B" has the qualities of "high stress tolerance" and "always calm."

[0089] Furthermore, according to the upper table of FIG. 5, "Senior C" has an entrance exam result of "Passed into △△ University." Then, according to the lower table of FIG. 5, the senior user who has this entrance exam result is determined to have the quality of "always calm." Therefore, the determination unit 104 determines that "Senior C" has the quality of "always calm."

[0090] Furthermore, according to the upper table of FIG. 5, "Senior D" has the entrance exam results of "passed to XX University" and "passed to △△ University." Then, according to the lower table of FIG. 5, senior users who have these entrance exam results are determined to have the qualities of "high conceptualization ability" and "always calm." Therefore, the determination unit 104 determines that "Senior D" has the qualities of "high conceptualization ability" and "always calm."

[0091] The relationship between entrance exam results and the qualities determined based on the entrance exam results is not limited to the example shown in Figure 5. For example, one quality may be associated with a combination of multiple entrance exam results, or a quality may be associated with the content and / or level of a certain entrance exam result.

[0092] In one embodiment, after the output unit 106 outputs the support information, the acquisition unit 100 further acquires the target user's entrance exam results, and the storage control unit 108 adds information associating the target user's qualifications with the target user's entrance exam results to the reference information. For example, the output unit 106 outputs support information to the target user before the exam, and after the target user finishes taking the exam, the acquisition unit 100 acquires the results (an example of entrance exam results), and the storage control unit 108 adds the target user's qualifications and the target user's exam results to the reference information. This configuration allows the accumulation of reference information, making it possible to provide higher quality support information to users who are junior to the target user. This allows information to flow back from one user to other users, building an ecosystem that further enhances the convenience and educational effectiveness of the services provided by the information processing device 2.

[0093] 2.4.2 Storage section 12 The storage unit 12 stores various types of information required for the operation of the information processing device 2. The storage unit 12 stores programs executed by the control unit .

[0094] 2.4.3 Network Interface Unit 14 The network interface unit 14 realizes communication with other devices via the communication network 6 .

[0095] 2.5 Communication Networks6 The communication network 6 realizes communication between the devices included in the system 1. The communication network 6 realizes communication between the devices based on, for example, the TCP / IP protocol.

[0096] 3 operations 6 to 14, an example of the operation of the system 1 will be described. Note that each operation of the information processing device 2 described below may be executed by at least one of the acquisition unit 100, the identification unit 102, the determination unit 104, the output unit 106, and the storage control unit 108 described above.

[0097] 3.1 Sequence 6 is a sequence diagram illustrating an example of the operation of the system 1. First, the information processing device 2 transmits information for displaying an input screen to the terminal device 3 (S100). Next, the terminal device 3 accepts input of first retrospective information from the target user via the input screen (S102). Next, the terminal device 3 transmits the input first retrospective information to the information processing device 2 (S104).

[0098] Next, the information processing device 2 transmits an instruction to generate a retrospective comment to the LLM server device 4 based on the first retrospective information (S106). In response to this, the LLM server device 4 determines a comment and returns a response including the comment to the information processing device 2 (S108).

[0099] Next, the information processing device 2 transmits information for displaying the comment received from the LLM server device 4 to the terminal device 3 (S110). The terminal device 3 displays the comment on the input screen and accepts input of second retrospective information from the target user (S112). Next, the terminal device 3 transmits the input second retrospective information to the information processing device 2 (S114).

[0100] Next, the information processing device 2 transmits a qualification identification instruction to the LLM server device 4 based on the first retrospective information and the second retrospective information (S116). In response to this, the LLM server device 4 identifies the qualifications of the target user and returns a response including information on the qualifications to the information processing device 2 (S118).

[0101] Next, the information processing device 2 inquires of the database server device 5 about the entrance exam results of senior users who have the qualifications based on the information about the qualifications of the subject received from the LLM server device 4 (S120). In response to the inquiry, the database server device 5 extracts information from the reference information and returns it to the information processing device 2 (S122).

[0102] Next, the information processing device 2 generates support information including information about the qualifications of the target user and information about the entrance exam results of senior users who have the qualifications (S124), and transmits the support information to the terminal device 3 (S126).

[0103] 7 is a sequence diagram for explaining another example of the operation of the system 1. Note that in FIG. 7, an example of the operation after step S126 in FIG. 6 will be explained.

[0104] The information processing device 2 transmits the support information to the terminal device 3 (S126), and then transmits a proposal generation instruction to the LLM server device 4 based on the received review information (S200). In response to this, the LLM server device 4 determines a goal to be proposed to the target user, and returns a response including information about the goal to the information processing device 2 (S202).

[0105] Next, the information processing device 2 generates proposal information including information related to the goal proposed to the target user (S204) and transmits the proposal information to the terminal device 3 (S206). The terminal device 3 displays the proposal information on the input screen and receives input of first goal information from the target user (S208). Next, the terminal device 3 transmits the input first goal information to the information processing device 2 (S210).

[0106] Next, the information processing device 2 transmits a target comment generation instruction to the LLM server device 4 based on the first target information (S212). In response to this, the LLM server device 4 determines a comment and returns a response including the comment to the information processing device 2 (S214).

[0107] Next, the information processing device 2 transmits information for displaying the comment received from the LLM server device 4 to the terminal device 3 (S216). The terminal device 3 displays the comment on the input screen and accepts input of second goal information from the target user (S218). Next, the terminal device 3 transmits the input second goal information to the information processing device 2 (S220). The information processing device 2 stores goal information including the first goal information and the second goal information in association with the target user (S222).

[0108] 3.2 Display screen example Fig. 8 is a diagram showing an example of a display screen that may be displayed on the terminal device 3 immediately after step S100 in Fig. 6. The example display screen in Fig. 8 displays an input field d100, a progress bar d102, a comment display field d104, a comment request button d106, and a review confirmation button d108.

[0109] The input field d100 is a display element where the target user can input reflection information. In the example display screen of Figure 8, reflection information has not yet been entered, so placeholder text (e.g., "I was able to study every day for a week. I especially worked hard on chemistry, which I am not good at.") is displayed.

[0110] The progress bar d102 is a display element whose colored portion expands depending on the degree of completion of the retrospective information input. In the example display screen of Fig. 8, the retrospective information has not yet been input, so the colored portion is minimal.

[0111] The comment display field d104 is a display element that displays comments received from the information processing device 2. In the example display screen of Fig. 8, no comments have been created yet, so the comment display field d104 is blank.

[0112] The comment request button d106 is a button for requesting the creation of a comment using the text that has been input in the input field d100 at the time of pressing as first retrospective information.

[0113] The review confirmation button d108 is a button for ending input into the input field d100 at the time of pressing, and for confirming the review information with the text that has been input up to that point.

[0114] 9 shows an example of a display screen in the case where the target user has input first review information into the input field d100 in the example display screen of FIG. 8. The example display screen of FIG. 9 can be displayed on the terminal device 3 immediately after step S102 of FIG. 6. Compared to the example display screen of FIG. 8, the first review information entered into the input field d100 is, "I was able to study math and English for 30 minutes each every day. I didn't do perfectly on the test, but I felt like I made more progress than last time." This also causes the progress bar d102 to reach the "Good" area.

[0115] In this example, the target user presses the comment request button d106 in the state of the display screen example in Fig. 9. As a result, the terminal device 3 transmits the input content in the input field d100 at this time to the information processing device 2 as first retrospective information (see S104 in Fig. 6).

[0116] Fig. 10 shows an example of a display screen when the target user presses the comment request button d106 on the example display screen of Fig. 9. The example display screen of Fig. 10 can be displayed on the terminal device 3 immediately after step S110 of Fig. 6. Comparing it to the example display screen of Fig. 9, the comment display field d104 displays a comment saying, "That's a good, specific review! Please add in what areas you felt you made progress and in what areas you didn't."

[0117] FIG. 11 shows an example of a display screen in which the target user refers to the comment and adds something to the input field d100 in the example display screen of FIG. 10. The example display screen of FIG. 11 can be displayed on the terminal device 3 immediately after step S112 of FIG. 6. Compared to the example display screen of FIG. 10, the second review information has been added to the input field d100, stating, "In particular, in mathematics, I was able to solve problems that calculated the volume of a solid figure. I think this is because I realized that calculating the volume of a solid figure has some commonality with calculating the area of ​​a plane figure. However, I still didn't understand the vector problem." This also causes the progress bar d102 to reach the "Great" area.

[0118] Fig. 12 shows an example of a display screen when the target user presses the review confirmation button d108 on the example display screen of Fig. 11. The example display screen of Fig. 12 can be displayed on the terminal device 3 immediately after step S126 of Fig. 6. The example display screen of Fig. 12 displays a comment d200, a quality name d201, achievement information d202, achievement information d204, and a progress button d206. The comment d200, the quality name d201, achievement information d202, and achievement information d204 are all examples of support information.

[0119] Comment d200 includes the text, "From the statement, 'I think it's because I realized that calculating the volume of a three-dimensional figure has something in common with calculating the area of ​​a two-dimensional figure,' we can see that the user has a strong ability to grasp the relationships between things." The quality name d201 is displayed as "conceptualization ability." The part of comment d200, "We can see that the user has a strong ability to grasp the relationships between things," and the quality name d210 correspond to information about the quality of the target user.

[0120] The performance information d202 and the performance information d204 indicate the number of successful applicants to "XX University" and "XX University," respectively. These are examples of information generated based on the reference information and information about the qualifications of the target user, and are also examples of information about the statistics of the learning performance of senior users who have qualifications common and / or similar to the qualifications of the target user.

[0121] The progress button d206 is a button for transitioning to a screen for inputting goal information.

[0122] FIG. 13 shows an example of a display screen when the target user presses the progress button d206 on the example display screen of FIG. 12. The example display screen of FIG. 13 can be displayed on the terminal device 3 immediately after step S206 of FIG. 7. The example display screen of FIG. 13 displays a target candidate button d300, a target candidate button d302, and a target candidate button d304. The target candidates displayed on the target candidate button d300, the target candidate button d302, and the target candidate button d304 each correspond to an example of suggested information. By pressing one of these buttons, the target user can select the target displayed on that button.

[0123] Fig. 14 shows an example of a display screen when the target user presses the goal candidate button d302 on the example display screen of Fig. 13. The example display screen of Fig. 14 may be displayed on the terminal device 3 immediately before step S208 of Fig. 7. The example display screen of Fig. 14 displays an input field d400, a progress bar d402, a comment display field d404, a comment request button d406, and a goal confirmation button d408.

[0124] The input field d400 is a display element in which the target user can input goal information. In the example display screen of FIG. 14, goal information has not yet been input, so placeholder text (e.g., "After waking up in the morning, study math and English for 15 minutes each, and then study another 30 minutes each when you get home from school") is displayed. The request for comments on goal information and the display thereof may be realized in a manner similar to the request for comments on review information and the display thereof described with reference to FIG. 8, and therefore will not be described below.

[0125] 4. Hardware Configuration 15, an example of a hardware configuration in which the devices included in the above-described system 1 are realized by a computer 70 will be described. Note that the functions of each device can also be realized by dividing them into multiple devices.

[0126] As shown in FIG. 15, a computer 70 includes a processor 700 , a storage device 702 , an input I / F 704 , a data I / F 706 , a communication I / F 708 , and a display device 710 .

[0127] The processor 700 controls various processes in the computer 70 by executing programs stored in the storage device 702. For example, each functional unit included in the control unit 10 of the information processing device 2 can be realized by the processor 700 executing the programs stored in the storage device 702.

[0128] The storage device 702 is a storage medium such as a RAM (Random Access Memory), etc. The RAM temporarily stores the program code of the program executed by the processor 700 and data required when the program is executed.

[0129] The storage device 702 may also be a non-volatile storage medium such as a hard disk drive (HDD) or flash memory. The storage device 702 stores an operating system and various programs for implementing the above-described configurations. The storage medium storing the various programs may be a non-transitory computer-readable medium. The storage device 702 may also store tables that register various types of information and a DB that manages the tables. Such programs and data are loaded into the storage device 702 as needed and referenced by the processor 700.

[0130] The input I / F 704 is a device for receiving input from a user. Specific examples of the input I / F 704 include a camera, a button, a microphone, a keyboard, a mouse, a touch panel, various sensors, and a wearable device. The input I / F 704 may be connected to the computer 70 via an interface such as a USB (Universal Serial Bus).

[0131] The data I / F 706 is a device for inputting data from outside the computer 70. A specific example of the data I / F 706 is a drive device for reading data stored in various storage media. The data I / F 706 may be provided outside the computer 70. In this case, the data I / F 706 is connected to the computer 70 via an interface such as a USB.

[0132] The communication I / F 708 is a device for performing data communication with devices external to the computer 70 via the communication network 6, either wired or wirelessly. The communication I / F 708 may be provided external to the computer 70. In this case, the communication I / F 708 is connected to the computer 70 via an interface such as a USB.

[0133] The display device 710 is a device for displaying various types of information. Specific examples of the display device 710 include a liquid crystal display, an organic EL (Electro-Luminescence) display, and a display of a wearable device. The display device 710 may be provided outside the computer 70. In this case, the display device 710 is connected to the computer 70 via, for example, a display cable. Furthermore, when a touch panel is adopted as the input I / F 704, the display device 710 can be configured as an integral part of the input I / F 704.

[0134] Furthermore, the components of the devices included in the system 1 described in the above embodiment are assumed to realize predetermined processing in cooperation with other hardware by the processor 700 executing a program stored in the storage device 702. In other words, these components are envisioned as both software or firmware and the corresponding hardware, and in both of these concepts, they are also referred to as "functions," "means," "parts," "processing circuits," "units," or "modules," and can be interpreted as such.

[0135] 5. Variations The above-described embodiments are intended to facilitate understanding of the present disclosure and are not intended to limit the present disclosure. The configurations that the embodiments may have are not limited to those exemplified and may be modified as appropriate. Furthermore, configurations shown in different embodiments may be partially substituted or combined with each other.

[0136] The configurations described in the above embodiments with the prefix "first" or "second" can be extended to relationships from "first" to "Nth" (where N is a natural number) based on the knowledge of a person skilled in the art. In one example, although an example of acquiring first retrospective information and second retrospective information has been described in the above embodiments, the information processing device 2 may acquire the first retrospective information to the Nth retrospective information. At this time, the information processing device 2 may determine and output a comment for each of the first retrospective information to the (N-1)th retrospective information. In another example, although an example of acquiring first target information and second target information has been described in the above embodiments, the information processing device 2 may acquire the first target information to the Nth target information. At this time, the information processing device 2 may determine and output a comment for each of the first target information to the (N-1)th target information.

[0137] In the above embodiment, the achievements of the senior users are described as entrance exam results, but are not limited thereto. In one example, the achievements of the senior users may be their career paths after graduation, deviation scores, rankings, and pass / fail results of any academic ability test, qualifications acquired in relation to learning, or learning habits acquired. Therefore, in one example, the support information may include information regarding the distribution of deviation scores of senior users who have qualities common and / or similar to the qualities of the target user. Furthermore, in one example, the support information may include information regarding qualifications acquired by senior users who have qualities common and / or similar to the qualities of the target user. In another example, the achievements of the senior users may include the names, industries, and occupations of companies where the senior users were employed after graduating from school. Therefore, the support information may include information regarding the names of companies where senior users who have qualities common and / or similar to the qualities of the target user were employed.

[0138] In the above embodiment, an example has been described in which the target user is a student and the target user's behavior is studying, but the scope of application of this embodiment is not limited to this. In one example, the target user may be someone who practices a sport, and the target user's behavior may be practicing that sport. In another example, the target user may be a working person, and the target user's behavior may be behavior for career advancement or qualification acquisition. In this way, system 1 can be used in a general purpose manner to help users improve their accuracy and / or ability regarding some behavior.

[0139] In the above embodiment, an example has been described in which the information processing device 2 receives services related to LLM from the LLM server device 4, but the present invention is not limited to this. The information processing device 2 can host an LLM on its own device and execute the various processes described in the above embodiment based on the LLM.

[0140] In the above embodiment, examples of qualities include "high conceptualization ability," "high stress tolerance," and "always calm," but qualities are not limited to these. Other examples of qualities include "ability to discern the essence," "ability to learn from experience," and "ability to overcome difficulties."

[0141] In the above embodiment, an example has been described in which input of retrospective information and goal information is accepted from the target user. In this regard, the terminal device 3 may display retrospective information previously entered by other users different from the target user (e.g., senior users, users equivalent to the target user's contemporaries, and junior users) on a screen (see FIGS. 8 to 11) for accepting input of retrospective information from the target user. Similarly, the terminal device 3 may display goal information previously entered by other users on a screen (see FIGS. 13 to 14) for accepting input of goal information from the target user. This configuration allows the target user to input their own retrospective information and / or goal information by referring to the retrospective information and / or goal information entered by other users, thereby encouraging the target user to enter more substantial retrospective information and / or goal information.

[0142] In the above embodiment, the system 1 includes the LLM server device 4, and an example in which an LLM is used has been described, but this is not limiting. The LLM server device 4 may be replaced with an AI server device, and the LLM may be replaced with another AI model. The AI ​​model may include, but is not limited to, an LLM, and may be, for example, a machine learning model, a language model, or a base model.

[0143] 6 Supplementary Information The wording in this embodiment can be understood as follows to the extent that no contradiction occurs.

[0144] In this embodiment, "executing a predetermined process based on predetermined information" may mean executing the predetermined process based on at least a part of the predetermined information, executing the predetermined process based on at least the predetermined information, or executing the predetermined process probabilistically based on the predetermined information. In other words, "executing a predetermined process based on predetermined information" is not limited to executing the predetermined process based only on the predetermined information.

[0145] In this embodiment, "executing another process based on a predetermined process" may mean executing the other process after the predetermined process has been executed, executing the predetermined process and the other process consecutively, executing the other process based on information determined by the predetermined process, executing the other process on the condition that the predetermined process has been executed, or executing the other process by means of the predetermined process. Note that "executing another process by a predetermined process" may also be understood as the same as "executing another process based on a predetermined process."

[0146] In this embodiment, "the specified information includes other information" may mean either that at least a part of the specified information is the other information, or that the other information can be obtained based on the specified information.

[0147] In this embodiment, "a specified process includes another process" may mean either that at least a part of the specified process is the other process (i.e., that the other process is performed in the process of obtaining the result of the specified process), or that one aspect of the specified process is the other process.

[0148] In this embodiment, "a predetermined object corresponds to another object" may mean any of the following: the predetermined object and the other object are in a one-to-one relationship; the other object is included in a predetermined set identified based on the predetermined object; or the other object can be identified based on the predetermined object. Note that "a predetermined object corresponds to another object" is not limited to being managed, for example, in a database. Furthermore, "a predetermined object is associated with another object" may be understood in the same way as "a predetermined object corresponds to another object."

[0149] In this embodiment, "obtaining information" includes making the information processable in the control unit 10. "Obtaining information" may mean, for example, receiving the information from another device, obtaining the information through predetermined processing, reading the information from the storage unit 12, etc.

[0150] In this embodiment, "generating information" may mean either making the information obtained by a specified process processable in the control unit 10, or storing the information obtained by a specified process in the memory unit 12.

[0151] In this embodiment, "determining information" may mean either selecting at least one piece of information from one or more pieces of information, or newly generating that information.

[0152] In this embodiment, "outputting information" may mean either transmitting the information to another device or outputting the information as sound or video.

[0153] 7 Configuration example The present disclosure includes the following techniques:

[0154] [Appendix 1] An information processing device 2 comprising: an acquisition unit 100 that acquires reflection information regarding a user's reflection on the user's behavior; an identification unit 102 that identifies the user's qualities based on the reflection information; and an output unit 106 that outputs support information generated based on reference information in which each of one or more qualities is associated with the achievements of one or more other users who have the quality, and information regarding the user's qualities identified by the identification unit 102.

[0155] [Appendix 2] The information processing device 2 described in Appendix 1, wherein the identification unit 102 identifies the user's qualities by inputting instructions including information for identifying the user's qualities based on the retrospective information to the AI ​​model.

[0156] [Appendix 3] The information processing device 2 described in Appendix 1 or 2, wherein the acquisition unit 100 further includes a memory control unit 108 that acquires the user's achievements after the output unit 106 outputs the support information and adds information that associates the user's qualities with the user's achievements to the reference information.

[0157] [Appendix 4] An information processing device 2 described in any one of Appendices 1 to 3, further comprising a judgment unit 104 that judges whether one or more other users have each of one or more qualities based on the achievements of the one or more other users and / or answers to predetermined questions by each of the one or more other users.

[0158] [Appendix 5] An information processing device 2 described in any one of Appendices 1 to 4, wherein the acquisition unit 100 acquiring the retrospective information includes acquiring first retrospective information regarding a first retrospective input by the user, determining a comment for the first retrospective based on the first retrospective information, outputting the comment to the user, and after outputting the comment, acquiring second retrospective information regarding a second retrospective input by the user, and the identification unit 102 identifies the user's qualities based on the first retrospective information and the second retrospective information.

[0159] [Appendix 6] An information processing device 2 as described in Appendix 5, wherein determining a comment includes inputting instructions to an AI model including information for determining a comment based on the first retrospective information.

[0160] [Appendix 7] The information processing device 2 according to any one of appendices 1 to 6, wherein the output unit 106 further outputs, based on the review information, suggested information relating to a goal of an action suggested to the user.

[0161] [Appendix 8] The information processing device 2 according to Supplementary note 7, wherein the acquisition unit 100 further acquires goal information related to a goal of the action input by the user after the output unit 106 outputs the proposal information.

[0162] [Appendix 9] The information processing device 2 described in Appendix 8, wherein the acquisition unit 100 acquiring goal information includes acquiring first goal information regarding a first goal input by the user, determining a comment for the first goal based on the first goal information, outputting the comment to the user, and acquiring second goal information regarding a second goal input by the user after outputting the comment, further comprising a storage control unit 108 that associates the first goal information and the second goal information with the user and stores them.

[0163] [Appendix 10] The information processing device 2 described in Appendix 9, wherein determining the comment includes inputting instructions to the AI ​​model including information for determining the comment based on the first target information.

[0164] [Appendix 11] An information processing method in which a computer 70 acquires reflection information regarding a user's reflection on the user's behavior, identifies the user's qualities based on the reflection information, and outputs support information generated based on reference information in which each of one or more qualities is associated with the achievements of one or more other users who have the quality, and information regarding the user's qualities identified by the identification unit 102.

[0165] [Appendix 12] A program that causes a computer (70) to acquire reflection information regarding a user's reflection on the user's behavior, identify the user's qualities based on the reflection information, and output support information generated based on reference information that associates, for each of one or more qualities, the achievements of one or more other users who have the quality, and information regarding the user's qualities identified by the identification unit (102). [Explanation of symbols]

[0166] 1...system, 2...information processing device, 3...terminal device, 4...LLM server device, 5...database server device, 6...communication network, 10...control unit, 12...storage unit, 70...computer, 100...acquisition unit, 102...identification unit, 104...determination unit, 106...output unit, 108...storage control unit

Claims

1. an acquisition unit that acquires review information regarding a review by a user on the user's behavior; an identification unit that identifies qualities of the user based on the retrospective information; an output unit that outputs support information for each of a plurality of other users, generated based on reference information associated with one or more qualities and achievements of the other users, and information on the qualities of the user identified by the identification unit, wherein the support information includes statistical information on statistics of achievements of one or more other users among the plurality of other users who have qualities common and / or similar to the qualities of the user identified by the identification unit; An information processing device comprising:

2. The information processing device according to claim 1 , wherein the identification unit identifies the user's qualities by inputting an instruction including information for identifying the user's qualities based on the retrospective information to an AI model.

3. the acquisition unit further acquires a performance record of the user after the output unit outputs the support information; The information processing apparatus according to claim 1 , further comprising a storage control unit that adds information that associates the user's qualities with the user's achievements to the reference information.

4. The information processing device according to claim 1, further comprising a determination unit that determines whether the plurality of other users have each of the one or more qualities based on the achievements of the plurality of other users and / or answers given by each of the plurality of other users to predetermined questions.

5. The acquisition unit acquires the retrospective information, Acquiring first review information regarding a first review input by the user; determining a comment for the first review based on the first review information; outputting the comment to the user; After outputting the comment, acquiring second review information regarding a second review input by the user; Including, The information processing device according to claim 1 , wherein the specifying unit specifies the qualities of the user based on the first retrospective information and the second retrospective information.

6. The information processing device according to claim 5 , wherein determining the comment includes inputting, to an AI model, an instruction including information for determining the comment based on the first retrospective information.

7. The information processing device according to claim 1 , wherein the output unit further outputs suggested information relating to a goal of the action suggested to the user based on the review information.

8. The information processing device according to claim 7 , wherein the acquisition unit further acquires goal information relating to a goal of the action input by the user after the output unit outputs the suggestion information.

9. The acquisition unit acquires the target information, acquiring first goal information relating to a first goal input by the user; determining a comment for the first goal based on the first goal information; outputting the comment to the user; After outputting the comment, acquiring second goal information regarding a second goal input by the user; Including, The information processing apparatus according to claim 8 , further comprising: a storage control unit that stores the first goal information and the second goal information in association with the user.

10. The information processing device according to claim 9 , wherein determining the comment includes inputting an instruction including information for determining the comment based on the first target information to an AI model.

11. The computer Obtaining reflection information regarding a reflection by the user on the user's behavior; Identifying qualities of the user based on the retrospective information; outputting support information for each of a plurality of other users, the support information being generated based on reference information to which one or more qualities of the other users and achievements of the other users are associated, and information on the qualities of the identified user, the support information including statistical information on statistics of achievements of one or more other users among the plurality of other users who have qualities common and / or similar to the qualities of the identified user; An information processing method that performs the above.

12. On the computer, Obtaining reflection information regarding a reflection by the user on the user's behavior; Identifying qualities of the user based on the retrospective information; outputting support information for each of a plurality of other users, the support information being generated based on reference information to which one or more qualities of the other users and achievements of the other users are associated, and information on the qualities of the identified user, the support information including statistical information on statistics of achievements of one or more other users among the plurality of other users who have qualities common and / or similar to the qualities of the identified user; A program that executes.

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