Information processing device, information processing method, and program

Abstract

To solve the problem that conventionally, it is not possible to appropriately propose information helpful for the growth of members of an organization.SOLUTION: It is possible to appropriately propose information helpful for the growth of members of an organization by an information processing device 1. The information processing device 1 comprises: an information reception unit 121 for receiving an own member answer representing answers to a first questionnaire regarding one-self of a target member; an information acquisition unit 133 for acquiring the own member answer received by the information reception unit 121 or an own statistical result from the statical processing of the own member answer, and acquiring proposal identification information for identifying information to be proposed for the growth of the target member by using the own member answer or the own statistical result; and an information output unit 141 for outputting proposal identification information or proposal content information corresponding to the proposal identification information.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an information processing device or the like that proposes information for the growth of members of an organization. [Background technology]

[0002] Conventionally, there has been an improvement support system that generates job satisfaction evaluation information based on survey results or reports from employees at production sites and supports the improvement of job satisfaction for employees engaged in the work (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2022-47836 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the prior art has been unable to adequately provide information for the growth of organization members. [Means for solving the problem]

[0005] The information processing device of the first invention is an information processing device comprising an information receiving unit that receives the self-member responses, which are responses to a first questionnaire, which is a questionnaire about the target member himself / herself, an information acquisition unit that acquires the self-member responses or self-statistical results, which are the results of statistical processing of the self-member responses or the self-member responses received by the information receiving unit, and acquires proposal identification information, using the self-member responses or the self-statistical results, that identifies information that should be proposed for the growth of the target member, and an information output unit that outputs the proposal identification information or proposal content information corresponding to the proposal identification information.

[0006] With this configuration, the member's own response can be used to appropriately suggest information for the growth of the members of the organization.

[0007] In addition, the information processing device of the second invention is an information processing device in which, compared to the first invention, the information receiving unit also receives one or more other member responses which are responses to a second questionnaire, which is a questionnaire for other members addressed to target members, and the information acquisition unit acquires the own member response or own statistical result received by the information receiving unit and the one or more other member responses or other statistical results which are the result of statistical processing of the one or more other member responses received by the information receiving unit, and acquires proposed identification information using the own member response or own statistical result and the one or more other member responses or other statistical results.

[0008] With this configuration, it is possible to appropriately suggest information for the growth of members of an organization by using the responses of other members as well.

[0009] In addition, the information processing device of the third invention, compared to the first or second invention, further includes a learning information storage unit in which learning information created using two or more teacher data including a member's own response, which is a response of a member to a first questionnaire about himself / herself, or a self-statistical result, which is the result of statistical processing of the self-member response, and proposal identification information that identifies information to be proposed for the growth of the member, is stored, and the information acquisition unit is an information processing device that acquires original data for acquiring proposal identification information that identifies information to be proposed to the target member using the member's own response or the self-statistical result, and acquires the proposal identification information using the original data and the learning information.

[0010] With this configuration, it is possible to use the learning information to appropriately suggest information for the growth of members of an organization.

[0011] In addition, the information processing device of the fourth invention is an information processing device in which, compared to the first invention, the learning information is a learning model acquired by performing a machine learning learning process using two or more teacher data, and the information acquisition unit is an information processing device that performs a machine learning prediction process using the original data and the learning model, and acquires proposed identification information.

[0012] With this configuration, it is possible to appropriately suggest information for the growth of members of an organization through machine learning processing.

[0013] In addition, the information processing device of the fifth invention, compared to the fourth invention, further includes a candidate management unit in which one or more candidate information, which is proposal identification information of the acquired candidates, is stored, and the two or more teacher data include one or more positive examples corresponding to a first flag indicating that proposal content information corresponding to the proposal identification information held by the teacher data will be effective when output to one member, and one or more negative examples corresponding to a second flag indicating that the proposal content information corresponding to the proposal identification information held by the teacher data will not be effective when output to one member, the learning information is a learning model acquired by performing a machine learning learning process using the one or more positive examples and the one or more negative examples, and the information acquisition unit is an information processing device that performs a machine learning prediction process using the original data, the candidate information, and the learning model for each of the one or more candidate information, acquires the first flag or the second flag, and acquires proposal identification information, which is candidate information whose acquired flag is the first flag.

[0014] With this configuration, it is possible to appropriately suggest information for the growth of members of an organization through machine learning processing.

[0015] In addition, the information processing device of the sixth invention, compared to the fourth invention, further includes a candidate management unit in which one or more candidate information, which is proposed identification information of the acquired candidates, is stored, the two or more teacher data have a score corresponding to the proposed identification information held by the teacher data, the learning information is a learning model acquired by performing a machine learning learning process using the two or more teacher data, and the information acquisition unit is an information processing device that performs a machine learning prediction process using the original data, the candidate information, and the learning model for each of the one or more pieces of candidate information, acquires a score, and acquires the proposed identification information, which is candidate information whose score satisfies a good condition.

[0016] With this configuration, it is possible to appropriately suggest information for the growth of members of an organization through machine learning processing.

[0017] In addition, the information processing device of the seventh invention is an information processing device in which, compared to the fourth invention, the two or more teacher data are data having original data as an explanatory variable and proposed identification information as a target variable, the learning information is a learning model obtained by performing a machine learning learning process using the two or more teacher data, and the information acquisition unit acquires original data for acquiring proposed identification information that identifies information to be proposed to a target member using its own member's responses or its own statistical results, and performs machine learning prediction processing using the original data and the learning model to acquire the proposed identification information.

[0018] With this configuration, it is possible to appropriately suggest information for the growth of members of an organization through machine learning processing.

[0019] In addition, the information processing device of the eighth invention is an information processing device in which, compared to the third invention, the learning information is a learning model used by the generative AI module, and the information acquisition unit provides original data to the generative AI module and acquires proposed identification information from the generative AI module.

[0020] With this configuration, the generative AI can appropriately suggest information for the growth of members of an organization.

[0021] In addition, the information processing device of the ninth invention is an information processing device in which, compared to the third invention, the learning information is a correspondence table having two or more correspondence information indicating the correspondence between original data and proposed identification information, and the information acquisition unit determines from the correspondence table original data that satisfies the adoption conditions, and acquires from the correspondence table the proposed identification information that pairs with the determined original data.

[0022] With this configuration, the correspondence table can appropriately suggest information for the growth of members of an organization.

[0023] Furthermore, the information processing device of the tenth invention is an information processing device according to the first invention, wherein each of the two or more questions included in the first questionnaire corresponds to any one or more of the two or more proposed identification information, and the information acquisition unit acquires, for each of the two or more proposed identification information, its own statistical result which is the result of statistical processing of the answers to each of the one or more questions for the proposed identification information, and acquires the one or more proposed identification information whose own statistical result satisfies a selection condition.

[0024] With this configuration, it is possible to appropriately suggest information for the growth of members of an organization. Effect of the Invention

[0025] According to the information processing device of the present invention, it is possible to appropriately suggest information for the growth of members of an organization. [Brief description of the drawings]

[0026] [Figure 1] Conceptual diagram of information system A in embodiment 1. [Diagram 2] Block diagram of Information System A [Diagram 3] A flowchart illustrating an example of the operation of the information processing device 1. [Figure 4] 11 is a flowchart illustrating an example of the first original data acquisition process. [Diagram 5] 11 is a flowchart illustrating an example of the second original data acquisition process. [Figure 6] A flowchart illustrating an example of the first information acquisition process. [Figure 7] A flowchart illustrating an example of the second information acquisition process. [Figure 8] A flowchart illustrating an example of the third information acquisition process. [Figure 9] A flowchart illustrating an example of the fourth information acquisition process. [Figure 10] A flowchart illustrating an example of the statistical processing [Figure 11] A flowchart illustrating an example of the learning process. [Figure 12] A flowchart illustrating an example of the first teacher data configuration process. [Figure 13] A flowchart illustrating an example of the second teacher data configuration process. [Figure 14] A flowchart illustrating an example of the third teacher data configuration process. [Figure 15] A flowchart illustrating an example of the score acquisition process [Figure 16] The first questionnaire management table [Figure 17] FIG. 2 shows the second questionnaire management table. [Figure 18] A diagram showing the candidate management table [Figure 19] A diagram showing an example of the output [Figure 20] Overview of the computer system [Figure 21] Block diagram of the computer system DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] Hereinafter, an embodiment of an information processing device and the like will be described with reference to the drawings. Note that, in the embodiments, components with the same reference numerals perform similar operations, and therefore repeated description may be omitted.

[0028] (Embodiment 1) In this embodiment, an information processing device is described that uses a response to a first questionnaire to a member of an organization to output proposed identification information that is information tailored to the response and is information for the growth of the member. Note that the organization may be, for example, a company, a local government, a school, or an individual business entity, but the type of organization is not important.

[0029] In addition, in this embodiment, we will describe an information processing device that outputs proposed identification information using a response to a first questionnaire to a member of an organization and a response to a second questionnaire to a member of another member of the organization.

[0030] In this embodiment, learning information created using two or more pieces of teacher data acquired using the response to the first questionnaire to one member and the proposed identification information is stored, and an information processing device that outputs the proposed identification information using the learning information is described. Note that the learning information is, for example, a learning model, a model possessed by a generative AI, or a correspondence table.

[0031] Furthermore, in this embodiment, learning information created using two or more pieces of teacher data acquired using the answer to the first questionnaire to one member, the answer to the second questionnaire to one member of other members, and the proposed identification information is stored, and an information processing device that outputs the proposed identification information using the learning information is described. Note that the learning information is, for example, a learning model, a model possessed by a generative AI, or a correspondence table.

[0032] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is not important. Information X and information Y may be linked, may exist in the same buffer, information X may be included in information Y, or information Y may be included in information X, etc.

[0033] 1 is a conceptual diagram of an information system A according to the present embodiment. The information system A includes an information processing device 1 and one or two terminal devices 2.

[0034] The information processing device 1 is a device that uses at least the responses to a first questionnaire given to the target members to output information for the growth of the target members. The information processing device 1 is a so-called server device here. The information processing device 1 is, for example, a cloud server or an ASP server, but the type and installation location are not important. The information processing device 1 may also be a standalone device.

[0035] The terminal device 2 is a terminal used by a user. The user is usually a member of an organization. The user is usually a target member from whom the proposed identification information is obtained, or another member who answers the second questionnaire. A member is also a member of an organization. A member is, for example, a company employee, a part-time worker, etc. The member's position or title, etc., does not matter. The terminal device 2 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, a so-called personal computer, etc., and the type does not matter.

[0036] 2 is a block diagram of an information system A according to the present embodiment. The information processing device 1 includes a storage unit 11, a reception unit 12, a processing unit 13, and an output unit .

[0037] The storage unit 11 includes a questionnaire storage unit 111, a candidate management unit 112, an organization information storage unit 113, a response storage unit 114, and a learning information storage unit 115. The reception unit 12 includes an information reception unit 121. The processing unit 13 includes a statistical processing unit 131, a learning unit 132, and an information acquisition unit 133. The output unit 14 includes an information output unit 141.

[0038] The terminal device 2 includes a terminal storage unit 21, a terminal reception unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal reception unit 25, and a terminal output unit .

[0039] Various types of information are stored in the storage unit 11 constituting the information processing device 1. The various types of information include, for example, a questionnaire (to be described later), candidate information (to be described later), organizational information (to be described later), answers (to be described later), and learning information (to be described later).

[0040] The questionnaire storage unit 111 stores one or more questionnaires. The questionnaire storage unit 111 typically stores a first questionnaire and a second questionnaire. Typically, the first questionnaire and the second questionnaire are different questionnaires. However, the first questionnaire and the second questionnaire may be the same questionnaire. If the first questionnaire and the second questionnaire are the same questionnaire, the questionnaire storage unit 111 may store only one questionnaire.

[0041] A questionnaire usually has two or more questions. The questions may be called items, questions, etc. The content of the questions is not important.

[0042] The first questionnaire is a questionnaire about the member himself / herself. It is preferable that the first questionnaire has one or more questions about the member's work. The one or more questions in the first questionnaire include, for example, a question asking about the importance of a work-related item in the work of the member. The one or more questions in the first questionnaire include, for example, a question asking about the behavior or way of thinking of the member.

[0043] The second questionnaire is a questionnaire for one member by other members. In other words, the second questionnaire is a questionnaire that is answered by other members. The second questionnaire is a set of one or more questions for one member. The second questionnaire has, for example, one or more questions related to the work performance of the one member. It is preferable that each of the one or more questions constituting the second questionnaire is a question asking about the expectation or satisfaction with an item related to the work performance of the one member. When asking members about their expectations and satisfaction with an item, the question asking about the expectation and the question asking about the satisfaction with the item may be considered to be separate questions, or one item may be considered to be one question. When one item is considered to be one question, one question is a question asking about both the expectation and the satisfaction.

[0044] Examples of items include, "He / she informs you about changes and trends in the market" and "He / she informs you about plans and methods for achieving the goals of his / her department."

[0045] The degree of expectation is the degree of expectation for a target (e.g., information provision, information gathering, decision-making behavior, support behavior, etc.). The target may also be referred to as an area, an item, etc. Furthermore, it goes without saying that "degree of expectation" may be considered to have the meaning of similar terms such as "importance" or "degree of impact," and may be replaced with similar terms. Furthermore, satisfaction is the degree of satisfaction with a target (e.g., information provision, information gathering, decision-making behavior, support behavior, etc.). It goes without saying that "satisfaction" may be considered to have the meaning of similar terms such as "degree of realization" or "degree of achievement," and may be replaced with similar terms.

[0046] The other members are members different from the first member. The other members and the first member may, for example, be from the same company. The other members may, for example, be the first member's boss, the first member's subordinate, the first member's colleague, or the first member's second superior. A second superior is someone in a higher position in a different department than the first member (for example, the department next door).

[0047] The candidate management unit 112 stores one or more pieces of candidate information. The candidate information is information related to the proposed identification information of a candidate that can be acquired. Hereinafter, the candidate information may simply be referred to as a candidate. A weight may be associated with the candidate information. The weight is the weight of the candidate information, and is information that specifies the amount of correction when calculating the score of the candidate information. The candidate information is, for example, one or more types of information among a proposed identifier, title information, and proposed content information.

[0048] The proposal identification information is information that should be proposed for the growth of the member. The information to be proposed is, for example, information that indicates the growth task of the member. The growth task is a task for growth. The proposal identification information is, for example, a proposal identifier, title information, or proposal content information. The proposal identifier is information that identifies the proposal content information. The proposal identifier is, for example, an ID of the proposal content information. The title information is information that identifies the title of the proposal content information. The title information is usually the title of the proposal content information. The proposal content information is information proposed to the member. The proposal content information is, for example, information having information of a character string, an image, a video, an audio, or two or more of these data types. The data type of the proposal content information is not important.

[0049] One or more pieces of organization information are stored in the organization information storage unit 113. The organization information is information related to an organization. The organization information has, for example, an organization identifier and one or more organization attribute values.

[0050] The organization identifier is information for identifying an organization. For example, the organization identifier is an organization ID or an organization name. For example, the organization name is a company name, a department name, a company name and a department name, or a city, town, or village name.

[0051] The organizational attribute value is an attribute value of an organization. Examples of the organizational attribute value include industry type, size information that identifies the size of the organization, annual ratio information that identifies the annual ratio of the organization, age ratio information that identifies the age ratio of the members of the organization, business content, gender composition information that identifies the gender composition of the organization, settlement month, general meeting timing information that identifies the timing of the general meeting, and business information. Business information is information related to the organization's business. Examples of the business information include sales, profit amount, profit margin, productivity, and growth rate.

[0052] An answer set is stored in the answer storage unit 114. An answer set is a set of one or more member answers. A member answer is an answer to a questionnaire for one member. A member answer is either the member's own answer or another member's answer. A member answer is usually a set of answers to two or more questions.

[0053] The own member response is the response of the target member to the first questionnaire. The own member response is associated with the member identifier of the target member who responded. The own member response may be associated with the own statistical result. The own statistical result is the result of statistical processing of the own member response. Such statistical processing is performed, for example, by the statistical processing unit 131.

[0054] The other member responses are responses to the second questionnaire given to the target member. The other member responses are associated with the member identifier of the target member. The other member responses may be associated with the member identifier of the member who responded. The other member responses may be associated with other statistical results. The other statistical results are the results of statistical processing of one or more other member responses to one target member. Such statistical processing is performed, for example, by the statistical processing unit 131.

[0055] In the answer storage unit 114, for example, one or more sets of a self-member answer corresponding to a member identifier and one or more other member answers are stored. Such a set is a self-member answer given by a target member identified by a member identifier and other member answers of one or more other members to the target member. In addition, such a set is associated with, for example, a proposal history. The proposal history is information about the proposal content information output to the target member after the self-member answer and one or more other member answers are accepted, and is a history of proposals to the target member. The proposal history has, for example, proposal identification information (for example, a proposal identifier) ​​and the date of proposal. The proposal history has, for example, result information. The result information is information about the result of the proposal of the proposal content information to the member. The result information is, for example, a flag or a score. The flag here is information indicating whether the proposal of the proposal content information was good (for example, "1") or not good (for example, "0"). The score here is an evaluation value of the proposal in the proposal content information (for example, on a 10-point scale from "1" to "10").

[0056] The learning information storage unit 115 stores one or more pieces of learning information. The learning information is, for example, information acquired by the learning unit 132 described later. The learning information may be associated with a model condition. In other words, learning information may exist for each model condition. The model condition is a condition for determining the learning information to be used in the prediction process. The model condition is a condition using one or more organization attribute values. An example of the model condition is "industry type=trading company AND size=large company". Note that when the proposed identification information is acquired without using the learning information, the learning information storage unit 115 is not necessary.

[0057] The learning information is information based on two or more teacher data. The learning information is usually information obtained using two or more teacher data. The learning information is, for example, a learning model or a correspondence table. The learning information is, for example, a learning model possessed by a generative AI.

[0058] A learning model is information acquired by performing a machine learning learning process using two or more pieces of teacher data. The learning information is, for example, a model acquired by performing a machine learning learning process using one or more positive examples and one or more negative examples described later. The learning model may be called a learning device, a classifier, or the like. The machine learning algorithm for the learning process and the prediction process described later may be deep learning, random forest, decision tree, SVM, SVR, or the like. In addition, for machine learning, for example, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, fastText, TinySVM, and various existing libraries may be used.

[0059] The correspondence table is information having two or more pieces of correspondence information indicating the correspondence between original data and proposed identification information.

[0060] When using a learning model possessed by the generative AI, the learning information storage unit 115 exists in an external device other than the information processing device 1. The generative AI may also be called a generative AI. The generative AI is usually an AI that generates text. Examples of the generative AI include ChatGPT (registered trademark) and Jasper.

[0061] Teacher data is information that is the basis of learning information. Teacher data has original data. Original data is information that is the basis of teacher data. The original data has, for example, a member's own response or a member's own statistical result. The original data has, for example, one or more other member's responses or other statistical results. The original data has, for example, a member's own response or a member's own statistical result, and one or more other member's responses or other statistical results. The original data may have proposal identification information. The original data may have one or more organization attribute values.

[0062] The teacher data is, for example, any one of the following (1), (2), or (3). (1) When the explanatory variables are the original data and the objective variable is the proposed identification information

[0063] In such a case, the original data has, for example, the member's own response or the member's own statistical result. In such a case, the original data has, for example, one or more other member's responses or other statistical results. In such a case, the original data has, for example, one or more other member's responses or other statistical results and the member's own response or the member's own statistical result. In such a case, the original data does not have proposal identification information. (2) When the explanatory variables are the original data and the objective variable is a flag

[0064] The original data in such a case has, for example, the member's own response or the member's own statistical result, and proposal identification information. The original data in such a case has, for example, one or more other member's responses or other statistical results, and proposal identification information. The original data in such a case has the member's own response or the member's own statistical result, one or more other member's responses or other statistical results, and proposal identification information. The flag, which is the objective variable, can take the value of a first flag (for example, "1") or a second flag (for example, "0"). The first flag is information indicating that there is an effect when output to one member. The second flag is information indicating that there is no effect when output to one member.

[0065] The training data in which the flag, which is the objective variable, is the first flag is a positive example, and the training data in which the flag, which is the objective variable, is the second flag is a negative example. (3) When the explanatory variables are the original data and the objective variable is the score

[0066] In such a case, the original data includes, for example, the member's own answer or the statistical result of the member's own, and the proposal identification information. In such a case, the original data includes, for example, one or more other member's answers or the statistical result of the other, and the proposal identification information. In such a case, the original data includes, for example, the member's own answer or the statistical result of the member's own, and one or more other member's answers or the statistical result of the other, and the proposal identification information. The score, which is the objective variable, is the degree of effectiveness of proposing the proposal content information corresponding to the proposal identification information to one member. The score, which is the objective variable, is, for example, the difference between the evaluation value of the member after proposing the proposal content information corresponding to the proposal identification information to the member and the evaluation value of the member before proposing the proposal content information corresponding to the proposal identification information to the member. The score may be information entered by a person or may be information obtained from the history of the member's own answers. The information obtained from the history of the member's responses is, for example, the difference between a first score obtained from the member's responses after proposing proposal content information corresponding to the proposal identification information to a member, and a second score obtained from the member's responses before proposing proposal content information corresponding to the proposal identification information to a member.

[0067] The reception unit 12 receives various information, instructions, etc. The various information, instructions, etc. are, for example, a response from the member himself, a response from another member, etc.

[0068] The information receiving unit 121, for example, receives a self-member response. The information receiving unit 121, for example, receives one or more other-member responses. The self-member response is usually associated with the member identifier of the target member who is the subject of the questionnaire and is the respondent. The other-member response is, for example, associated with the member identifier of the member who responded. The other-member response is usually associated with the member identifier of the target member who is the subject of the questionnaire. The information receiving unit 121 does not need to simultaneously receive a self-member response associated with the member identifier of one target member and one or more other-member responses.

[0069] Here, acceptance usually means receiving information from the terminal device 2, but it may also be considered as a concept including, for example, the acceptance of information input from an input device such as a keyboard, mouse, or touch panel, and the acceptance of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

[0070] The processing unit 13 performs various types of processing. The various types of processing are, for example, processing performed by a statistical processing unit 131, a learning unit 132, and an information acquisition unit 133.

[0071] The statistical processing unit 131 statistically processes one or more member responses to obtain one or more statistical results. The member responses are the member's own response or other member responses. The statistical results are the member's own statistical result or other statistical results. The statistical results may be information obtained using the member's own statistical result and other statistical results.

[0072] The statistical processing unit 131, for example, statistically processes one other member answer and obtains one or more other statistical results. The statistical processing unit 131, for example, statistically processes two or more other member answers corresponding to the member identifier of the same target member and obtains one or more other statistical results. The other statistical results are, for example, the sum of two or more answers contained in one or more other member answers, a representative value (e.g., average, median) of each of the two or more answers, the sum of one or more answers corresponding to each of two or more areas, and a representative value (e.g., average, median) of one or more answers corresponding to each of two or more areas. An area may be referred to as a category, class, group, etc. An area is usually a set of two or more questions.

[0073] The statistical processing unit 131, for example, statistically processes the own member's answers and obtains one or more own statistical results. The own statistical results are, for example, the sum of two or more answers contained in the own member's answers, a representative value of two or more answers (for example, average value, median), the sum of one or more answers corresponding to each of two or more areas, and a representative value of one or more answers corresponding to each of two or more areas (for example, average value, median). An area may be called a category, class, group, or the like. An area is usually a set of two or more questions.

[0074] The statistical processing algorithm and statistical results performed by the statistical processing unit 131 are not important.

[0075] The learning unit 132 acquires learning information using two or more pieces of teacher data. Below, the case where the learning information is a learning model and the case where the learning information is a correspondence table will be described separately. Note that when the proposed identification information is acquired without using the learning information, the learning unit 132 is not necessary. (1) When the learning information is a learning model (1-1) When the explanatory variables are the original data and the objective variable is the proposed identification information

[0076] In such a case, the original data includes, for example, the member's own response or the member's own statistical result. In such a case, the original data includes, for example, one or more other member's responses or other statistical results. In such a case, the original data includes, for example, the member's own response or the member's own statistical result and one or more other member's responses or other statistical results. In such a case, the original data does not include proposal identification information.

[0077] The learning unit 132 performs a machine learning learning process using the two or more pieces of teacher data to obtain a learning model. The learning model is a model used for multi-value classification. The learning model is used together with the original data of the target members to obtain proposed identification information by a machine learning prediction process. (1-2) When the explanatory variables are the original data and the objective variable is a flag

[0078] In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and proposal identification information. In such a case, the original data includes, for example, one or more other member's answers or other statistical results, and proposal identification information. In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and one or more other member's answers or other statistical results, and proposal identification information.

[0079] The learning unit 132 performs a machine learning learning process using the two or more pieces of teacher data to obtain a learning model. The learning model is a model used for binary classification. The learning model is used together with the original data to obtain a target variable of a flag (for example, "1" or "0") by a machine learning prediction process. (1-3) When the explanatory variable is the original data and the objective variable is the score

[0080] In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and proposal identification information. In such a case, the original data includes, for example, one or more other member's answers or other statistical results, and proposal identification information. In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and one or more other member's answers or other statistical results, and proposal identification information.

[0081] The learning unit 132 performs a machine learning learning process using the two or more pieces of teacher data to obtain a learning model. The learning model is a model used for multi-value classification. The learning model is used together with the original data of the target members to obtain a score by a machine learning prediction process. (2) When the learning information is a correspondence table (2-1) When the explanatory variables are the original data and the objective variable is the proposed identification information

[0082] In such a case, the original data includes, for example, the member's own response or the member's own statistical result. In such a case, the original data includes, for example, one or more other member's responses or other statistical results. In such a case, the original data includes, for example, the member's own response or the member's own statistical result and one or more other member's responses or other statistical results. In such a case, the original data does not include proposal identification information.

[0083] For each of the two or more pieces of teacher data, the learning unit 132 acquires correspondence information having a vector whose elements are two or more pieces of information contained in the original data of the teacher data and the proposed identification information of the teacher data. The learning unit 132 acquires, for example, a correspondence table having the two or more pieces of correspondence information. (2-2) When the explanatory variables are the original data and the objective variable is a flag

[0084] In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and proposal identification information. In such a case, the original data includes, for example, one or more other member's answers or other statistical results, and proposal identification information. In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and one or more other member's answers or other statistical results, and proposal identification information.

[0085] The learning unit 132 acquires, for example, for each of the two or more pieces of teacher data, correspondence information having a vector whose elements are the two or more pieces of information included in the original data of the teacher data and the proposed identification information, and a flag. The learning unit 132 acquires, for example, a correspondence table having the two or more pieces of correspondence information.

[0086] In addition, the learning unit 132 may obtain a correspondence table having a first representative vector which is a representative vector of vectors whose flag corresponds to the first flag, and a second representative vector which is a representative vector of vectors whose flag corresponds to the second flag. (2-3) When the explanatory variable is the original data and the objective variable is the score

[0087] In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and proposal identification information. In such a case, the original data includes, for example, one or more other member's answers or other statistical results, and proposal identification information. In such a case, the original data includes, for example, the member's own answer or the member's own statistical result, and one or more other member's answers or other statistical results, and proposal identification information.

[0088] For each of the two or more pieces of teacher data, the learning unit 132 acquires correspondence information having a vector whose elements are two or more pieces of information contained in the original data of the teacher data and a score of the teacher data. The learning unit 132 acquires, for example, a correspondence table having the two or more pieces of correspondence information.

[0089] The learning unit 132 normally accumulates the acquired learning information in the learning information storage unit 115 .

[0090] The information acquiring unit 133 acquires original data. Next, the information acquiring unit 133 acquires proposed identification information, for example, by using the original data and the learning information in the learning information storage unit 115. Note that the information acquiring unit 133 may acquire proposed identification information without using the learning information.

[0091] The information acquisition unit 133, for example, acquires the own member answer or the own statistical result based on the own member answer accepted by the information acceptance unit 121, and acquires original data using the own member answer or the own statistical result. The information acquisition unit 133, for example, acquires one or more other member answers or other statistical results based on the one or more other member answers accepted by the information acceptance unit 121, and acquires original data using the one or more other member answers or other statistical results. The information acquisition unit 133, for example, acquires the own member answer or the own statistical result based on the own member answer accepted by the information acceptance unit 121, and one or more other member answers or other statistical results based on the one or more other member answers accepted by the information acceptance unit 121, and acquires original data using the own member answer or the own statistical result and the one or more other member answers or other statistical results.

[0092] The original data is information for acquiring proposal identification information that identifies information to be proposed to the target member. The original data is, for example, a vector whose elements are each piece of information used to acquire the original data, but the data structure is not important. The original data is, for example, a vector whose elements are two or more answers included in one or more other member answers and two or more answers included in the own member answer. Such a vector is, for example, (supervisor's answer to question 1, supervisor's answer to question 2,..., subordinate's answer to question 1, subordinate's answer to question 2,..., colleague's answer to question 1, colleague's answer to question 2,..., answer to question 1 included in the own member answer,..., answer to question N included in the own member answer). The original data is, for example, a vector whose elements are one or more other statistical results and one or more own statistical results. Such a vector is, for example, (the average value of the answers to two or more questions corresponding to group 1 included in two or more other members' answers, the average value of the answers to two or more questions corresponding to group 2 included in two or more other members' answers, ..., the average value of the answers to two or more questions corresponding to group 1 included in the own member's answer, ..., the average value of the answers to two or more questions corresponding to group N included in the own member's answer).

[0093] Below, we will explain the cases in which the information acquisition unit 133 (1) acquires proposed identification information through machine learning prediction processing, (2) acquires proposed identification information using a generative AI module, (3) acquires proposed identification information using a correspondence table, and (4) acquires proposed identification information using statistical results without using learning information.

[0094] In any of the cases (1), (2), (3), and (4), the information acquisition unit 133 acquires, for example, its own statistical result, which is the result of statistical processing of its own member response or the own member response received by the information receiving unit 121, and other statistical results, which are the result of statistical processing of one or more other member responses or the one or more other member responses received by the information receiving unit 121, and acquires original data using its own member response or its own statistical result and the one or more other member responses or the other statistical results. In addition, the information acquisition unit 133 may acquire original data using, for example, either (a) its own member response or its own statistical result, and (b) one or more other member responses or the other statistical results ((a) or (b)). (1) When obtaining proposed identification information through machine learning prediction processing (1-1) When the explanatory variables are the original data and the objective variable is the proposed identification information

[0095] The information acquiring unit 133 performs machine learning prediction processing using, for example, the acquired original data and the learning model in the learning information storage unit 115, and acquires proposed identification information that is a target variable. (1-2) When the explanatory variables are the original data and the objective variable is a flag (1-2-1) When not using likelihood (or score)

[0096] The information acquisition unit 133 performs machine learning prediction processing for each of one or more candidate information in the candidate management unit 112 using the original data, the candidate information, and the learning model in the learning information storage unit 115, and acquires a first flag or a second flag.

[0097] Next, the information acquisition unit 133 acquires one or more pieces of candidate information whose acquired flag is the first flag. Each of the one or more pieces of candidate information is proposed identification information. (1-2-2) When using likelihood

[0098] The information acquisition unit 133 performs machine learning prediction processing for each of one or more pieces of candidate information using the original data, the candidate information, and the learning model, and acquires a first flag or a second flag, and a likelihood (score).

[0099] Next, the information acquisition unit 133 acquires one or more pieces of candidate information corresponding to the likelihood of satisfying the good condition. Each of the one or more pieces of candidate information is proposed identification information. The good condition is, for example, that the likelihood is equal to or greater than a threshold, or that the likelihood is in the top N or higher (N is a natural number equal to or greater than 1). (1-3) When the explanatory variable is the original data and the objective variable is the score

[0100] The information acquisition unit 133 performs machine learning prediction processing for each of one or more pieces of candidate information using the original data, the candidate information, and the learning model, and acquires a score.

[0101] Next, the information acquisition unit 133 acquires proposed identification information that is candidate information whose score satisfies a good condition. The good condition is, for example, that the score is equal to or greater than a threshold, or that the score is in the top N or higher (N is a natural number equal to or greater than 1). (2) When obtaining proposed identification information using a generative AI module

[0102] The information acquisition unit 133 provides the original data to the generative AI module and acquires proposal identification information from the generative AI module. Note that the generative AI module does not need to exist in the storage unit 11, and may exist in another device not shown. Also, the proposal identification information here is usually proposal content information. (3) When obtaining proposal identification information using a correspondence table

[0103] The information acquiring unit 133 determines one or more pieces of original data in the correspondence table that satisfy an adoption condition for the original data acquired by the information acquiring unit 133 and that are included in the correspondence information held by the correspondence table, and acquires proposed identification information that pairs with each of the determined one or more pieces of original data from the correspondence table. The adoption condition is, for example, that the similarity is equal to or greater than a threshold, or that the similarity is in the top N or higher (N is a natural number equal to or greater than 1). (4) When obtaining proposed identification information using statistical results without using learning information

[0104] For example, it is assumed here that each question in the first questionnaire is associated with candidate information, and each question in the second questionnaire is also associated with candidate information. (4-1) When using our own statistical results

[0105] The information acquisition unit 133 acquires from the candidate management unit 112, for each of two or more pieces of candidate information, one or more answers that are answers to one or more questions corresponding to the candidate information and are included in the member's own answers. Next, the information acquisition unit 133 performs statistical processing on the acquired one or more answers for each of two or more pieces of candidate information and acquires a score for each piece of candidate information. Next, the information acquisition unit 133 acquires one or more pieces of proposed identification information that are candidate information whose score satisfies the selection condition. Note that the score is, for example, the sum of the acquired one or more answers, or a representative value (for example, average value, median value) of the acquired one or more answers, or a value obtained by adjusting the sum of the acquired one or more answers using the weight of the candidate information. (4-2) When using other statistical results

[0106] The information acquisition unit 133 acquires from the candidate management unit 112, for each of two or more pieces of candidate information, one or more answers that are answers to one or more questions corresponding to the candidate information and are included in one or more other member answers. Next, the information acquisition unit 133 performs statistical processing on the acquired one or more answers for each of two or more pieces of candidate information and acquires a score for each piece of candidate information. Next, the information acquisition unit 133 acquires one or more pieces of proposed identification information that are candidate information whose score satisfies the selection condition. Note that the score is, for example, the sum of the acquired one or more answers, or a representative value (for example, average value, median value) of the acquired one or more answers, or a value obtained by adjusting the sum of the acquired one or more answers using the weight of the candidate information. (4-3) When using your own statistical results and those of other statistical organizations

[0107] The information acquisition unit 133 acquires from the candidate management unit 112, for each of two or more pieces of candidate information, one or more answers that are answers to one or more questions corresponding to the candidate information and are included in the own member answer and one or more other member answers. Next, the information acquisition unit 133 performs statistical processing on the acquired one or more answers for each of two or more pieces of candidate information and acquires a score for each piece of candidate information. Next, the information acquisition unit 133 acquires one or more pieces of proposed identification information that are candidate information whose score satisfies the selection condition. Note that the score is, for example, the sum of the acquired one or more answers, or a representative value (for example, average value, median value) of the acquired one or more answers, or a value obtained by adjusting the sum of the acquired one or more answers using the weight of the candidate information.

[0108] The output unit 14 outputs various types of information. Here, the various types of information are, for example, proposal identification information or proposal content information.

[0109] The information output unit 141 outputs the proposal identification information acquired by the information acquisition unit 133, or the proposal content information corresponding to the proposal identification information. Note that the proposal content information corresponding to the proposal identification information is stored in the candidate management unit 112, for example.

[0110] In this case, output usually means transmission to terminal device 2, but it may also be considered a concept that includes display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, and handing over the processing results to other processing devices or other programs.

[0111] Various types of information are stored in the terminal storage unit 21 constituting the terminal device 2. The various types of information are, for example, member replies. The member replies are, for example, other member replies or the own member replies.

[0112] The terminal reception unit 22 receives various instructions, information, etc. The various instructions, information, etc. are, for example, member responses. The member responses correspond to, for example, an organization identifier and a member identifier.

[0113] Here, acceptance is a concept that includes acceptance of information input from input devices such as a keyboard, mouse, or touch panel, receiving information transmitted via a wired or wireless communication line, and accepting information read from recording media such as an optical disk, magnetic disk, or semiconductor memory.

[0114] The means for inputting various instructions and information may be any means, such as a touch panel, a keyboard, a mouse, or a menu screen.

[0115] The device processing unit 23 performs various types of processing. For example, the various types of processing are processing for configuring information received by the device receiving unit 25 into data to be displayed. For example, the various types of processing are processing for configuring instructions, etc. accepted by the device accepting unit 22 into instructions, etc. to be transmitted.

[0116] The terminal transmitting unit 24 transmits various instructions, information, and the like to the information processing device 1. The various instructions, information, and the like are, for example, instructions configured by the terminal processing unit 23, instructions and information accepted by the terminal accepting unit 22, and the like.

[0117] The terminal receiving unit 25 receives various types of information from the information processing device 1. The various types of information are, for example, a questionnaire and proposal identification information. The questionnaire is, for example, a first questionnaire or a second questionnaire. The proposal identification information here is, for example, proposal content information, title information, or a proposal identifier.

[0118] The terminal output unit 26 acquires various information. The various information is, for example, information accepted by the terminal acceptance unit 22, information received by the terminal reception unit 25, and information configured by the device processing unit 23. The various information is, for example, proposal identification information or proposal content information. Here, output is a concept including display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage in a recording medium, delivery of processing results to other processing devices, other programs, etc.

[0119] The storage unit 11, questionnaire storage unit 111, candidate management unit 112, organizational information storage unit 113, answer storage unit 114, learning information storage unit 115, and terminal storage unit 21 are preferably non-volatile recording media, but can also be realized using volatile recording media.

[0120] There is no restriction on the process by which information is stored in the storage unit 11, etc. For example, information may be stored in the storage unit 11, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 11, etc., or information inputted via an input device may be stored in the storage unit 11, etc.

[0121] It is preferable that the reception unit 12 and the information reception unit 121 are realized by wireless or wired communication means, but they may also be realized by a means for receiving broadcasts, a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

[0122] The processing unit 13, the statistical processing unit 131, the learning unit 132, the information acquisition unit 133, and the device processing unit 23 can usually be realized by a processor, a memory, etc. The processing procedure of the processing unit 13, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type is not important.

[0123] The output unit 14 and the information output unit 141 are usually realized by wireless or wired communication means, but may also be realized by broadcasting means.

[0124] The terminal reception unit 22 can be realized by a device driver for an input means such as a touch panel or a keyboard, or control software for a menu screen.

[0125] The terminal transmitting unit 24 is usually realized by a wireless or wired communication means, but may also be realized by a broadcasting means.

[0126] The terminal receiving unit 25 is usually realized by a wireless or wired communication means, but may be realized by a means for receiving a broadcast.

[0127] The terminal output unit 26 may be considered to include, or may not include, an output device such as a display, a speaker, etc. The terminal output unit 26 may be realized by driver software for an output device, or a combination of driver software for an output device and an output device, etc.

[0128] Next, an example of the operation of the information processing device 1 will be described with reference to the flowchart of FIG.

[0129] (Step S301) Information receiving unit 121 judges whether or not a self-member response has been received. If a self-member response has been received, the process proceeds to step S302, and if a self-member response has not been received, the process proceeds to step S307.

[0130] Here, the information receiving unit 121 normally judges whether or not the own member response has been received from the terminal device 2. Furthermore, before receiving the own member response, the output unit 14 normally transmits the first questionnaire to the terminal device 2. Furthermore, the received own member response is associated with the member identifier of the responding member response.

[0131] (Step S302) The processing unit 13 stores the self member response received in step S301 in the response storage unit 114 in association with the member identifier and today's date.

[0132] (Step S303) The processing unit 13 performs an original data acquisition process. An example of the original data acquisition process will be described with reference to the flowcharts of FIGS.

[0133] The original data acquisition process is a process for acquiring original data that is used to acquire proposed identification information.

[0134] (Step S304) The information acquisition unit 133 performs information acquisition processing. Examples of the information acquisition processing will be described with reference to the flowcharts of Figures 6, 7, 8, and 9. Note that the information acquisition processing is processing for acquiring proposed identification information.

[0135] (Step S305) The information acquiring unit 133 acquires, from the candidate managing unit 112, proposal content information corresponding to the proposal identification information acquired in step S304.

[0136] (Step S306) The information output unit 141 outputs the proposal content information acquired in step S305. The process returns to step S301.

[0137] Here, the information output unit 141 usually transmits the proposal content information acquired in step S305 to the terminal device 2 that transmitted the member's own answer. In other words, it is preferable that the target member can obtain the proposal content information in response to the transmission of the member's own answer (answer to the questionnaire).

[0138] (Step S307) The information receiving unit 121 judges whether or not a response from another member has been received. If a response from another member has been received, the process proceeds to step S308. If a response from the own member has not been received, the process proceeds to step S309. The response from another member corresponds to the member identifier of the target member.

[0139] Here, the information receiving unit 121 normally determines whether or not the answers of other members have been received from the terminal device 2. Furthermore, before receiving the answers of other members, the output unit 14 normally transmits the second questionnaire to the terminal device 2.

[0140] (Step S308) The processing unit 13 associates the other member responses received in step S307 with the member identifier of the target member, and stores them in the response storage unit 114. The process returns to step S301.

[0141] (Step S309) The statistical processing unit 131 judges whether or not it is time for statistical processing. If it is time for statistical processing, the process proceeds to step S310, and if it is not time for statistical processing, the process proceeds to step S314.

[0142] The timing of the statistical processing is, for example, when the processing unit 13 determines that the other member responses to the second questionnaire of one or more other members scheduled for one member have been accepted. The timing of the statistical processing is, for example, when the processing unit 13 determines that the other member responses and the member's own response to the second questionnaire of one or more other members scheduled for one member have been accepted. The timing of the statistical processing is, for example, when the processing unit 13 determines that a predetermined time has arrived.

[0143] (Step S310) The statistical processing unit 131 assigns 1 to the counter i.

[0144] (Step S311) Step S311 determines whether or not there is an i-th member for which statistical processing is to be performed. If there is an i-th member, proceed to step S312, and if there is no i-th member, return to step S301. The i-th member for which statistical processing is to be performed is, for example, a member for whom other member responses to the second questionnaire of one or more other members scheduled to be performed have been accepted. The i-th member for which statistical processing is to be performed is, for example, a member for whom other member responses and one's own member response to the second questionnaire of one or more other members scheduled to be performed have been accepted.

[0145] (Step S312) The statistical processing unit 131 performs statistical processing using one or more other member responses paired with the member identifier of the i-th member. An example of such statistical processing will be described with reference to the flowchart of FIG.

[0146] (Step S313) The statistical processing unit 131 increments the counter i by 1. The process returns to step S311.

[0147] (Step S314) The learning unit 132 judges whether or not to perform the learning process. If it is judged that the learning process is to be performed, the process proceeds to step S315, and if it is judged that the learning process is not to be performed, the process proceeds to step S320. It should be noted that the learning process is judged to be performed when, for example, a learning instruction is accepted or a predetermined time has come.

[0148] (Step S315) The learning unit 132 assigns 1 to a counter i.

[0149] (Step S316) The learning unit 132 judges whether or not the i-th condition exists. If the i-th condition exists, the process proceeds to step S317, and if the i-th condition does not exist, the process returns to step S301.

[0150] The one or more conditions are determined in advance. There may be only one condition, or the condition may be "empty". When the condition is "empty", it means that no condition exists. The conditions here are usually conditions for each piece of learning information. The conditions are conditions for adopting the learning information. The conditions are usually conditions based on one or more organizational attribute values. The conditions here are, for example, model conditions and correspondence table conditions. The model conditions are conditions for adopting a learning model. The correspondence table conditions are conditions for adopting a correspondence table.

[0151] (Step S317) The learning unit 132 performs a learning process and acquires learning information. An example of the learning process will be described with reference to the flowchart of FIG.

[0152] (Step S318) The learning unit 132 accumulates the learning information acquired in step S317 in the learning information storage unit 115 in association with the i-th condition.

[0153] (Step S319) The learning unit 132 increments the counter i by 1. The process returns to step S316.

[0154] (Step S320) The reception unit 12 judges whether or not the information has been received. If the information has been received, the process proceeds to step S321, and if the information has not been received, the process returns to step S301. Note that the information here is, for example, candidate information, organization information, and result information corresponding to the proposal content information presented to the members. The result information is, for example, information indicating whether the proposal content information presented to the members was good, and an evaluation value or score of the proposal content information presented to the members. Note that here, the reception unit 12 receives information from, for example, the terminal device 2.

[0155] (Step S321) Processing unit 13 accumulates in storage unit 11 the information accepted in step S320.

[0156] In the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt occurs to end the process.

[0157] Next, an example of the first raw data acquisition process in step S303 will be described with reference to the flowchart in FIG.

[0158] (Step S401) The information acquisition unit 133 acquires a member identifier of a target member. The information acquisition unit 133 acquires one or more other member responses that are paired with the member identifier from the response storage unit 114. The target member is a member to which the proposal content information is to be output.

[0159] (Step S402) The information acquisition unit 133 acquires the target member's own member response.

[0160] (Step S403) When acquiring the original data, the information acquisition unit 133 judges whether or not to use the organization attribute value. If the organization attribute value is to be used, the process proceeds to step S404, and if the organization attribute value is not to be used, the process proceeds to step S406. Note that whether or not to use the organization attribute value is usually determined in advance.

[0161] (Step S404) The information acquiring unit 133 acquires an organization identifier that pairs with the member identifier of the target member.

[0162] (Step S405) The information acquiring unit 133 acquires, from the organization information storage unit 113, one or more organization attribute values ​​that are paired with the organization identifier acquired in step S404 and that are to be used when acquiring the original data. Note that the organization attribute values ​​to be used are usually determined in advance.

[0163] (Step S406) The information acquisition unit 133 uses one or more other member responses and the own member response, or one or more other member responses and the own member response and one or more organization attribute values ​​to configure original data, and returns to the upper level process.

[0164] The original data is, for example, a vector whose elements are one or more answers included in one or more other member answers and one or more answers included in the own member answer. The original data is, for example, a vector whose elements are one or more answers included in one or more other member answers and one or more answers included in the own member answer and one or more organization attribute values.

[0165] In the flowchart of FIG. 4, the information acquisition unit 133 may acquire the original data by using either the self member response or one or more other member responses.

[0166] Next, an example of the second raw data acquisition process in step S303 will be described with reference to the flowchart in FIG.

[0167] (Step S501) The information acquiring unit 133 acquires the member identifier of the target member. The information acquiring unit 133 acquires, from the response storage unit 114, one or more other statistical results paired with the member identifier.

[0168] (Step S502) Information acquisition unit 133 acquires the own member response of the target member. Next, statistical processing unit 131 performs statistical processing on the own member response and acquires one or more own statistical results. Note that if one or more own statistical results already exist, information acquisition unit 133 acquires one or more own statistical results that are paired with the member identifier of the target member from response storage unit 114.

[0169] (Step S503) The information acquisition unit 133 judges whether or not to use an organization attribute value when acquiring the original data. If the organization attribute value is to be used, the process proceeds to step S504. If the organization attribute value is not to be used, the process proceeds to step S506.

[0170] (Step S504) The information acquiring unit 133 acquires an organization identifier that pairs with the member identifier of the target member.

[0171] (Step S505) The information acquiring unit 133 acquires, from the organization information storage unit 113, one or more organization attribute values ​​that are paired with the organization identifier acquired in step S504 and that are used when acquiring the original data.

[0172] (Step S506) The information acquiring unit 133 configures original data using one or more other statistical results and one or more of its own statistical results, or one or more other statistical results, one or more of its own statistical results, and one or more organization attribute values, and returns to the upper level process.

[0173] The original data is, for example, a vector whose elements are one or more other statistical results and one or more individual statistical results. The original data is, for example, a vector whose elements are one or more other statistical results and one or more individual statistical results and one or more organizational attribute values.

[0174] In addition, in the flowcharts of Figures 4 and 5, the information acquisition unit 133 may construct original data using one or more types of information from one or more other member responses, one or more own member responses, one or more organizational attribute values, one or more other statistical results, and one or more own statistical results.

[0175] Next, an example of the first information acquisition process in step S304 will be described with reference to the flowchart in FIG.

[0176] (Step S601) The information acquiring unit 133 acquires a learning model to be used for information acquisition processing from the learning information storage unit 115.

[0177] The information acquiring unit 133 acquires, for example, a learning model that is paired with a condition that one or more organizational attribute values ​​corresponding to the member identifier of the target member match from the learning information storage unit 115. The learning model acquired here is used together with original data that does not include proposed identification information, and is a model for acquiring proposed identification information.

[0178] (Step S602) The information acquisition unit 133 provides the original data acquired in step S304 and the learning model acquired in step S601 to a prediction module that performs machine learning prediction processing, and executes the prediction module.

[0179] (Step S603) The information acquisition unit 133 acquires the proposal identification information returned from the prediction module in step S602.

[0180] (Step S604) The information acquisition unit 133 acquires proposal content information corresponding to the proposal identification information acquired in step S603 from the candidate management unit 112. Then, the process returns to the upper level process.

[0181] Next, an example of the second information acquisition process in step S304 will be described with reference to the flowchart in FIG.

[0182] (Step S701) The information acquiring unit 133 acquires a learning model to be used for information acquisition processing from the learning information storage unit 115.

[0183] The information acquiring unit 133 acquires, for example, a learning model that is paired with a condition that one or more organizational attribute values ​​corresponding to the member identifier of the target member match from the learning information storage unit 115. The learning model acquired here is used together with the original data including the proposed identification information, and is a model for acquiring a flag (first flag or second flag) and a score (likelihood), and is a model for performing binary classification.

[0184] (Step S702) The information acquiring unit 133 assigns 1 to a counter i.

[0185] (Step S703) Information acquisition unit 133 judges whether or not the i-th candidate exists in candidate management unit 112. If the i-th candidate exists, the process proceeds to step S704, and if not, the process proceeds to step S708.

[0186] (Step S704) The information acquiring unit 133 configures an input vector, which is a vector obtained by adding proposed identification information that is the i-th candidate to the original data acquired in step S304.

[0187] (Step S705) The information acquiring unit 133 provides the input vector acquired in step S704 and the learning model acquired in step S701 to a prediction module that performs machine learning prediction processing, and executes the prediction module.

[0188] (Step S706) The information acquiring unit 133 acquires the flag and the score, which are the execution results in step S705.

[0189] (Step S707) The information acquiring unit 133 increments the counter i by 1. The process returns to step S703.

[0190] (Step S708) The information acquisition unit 133 acquires one or more candidates whose flag value is "flag 1" and that are paired with a score that satisfies a good condition. Note that the good condition is, for example, a score equal to or greater than a threshold, or a score in the top N or higher (N is a natural number equal to or greater than 1).

[0191] (Step S709) The information acquisition unit 133 acquires proposal content information corresponding to each of the one or more candidates (proposal identification information) acquired in step S708 from the candidate management unit 112. The process returns to the upper level process.

[0192] Next, an example of the third information acquisition process in step S304 will be described with reference to the flowchart in FIG.

[0193] (Step S801) The information acquiring unit 133 acquires a learning model to be used for information acquisition processing from the learning information storage unit 115.

[0194] The information acquiring unit 133 acquires, for example, a learning model that is paired with a condition that one or more organizational attribute values ​​corresponding to the member identifier of the target member match from the learning information storage unit 115. The learning model acquired here is used together with the original data including the proposed identification information, and is a model for acquiring a score and for performing multi-value classification.

[0195] (Step S802) The information acquiring unit 133 assigns 1 to a counter i.

[0196] (Step S803) Information acquisition unit 133 judges whether or not the i-th candidate exists in candidate management unit 112. If the i-th candidate exists, the process proceeds to step S804, and if not, the process proceeds to step S808.

[0197] (Step S804) The information acquiring unit 133 configures an input vector, which is a vector obtained by adding proposed identification information that is the i-th candidate to the original data acquired in step S304.

[0198] (Step S805) The information acquiring unit 133 provides the input vector acquired in step S804 and the learning model acquired in step S801 to a prediction module that performs machine learning prediction processing, and executes the prediction module.

[0199] (Step S806) The information acquiring unit 133 acquires the score which is the execution result in step S805.

[0200] (Step S807) The information acquiring unit 133 increments the counter i by 1. The process returns to step S803.

[0201] (Step S808) The information acquisition unit 133 acquires one or more candidates that are paired with a score that satisfies a good condition. Note that the good condition is, for example, that the score is equal to or greater than a threshold, or that the score is in the top N or higher (N is a natural number equal to or greater than 1).

[0202] (Step S809) The information acquisition unit 133 acquires proposal content information corresponding to each of the one or more candidates (proposal identification information) acquired in step S808 from the candidate management unit 112. The process returns to the upper level process.

[0203] Next, an example of the fourth information acquisition process in step S304 will be described with reference to the flowchart in FIG.

[0204] (Step S901) The information acquiring unit 133 assigns 1 to a counter i.

[0205] (Step S902) The information acquisition unit 133 judges whether or not the i-th correspondence information exists in the correspondence table that is the learning information. If the i-th correspondence information exists, the process proceeds to step S903, and if the i-th correspondence information does not exist, the process proceeds to step S905.

[0206] (Step S903) The information acquiring unit 133 acquires the similarity between the vector included in the i-th corresponding information and the original data (here, the vector).

[0207] (Step S904) The information acquiring unit 133 increments the counter i by 1. The process returns to step S902.

[0208] (Step S905) If the similarity acquired in step S902 satisfies the good condition, the information acquiring unit 133 acquires, from the correspondence table, one or more pieces of proposed identification information paired with the vector corresponding to the similarity that satisfies the good condition.

[0209] (Step S906) The information acquisition unit 133 acquires proposal content information corresponding to each of the one or more pieces of proposal identification information acquired in step S905 from the candidate management unit 112. Then, the process returns to the upper level process.

[0210] Next, an example of the statistical processing in step S312 will be described with reference to the flowchart in FIG.

[0211] (Step S1001) The statistical processing unit 131 acquires the member identifier of the target member.

[0212] (Step S1002) The statistical processing unit 131 acquires from the response storage unit 114 one or more other member responses that are paired with the member identifier acquired in step S1001.

[0213] (Step S1003) The statistical processing unit 131 acquires from the response storage unit 114 the own member response that is paired with the member identifier acquired in step S1001.

[0214] (Step S1004) The statistical processing unit 131 assigns 1 to the counter i.

[0215] (Step S1005) The statistical processing unit 131 judges whether or not to calculate the i-th statistical value. If the i-th statistical value is to be calculated, the process proceeds to step S1006, and if the i-th statistical value is not to be calculated, the process proceeds to step S1009. The statistical value to be calculated is predetermined. The i-th statistical value is, for example, one or more answers corresponding to each area for each region, and is the sum of the answers included in the own member answers. The i-th statistical value is, for example, one or more answers corresponding to each area for each region, and is the sum of the answers included in one or more other member answers.

[0216] (Step S1006) The statistical processing unit 131 acquires one or more pieces of information used when calculating the i-th statistical value.

[0217] (Step S1007) The statistical processing unit 131 calculates the i-th statistical value by using one or more pieces of information acquired in step S1006.

[0218] (Step S1008) The statistical processing unit 131 increments the counter i by 1. The process returns to step S1005.

[0219] (Step S1009) The statistical processing unit 131 uses the one or more statistical values ​​calculated in step S1007 to generate one or more statistical results to be accumulated. The statistical results may be the statistical values ​​as they are. The one or more statistical results to be accumulated are, for example, the sum of one or more answers in the own member answers corresponding to each area for each area and answers included in one or more other member answers.

[0220] (Step S1010) The statistical processing unit 131 associates one or more statistical results constructed in step S1009 with the member identifier acquired in step S1001, and accumulates them in the response storage unit 114. The process returns to the upper level process.

[0221] Next, an example of the learning process in step S317 will be described with reference to the flowchart in FIG.

[0222] (Step S1101) The learning unit 132 acquires one or more member identifiers for information for acquiring teacher data.

[0223] (Step S1102) The learning unit 132 assigns 1 to a counter i.

[0224] (Step S1103) The learning unit 132 judges whether or not the i-th member identifier exists among the member identifiers acquired in step S1101. If the i-th member identifier exists, the process proceeds to step S1104, and if the i-th member identifier does not exist, the process proceeds to step S1109.

[0225] (Step S1104) The learning unit 132 assigns 1 to a counter j.

[0226] (Step S1105) The learning unit 132 judges whether or not there exists a j-th proposed identification information corresponding to the i-th member identifier and which has been previously output to the member. If the j-th proposed identification information exists, the process proceeds to step S1106, and if the j-th proposed identification information does not exist, the process proceeds to step S1108.

[0227] (Step S1106) The learning unit 132 composes teacher data using the j-th proposed identification information corresponding to the i-th member identifier. An example of such teacher data composition processing will be described with reference to the flowcharts of Figs. 12, 13, and 14.

[0228] (Step S1107) The learning unit 132 increments the counter j by 1. The process returns to step S1105.

[0229] (Step S1108) The learning unit 132 increments the counter i by 1. The process returns to step S1103.

[0230] (Step S1109) The learning unit 132 acquires learning information using the two or more pieces of teacher data acquired in step S1106. Note that the learning information is, for example, a learning model or a correspondence table.

[0231] The learning unit 132, for example, provides the two or more pieces of teacher data acquired in step S1106 to a machine learning learning module, executes the learning module, and acquires a learning model.

[0232] The learning unit 132 creates a correspondence table in which the two or more pieces of teacher data acquired in step S1106 are used as correspondence information, for example.

[0233] (Step S1110) The learning unit 132 accumulates the learning information acquired in step S1109 in the learning information storage unit 115.

[0234] Next, an example of the first teacher data configuration process in step S1106 will be described with reference to the flowchart in FIG.

[0235] (Step S1201) The learning unit 132 obtains result information that indicates the result of outputting the proposal content information corresponding to the jth proposal identification information in step S1105 to the target member, and that pairs the i-th member identifier and the j-th proposal identification information, from the proposal history in the response storage unit 114.

[0236] (Step S1202) The learning unit 132 judges whether the result information acquired in step S1201 satisfies the good condition. If the good condition is satisfied, the process proceeds to step S1203, and if the good condition is not satisfied, the process returns to the upper level process. Note that the good condition is, for example, that the result information indicates that the proposal of the proposal content information is good (for example, "1"), or that the evaluation value is equal to or greater than the threshold value.

[0237] (Step S1203) The information acquisition unit 133 performs an original data acquisition process using one or more other member responses and the own member response that are paired with the i-th member identifier and the j-th proposed identification information. An example of the original data acquisition process has been described using the flowcharts of FIG. 4 and FIG. 5.

[0238] (Step S1204) The learning unit 132 acquires the j-th proposed identification information of step S1105.

[0239] (Step S1205) The learning unit 132 generates teacher data using the original data acquired in step S1203 and the j-th proposed identification information acquired in step S1204, and returns to the upper level process.

[0240] The training data here is, for example, information used in a machine learning learning process with the original data as an explanatory variable and the j-th proposed identification information as a target variable. The training data is, for example, correspondence information indicating the correspondence between the original data and the j-th proposed identification information.

[0241] Next, an example of the second teacher data configuration process in step S1106 will be described with reference to the flowchart in FIG.

[0242] (Step S1301) The learning unit 132 obtains a flag from the proposal history in the answer storage unit 114, which is result information indicating the result of outputting the proposal content information corresponding to the jth proposal identification information in step S1105 to the target member, and which is result information paired with the ith member identifier and the jth proposal identification information.

[0243] (Step S1302) The information acquisition unit 133 performs an original data acquisition process using one or more other member responses and its own member responses that are paired with the i-th member identifier and the j-th proposed identification information. An example of the original data acquisition process has been described using the flowcharts of FIG. 4 and FIG. 5.

[0244] (Step S1303) The learning unit 132 configures teacher data using the flag acquired in step S1301, the original data acquired in step S1302, and the j-th proposed identification information acquired in step S1204, and then returns to the upper level process.

[0245] The training data here is, for example, data with a flag as a response variable and original data and the j-th proposed identification information as explanatory variables, and is information used in machine learning learning processing. The training data here is, for example, correspondence information indicating the correspondence between a flag and a pair of original data and the j-th proposed identification information.

[0246] Next, an example of the third teacher data configuration process in step S1106 will be described with reference to the flowchart in FIG.

[0247] (Step S1401) The statistical processing unit 131 acquires a score. An example of such score acquisition processing will be described with reference to the flowchart in FIG.

[0248] (Step S1402) The information acquisition unit 133 performs an original data acquisition process using one or more other member responses and its own member responses that are paired with the i-th member identifier and the j-th proposed identification information. An example of the original data acquisition process has been described using the flowcharts of FIG. 4 and FIG. 5.

[0249] (Step S1403) The learning unit 132 generates teacher data using the score acquired in step S1301, the original data acquired in step S1302, and the j-th proposed identification information acquired in step S1204, and returns to the upper level process.

[0250] The training data here is, for example, data with the score as the objective variable and the original data and the j-th proposed identification information as explanatory variables, and is information used in machine learning learning processing. The training data here is, for example, correspondence information indicating the correspondence between the score and a pair of the original data and the j-th proposed identification information.

[0251] Next, an example of the score acquisition process in step S1401 will be described with reference to the flowchart in FIG.

[0252] (Step S1501) The statistical processing unit 131 acquires the self member's response after the proposal from the response storage unit 114.

[0253] (Step S1502) The statistical processing unit 131 obtains a score using one or more answers contained in the member's own answer after the proposal. This score is the post-proposal score. The post-proposal score is the member's score based on the member's own answer after the proposal content information is presented to the member. The post-proposal score is, for example, the result of statistical processing (e.g., sum, average, median) of each answer contained in the member's own answer after the proposal content information is presented to the member.

[0254] (Step S1503) The statistical processing unit 131 acquires the member's own response before the proposal from the response storage unit 114.

[0255] (Step S1504) The statistical processing unit 131 obtains a score using one or more answers contained in the member's own answer before the proposal. This score is the pre-proposal score. The pre-proposal score is the member's score based on the member's own answer before the proposal content information is presented to the member. The pre-proposal score is, for example, the result of statistical processing (e.g., sum, average, median) of each answer contained in the member's own answer before the proposal content information is presented to the member.

[0256] (Step S1505) The statistical processing unit 131 acquires a score that is information about the difference between the post-proposal score and the pre-proposal score, and returns to the upper level process.

[0257] Such a score can be said to be the degree to which the member has improved as a result of the proposal content information being presented to the member. Moreover, such a score is, for example, "post-proposal score - pre-proposal score."

[0258] In the flowchart of Fig. 15, the statistical processing unit 131 obtains the member's score using only the member's own response. However, the statistical processing unit 131 may obtain the member's score using only one or more other member responses, or may obtain the member's score using the member's own response and one or more other member responses. The score using one or more other member responses is, for example, the result of statistical processing (for example, the sum, average, median) of the responses contained in one or more other member responses.

[0259] A specific example of the operation of the information system A in this embodiment will be described below. In the following table, "ID" is information for identifying a record.

[0260] The questionnaire storage unit 111 stores a first questionnaire management table shown in FIG. 16. The first questionnaire management table is a table for managing a first questionnaire in which a member answers questions directed to him / herself. The first questionnaire management table manages two or more records having an "ID", "question", and "growth task ID". The "growth task ID" indicates the growth task ID to which the question corresponds. The "growth task ID" is an example of a proposal identifier. The answer to the question is, for example, a four-level scale (any number from 1 to 4), but it may also be ten levels, five levels, three levels, etc., and is not important. The questions in the first questionnaire include, for example, a question asking about the member's attitude toward work.

[0261] The questionnaire storage unit 111 stores the second questionnaire management table shown in FIG. 17. The second questionnaire management table is a table for managing a second questionnaire in which other members answer questions for one member. The second questionnaire management table manages two or more records having "area", "item No.", "question No.", "expectation question", "question No.", "satisfaction question", and "growth task ID". "Area" indicates the category of items and questions. "Item No." is an identifier of the item. The item is the subject of the question here. An item may have two questions, one asking about expectation and the other asking about satisfaction. An item may also be a meta question. A meta question is a question about a large item, and is a question that asks only about satisfaction. "Question No." is an identifier of the question. "Question No." may correspond to an expectation question or may correspond to an expectation question. "Expectation question" is a question that asks about expectations for an item. The "Satisfaction Question" is a question that asks about the satisfaction with an item. The "Growth Task ID" indicates the growth task ID that corresponds to the item or question. The growth task ID is an identifier for the growth task. The growth task indicates a task for the target member to grow. The growth task is information proposed to the target member. The growth task ID is an example of a proposal identifier. In addition, the answers to the expectation questions and the answers to the satisfaction questions are, for example, on a five-point scale (any number from 1 to 5), but they can also be on a ten-point scale, three-point scale, etc., and are not important. For example, the answer to the expectation question indicates a higher expectation. For example, the answer to the satisfaction question indicates a higher satisfaction as the number is higher.

[0262] The candidate management unit 112 stores a candidate management table shown in FIG. 18. The candidate management table is a table that manages two or more pieces of candidate information. The candidate management table manages two or more records having "growth task ID", "title information", "proposal content information", and "weight". "Title information" is the title of the growth task. "Proposal content information" is information corresponding to the growth task, and is information presented to the target member for growth. "Proposal content information" is a file name here. It is assumed that the entity of the file indicated by the "proposal content information" also exists in the candidate management unit 112. "Weight" is the weight of the growth task. The weight of the growth task is used when calculating the score of the growth task. For example, the score of the growth task is calculated by "growth task score = total of answers to questions in the first questionnaire corresponding to the growth task / growth task weight". Also, for example, the score of the growth task is calculated by "growth task score = (total of answers to questions in one or more second questionnaires corresponding to the growth task + total of answers to questions in the first questionnaire corresponding to the growth task) / growth task weight".

[0263] It is assumed that one or more other member replies are stored for each of one or more target members in the reply storage unit 114. It is assumed that the reply storage unit 114 stores, in pairs with the "member identifier=U1101" of target member A, an other member reply given by member A's superior and an other member reply given by member A's subordinate.

[0264] In this situation, member A (member identifier=U1101) answers the first questionnaire, and his / her own member response is transmitted from member A's terminal device 2 to the information processing device 1. In the above situation, the following two specific examples will be explained. Specific example 1 is a case where a growth challenge is presented to member A using his / her own member response without using the responses of other members. Specific example 2 is a case where a growth challenge is presented to member A using the responses of two other members and his / her own member response.

[0265] (Example 1) The information receiving unit 121 of the information processing device 1 receives the member's own response associated with the member identifier "U1101" from the terminal device 2 of member A. Next, the processing unit 13 stores the received member's own response in the response storage unit 114 in association with the member identifier "U1101" and today's date.

[0266] Next, the processing unit 13 performs information acquisition processing as follows. That is, the statistical processing unit 131 constituting the processing unit 13 acquires the member's own answers of the target member. Next, the statistical processing unit 131 acquires answers to one or more questions paired with the growth task ID for each growth task ID, and adds up the one or more answers to acquire a total value for each growth task ID. Next, the statistical processing unit 131 divides the total value for each growth task ID by the weight of the growth task to calculate a score for each growth task ID.

[0267] Next, the information acquisition unit 133 acquires the growth task IDs corresponding to the top two scores of the growth task ID. It is assumed that the acquired growth task ID is (3,1). Next, the information acquisition unit 133 acquires the proposal content information "file3" that is paired with the growth task ID "3" from the candidate management table (FIG. 18). Next, the information acquisition unit 133 acquires the file "file3" from the candidate management unit 112. Also, the information acquisition unit 133 acquires the proposal content information "file1" that is paired with the growth task ID "1" from the candidate management table (FIG. 18). Next, the information acquisition unit 133 acquires the file "file1" from the candidate management unit 112. Next, the information acquisition unit 133 synthesizes the contents of the two files to compose the proposal content information to be output. Next, it transmits the output to the terminal device 2 of member A.

[0268] Next, the terminal device 2 of member A receives and outputs the proposed content information. An example of such output is shown in Fig. 19. In Fig. 19, <Growth task 1> is the proposed content information paired with the growth task ID "3", and <Growth task 2> is the proposed content information paired with the growth task ID "1".

[0269] (Example 2) The information receiving unit 121 of the information processing device 1 receives the member's own response associated with the member identifier "U1101" from the terminal device 2 of member A. Next, the processing unit 13 stores the received member's own response in the response storage unit 114 in association with the member identifier "U1101" and today's date.

[0270] Next, the processing unit 13 performs information acquisition processing as follows. That is, the statistical processing unit 131 constituting the processing unit 13 acquires the self-member response of the target member. The statistical processing unit 131 also acquires two other member responses that are paired with the member identifier "U1101" of the target member from the response storage unit 114. The two other member responses are the other member response of member A's superior and the other member response of his subordinate.

[0271] Next, the statistical processing unit 131 refers to the self-member's answers for each growth task ID, and adds up the answers to one or more questions corresponding to the growth task ID to obtain a total value for each growth task ID. The statistical processing unit 131 also refers to two or more other member's answers for each growth task ID, and adds up the answers to one or more questions corresponding to the growth task ID to obtain a total value for each growth task ID. Next, the statistical processing unit 131 adds up the total value for the self-member's answers and the total value for the other member's answers for each growth task ID to calculate a final total value. Next, the statistical processing unit 131 divides the final total value for each growth task ID by the weight of the growth task to calculate a score for each growth task ID.

[0272] Next, it is assumed that the information acquisition unit 133 acquires the growth task ID (3,1) corresponding to the top two growth task scores. Next, the information acquisition unit 133 acquires the proposal content information "file3" that pairs with the growth task ID "3" from the candidate management table (FIG. 18). Next, the information acquisition unit 133 acquires the file "file3" from the candidate management unit 112. Also, the information acquisition unit 133 acquires the proposal content information "file1" that pairs with the growth task ID "1" from the candidate management table (FIG. 18). Next, the information acquisition unit 133 acquires the file "file1" from the candidate management unit 112. Next, the information acquisition unit 133 combines the contents of the two files to form the proposal content information to be output. Next, it transmits the output to the terminal device 2 of member A.

[0273] Next, the terminal device 2 of member A receives and outputs the proposed content information. An example of such output is shown in Fig. 19. In Fig. 19, <Growth task 1> is the proposed content information paired with the growth task ID "3", and <Growth task 2> is the proposed content information paired with the growth task ID "1".

[0274] As described above, according to this embodiment, it is possible to appropriately suggest information for the growth of members of an organization.

[0275] In particular, according to this embodiment, the member's own response can be used to appropriately suggest information for the growth of the members of the organization.

[0276] Furthermore, according to this embodiment, it is possible to appropriately suggest information for the growth of members of an organization by using the responses of other members as well.

[0277] Furthermore, according to this embodiment, it is possible to use the learning information to appropriately suggest information for the growth of members of an organization.

[0278] Furthermore, according to this embodiment, it is possible to appropriately suggest information for the growth of members of an organization through machine learning processing.

[0279] Furthermore, according to this embodiment, the generative AI can appropriately suggest information for the growth of members of an organization.

[0280] Furthermore, according to this embodiment, the correspondence table can appropriately suggest information for the growth of members of an organization.

[0281] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software for realizing the information processing device 1 in this embodiment is the following program. That is, this program causes a computer to function as an information receiving unit that receives a self-member response, which is a response to a first questionnaire that is a questionnaire about the target member, an information acquiring unit that acquires the self-member response or the self-statistical result that is the result of statistical processing of the self-member response or the self-member response received by the information receiving unit, and acquires proposal identification information that identifies information to be proposed for the growth of the target member using the self-member response or the self-statistical result, and an information output unit that outputs the proposal identification information or proposal content information corresponding to the proposal identification information.

[0282] Fig. 20 shows the appearance of a computer that executes the program described in this specification to realize the information processing device 1 and the like of the various embodiments described above. The above-mentioned embodiments can be realized by computer hardware and a computer program executed thereon. Fig. 20 is an overview of this computer system 300, and Fig. 21 is a block diagram of the system 300.

[0283] In FIG. 20, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0284] 21, computer 301 includes, in addition to CD-ROM drive 3012, MPU 3013, bus 3014 connected to CD-ROM drive 3012 etc., ROM 3015 for storing programs such as a boot-up program, RAM 3016 connected to MPU 3013 for temporarily storing instructions of application programs and providing temporary storage space, and hard disk 3017 for storing application programs, system programs, and data. Although not shown here, computer 301 may further include a network card for providing connection to a LAN.

[0285] A program for causing computer system 300 to execute the functions of information processing device 1 and the like of the above-mentioned embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and further transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 at the time of execution. The program may be loaded directly from CD-ROM 3101 or the network.

[0286] The program does not necessarily include an operating system (OS) or a third party program that causes the computer 301 to execute the functions of the information processing device 1 of the above-mentioned embodiment. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner to obtain a desired result. How the computer system 300 operates is well known, and a detailed description will be omitted.

[0287] In addition, in the above program, the steps of transmitting information and receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmitting step (processing that is performed only by hardware).

[0288] The program may be executed by a single computer or a plurality of computers, that is, centralized processing or distributed processing may be performed.

[0289] Furthermore, in each of the above embodiments, it goes without saying that two or more communication means present in one device may be physically realized by one medium.

[0290] Furthermore, in each of the above embodiments, each process may be realized by centralized processing in a single device, or may be realized by distributed processing in a plurality of devices.

[0291] The present invention is not limited to the above-described embodiment, and various modifications are possible, and it goes without saying that these modifications are also included within the scope of the present invention. [Industrial Applicability]

[0292] As described above, the information processing device 1 according to the present invention has an effect of being able to appropriately suggest information for the growth of members of an organization, and is useful as a server or the like that suggests information for the growth of members of an organization. [Explanation of symbols]

[0293] A. Information Systems 1. Information processing device 2 Terminal Equipment 2 Top 11 Storage area 12 Reception 13 Processing section 14 Output section 21 Terminal storage section 22 Terminal Reception 23 Terminal Processing Section 24 Terminal transmitter 25 Terminal receiving section 26 Terminal Output Section 111 Questionnaire storage section 112 Candidate Management Department 113 Organization Information Storage Section 114 Answer Storage Unit 115 Learning information storage unit 121 Information Reception Department 131 Statistical Processing Unit 132 Learning Department 133 Information Acquisition Department 141 Information output section

Claims

1. The Information Reception Department accepts responses from the target members, which are the responses to the first questionnaire about themselves, An information acquisition unit acquires the member responses received by the information receiving unit or the statistical results obtained from statistical processing of said member responses, and uses said member responses or statistical results to acquire proposal identification information that identifies information to be proposed for the growth of the target member, The system comprises an information output unit that outputs the aforementioned proposal identification information or proposal content information corresponding to the aforementioned proposal identification information, The aforementioned information acquisition unit, An information processing device that obtains raw data for obtaining proposal identification information that identifies information to be proposed to the target member, using the member's responses or the statistical results, provides the raw data to a generating AI, and obtains the proposal identification information from the generating AI.

2. The generated AI is The information processing device according to claim 1, which is a generating AI given two or more training data sets, each containing a self-member response, which is the response of one member to a first questionnaire about themselves, or a self-statistical result, which is the result of statistical processing of said self-member response, and suggested identification information for the growth of the said member.

3. The aforementioned information receiving unit is We also accept responses from one or more other members, which are responses to the second questionnaire, which is a questionnaire given by other members to the aforementioned target member. The aforementioned information acquisition unit, The information processing apparatus according to claim 1, which obtains the self-member responses or self-statistical results received by the information receiving unit, and the one or more other member responses or other statistical results which are the result of statistical processing of the one or more other member responses received by the information receiving unit, and uses the self-member responses or self-statistical results and the one or more other member responses or other statistical results to obtain the proposal identification information.

4. The system further comprises a candidate management unit that stores two or more candidate information, which are proposed identification information for the acquired candidates. The two or more training data mentioned above are The training data has a score corresponding to the proposed identification information, The aforementioned information acquisition unit, The information processing apparatus according to claim 2, wherein the two or more candidate information items and the original data are provided to a module of a generation AI, and the proposed identification information is obtained from the module of the generation AI.

5. An information processing method that causes a computer to perform all the processing performed by the information processing device described in any one of Claims 1 to 4.

6. Computers, A program for causing an information processing device to function as described in any one of claims 1 to 4.

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