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 employees in improving their job satisfaction (see Patent Document 1). [Prior art documents] [Patent documents]

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

[0004] However, the prior art has not been able to adequately suggest information for the growth of members of an organization. [Means for solving the problem]

[0005] The information processing device of the first invention is an information processing device that includes an information receiving unit that receives the self-member responses, which are responses to a first questionnaire that is a questionnaire about the target member himself / herself, an information acquisition unit that acquires the self-member responses or self-statistical results that are the result of statistical processing of the self-member responses received by the information receiving unit, and uses the self-member responses or self-statistical results to acquire proposal identification information 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, it is possible to appropriately suggest information for the growth of members of an organization using the member's own response.

[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 target members, and the information acquisition unit acquires the own member response or own statistical result received by the information receiving unit and 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 comprises 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 or herself, or a self-statistical result, which is the result of statistical processing of the member's own response, and proposal identification information, which identifies information to be proposed for the growth of the member, is stored; and the information acquisition unit uses the member's own response or the self-statistical result to acquire original data for acquiring proposal identification information, which identifies information to be proposed to the target member, 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 obtained by performing a machine learning learning process using two or more teacher data, and the information acquisition unit performs a machine learning prediction process using the original data and the learning model to acquire 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 for 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 the 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 one or more positive examples and 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 for which the 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 for the acquired candidates, is stored, the two or more teacher data have scores 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 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 is an information processing device that acquires 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] Furthermore, 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 in which the original data is an explanatory variable and the proposed identification information is an objective 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 uses its own member responses or its own statistical results to acquire original data for obtaining proposed identification information that identifies information to be proposed to target members, and performs a machine learning prediction process using the original data and the learning model to obtain 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 the 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 organizational members.

[0021] Furthermore, 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 pieces of correspondence information indicating the correspondence between original data and proposed identification information, and the information acquisition unit determines from the correspondence table original data whose original data satisfies the adoption conditions, and acquires from the correspondence table 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 in which, compared to the first invention, each of the two or more questions included in the first questionnaire corresponds to 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 one or more proposed identification information whose own statistical result satisfies the selection condition.

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

[0026] [Figure 1] Conceptual diagram of information system A in embodiment 1 [Figure 2] Block diagram of Information System A [Figure 3] A flowchart illustrating an example of the operation of the information processing device 1. [Figure 4] 10 is a flowchart illustrating an example of the first original data acquisition process. [Figure 5] 10 is a flowchart illustrating an example of the second original data acquisition process. [Figure 6] 10 is a flowchart illustrating an example of the first information acquisition process. [Figure 7] 10 is 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] 10 is 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] 10 is a flowchart illustrating an example of the first teacher data configuration process. [Figure 13] 10 is a flowchart illustrating an example of the second teacher data configuration process. [Figure 14] 10 is 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] Figure showing the first questionnaire management table [Figure 17] Figure showing the second questionnaire management table [Figure 18] A diagram showing the candidate management table [Figure 19] Figure 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 INVENTION

[0027] Hereinafter, embodiments of an information processing device and the like will be described with reference to the drawings. Note that components with the same reference numerals in the embodiments 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 sent to a member of an organization to output proposed identification information that is information tailored to the response and that is information for the growth of the member. Note that the organization may be, for example, a company, a local government, a school, or a private 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 responses to a first questionnaire given to one member of an organization and responses to a second questionnaire given to one of the other members of the organization.

[0030] In this embodiment, an information processing device stores learning information created using two or more pieces of training data obtained using the responses to a first questionnaire sent to one member and the proposed identification information, and outputs the proposed identification information using the learning information. The learning information may be, for example, a learning model, a model possessed by a generative AI, or a correspondence table.

[0031] Furthermore, in this embodiment, an information processing device is described that stores learning information created using two or more pieces of training data obtained using the responses to a first questionnaire given to one member, the responses to a second questionnaire given to one of the other members, and proposed identification information, and outputs proposed identification information using the learning information. 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 this 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 outputs information for the growth of a target member using at least the responses to a first questionnaire given to the target member. 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 location of the information processing device 1 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 the target member from whom the proposed identification information is obtained, or another member who answers the second questionnaire. Also, a member is a part of an organization. A member is, for example, a company employee, a part-time worker, etc. The member's position or title does not matter. The terminal device 2 is a mobile terminal such as a smartphone, tablet terminal, or mobile phone, or a so-called personal computer, and its type does not matter.

[0036] 2 is a block diagram of an information system A according to this 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 26.

[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 also be called items, questions, etc. The content of the questions is not important.

[0042] The first questionnaire is a questionnaire about one member himself / herself. The first questionnaire preferably 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 work-related items in the work of the one 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 one member.

[0043] The second questionnaire is a questionnaire that other members ask one member. In other words, the second questionnaire is a questionnaire that other members answer. The second questionnaire is a set of one or more questions for one member. The second questionnaire, for example, has one or more questions regarding the work performance of one member. It is preferable that each of the one or more questions that make up the second questionnaire is a question that asks about the member's expectations for an item or their satisfaction with an item regarding the work performance of one member. When members are asked about their expectations and satisfaction with an item, the question asking about their expectations for the item and the question asking about their satisfaction with the item may be considered 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 that asks about both expectations and satisfaction.

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

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

[0046] Another member is a member different from the first member. The other member and the first member may, for example, be from the same company. The other member may, for example, be the first member's boss, the first member's subordinate, the first member's colleague, or the first member's immediate superior. An immediate superior is someone in a higher position in a different department than the first member (for example, an adjacent department).

[0047] The candidate management unit 112 stores one or more pieces of candidate information. The candidate information is information relating to the proposed identification information of a candidate that can be acquired. Note that, hereinafter, the candidate information may be simply 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 from among a proposed identifier, title information, and proposed content information.

[0048] The proposal identification information identifies information that should be proposed for the growth of a member. The information to be proposed is, for example, information that indicates the growth challenges of the member. The growth challenges are challenges 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 that is proposed to the member. The proposal content information is, for example, information that has 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 relating to an organization. The organization information includes, for example, an organization identifier and one or more organization attribute values.

[0050] An organization identifier is information that identifies an organization. For example, the organization identifier is an organization ID or 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] Organization attribute values ​​are attribute values ​​of an organization. Examples of organization attribute values ​​include industry type, size information that identifies the size of the organization, annual percentage information that identifies the annual percentage of the organization, age percentage information that identifies the age ratio of the organization's members, job content, gender composition information that identifies the gender composition of the organization, fiscal year end, general meeting timing information that identifies the timing of the general meeting, and business information. Business information is information about the organization's business. Examples of business information include sales, profit amount, profit margin, productivity, and growth rate.

[0052] The answer storage unit 114 stores an answer set. An answer set is a set of one or more member answers. A member answer is a response to a questionnaire for one member. A member answer is either the member's own answer or the answer of another member. 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 also 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 also be associated with the member identifier of the member who responded. The other member responses may also 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] The response storage unit 114 stores, for example, one or more sets of a self-member response corresponding to a member identifier and one or more other-member responses. Such a set is a self-member response given by a target member identified by a member identifier and other-member responses from 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 response and one or more other-member responses are accepted, and is a history of proposals to the target member. The proposal history includes, for example, proposal identification information (for example, a proposal identifier) ​​and the date of proposal. The proposal history includes, for example, result information. The result information is information about the result of proposing 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 (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 below. 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. For example, the model condition is "industry = trading company AND size = large company." Note that if proposed identification information is acquired without using learning information, the learning information storage unit 115 is not necessary.

[0057] Learning information is information based on two or more sets of training data. Learning information is usually information acquired using two or more sets of training data. Learning information is, for example, a learning model or a correspondence table. Learning information is, for example, a learning model possessed by generative AI.

[0058] A learning model is information obtained by performing a machine learning learning process using two or more pieces of training data. Learning information is, for example, a model obtained by performing a machine learning learning process using one or more positive examples and one or more negative examples, which will be described later. A learning model may also be called a learner, a classifier, or the like. The machine learning algorithms for the learning process and the prediction process, which will be described later, may be deep learning, random forest, decision tree, SVM, SVR, or the like. In addition, 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 for machine learning.

[0059] The correspondence table is information having two or more pieces of correspondence information that indicate 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 typically 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 includes original data. Original data is information that is the basis of teacher data. The original data includes, for example, a member's own response or a member's own statistical result. The original data includes, for example, one or more other member's responses or other statistical results. The original data includes, 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 include proposal identification information. The original data may include one or more organization attribute values.

[0062] The training data is, for example, 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 cases, the original data may include, for example, the member's own responses or their own statistical results. In such cases, the original data may include, for example, one or more other member's responses or other statistical results. In such cases, the original data may include, for example, one or more other member's responses or other statistical results and the member's own responses or their own statistical results. In such cases, the original data does not include proposal identification information. (2) When the explanatory variables are the original data and the target variable is a flag

[0064] In such a case, the original data includes, for example, the member's own response or statistical result, and proposal identification information. In such a case, the original data includes, for example, one or more other member's responses or statistical results, and proposal identification information. In such a case, the original data includes the member's own response or statistical result, one or more other member's responses or 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 the output to one member will be effective. The second flag is information indicating that the output to one member will not be effective.

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

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

[0067] The receiving unit 12 receives various information, instructions, etc. The various information, instructions, etc. include, 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 the subject of the survey and corresponds to the member identifier of the target member who is the respondent. The other-member response, for example, corresponds to the member identifier of the member who responded. The other-member response is usually corresponds to the member identifier of the target member who is the subject of the survey. The information receiving unit 121 does not need to simultaneously receive a self-member response corresponding to the member identifier of one target member and one or more other-member responses.

[0069] Here, acceptance usually means receiving from the terminal device 2, but it may also be considered to be a concept that includes, for example, acceptance of information input from an input device such as a keyboard, mouse, or touch panel, or 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's answer and obtains one or more other statistical results. The statistical processing unit 131, for example, statistically processes two or more other member's answers associated with 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's answers, a representative value of two or more answers (e.g., average, 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 (e.g., average, median). An area may also 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 member's own responses and obtains one or more of the member's own statistical results. The member's own statistical results are, for example, the sum of two or more responses contained in the member's own responses, a representative value of two or more responses (for example, the average value, the median), the sum of one or more responses corresponding to each of two or more regions, or a representative value of one or more responses corresponding to each of two or more regions. The region may also be referred to as a category, class, group, etc. The region 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 training data. Below, we will explain the case where the learning information is a learning model and the case where the learning information is a correspondence table separately. Note that if proposed identification information is acquired without using 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 cases, the original data includes, for example, the member's own responses or their own statistical results. In such cases, the original data includes, for example, one or more other member's responses or other statistical results. In such cases, the original data includes, for example, the member's own responses or their own statistical results and one or more other member's responses or other statistical results. In such cases, the original data does not include proposal identification information.

[0077] The learning unit 132 performs machine learning learning processing using the two or more pieces of training 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 through machine learning prediction processing. (1-2) When the explanatory variables are the original data and the target variable is a flag

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

[0079] The learning unit 132 performs a machine learning learning process using the two or more pieces of training 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") through a machine learning prediction process. (1-3) When the explanatory variable is the original data and the target variable is the score

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

[0081] The learning unit 132 performs machine learning learning processing using the two or more pieces of training 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 member to obtain a score through machine learning prediction processing. (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 cases, the original data includes, for example, the member's own responses or their own statistical results. In such cases, the original data includes, for example, one or more other member's responses or other statistical results. In such cases, the original data includes, for example, the member's own responses or their own statistical results and one or more other member's responses or other statistical results. In such cases, 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 target variable is a flag

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

[0085] For each of the two or more pieces of teacher data, the learning unit 132 acquires 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 acquire a correspondence table having a first representative vector which is a representative vector of vectors whose flags correspond to the first flag, and a second representative vector which is a representative vector of vectors whose flags correspond to the second flag. (2-3) When the explanatory variable is the original data and the target variable is the score

[0087] In such a case, the original data includes, for example, the member's own answers or statistical results, and proposal identification information. In such a case, the original data includes, for example, one or more other member's answers or statistical results, and proposal identification information. In such a case, the original data includes, for example, the member's own answers or statistical results, and one or more other member's answers or 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 the 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 stores 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 its own member response or its own statistical results based on the own member response received by the information receiving unit 121, and acquires original data using the own member response or the own own statistical results. The information acquisition unit 133, for example, acquires one or more other member responses or other statistical results based on the one or more other member responses received by the information receiving unit 121, and acquires original data using the one or more other member responses or other statistical results. The information acquisition unit 133, for example, acquires its own member response or its own statistical results based on the own member response received by the information receiving unit 121, and one or more other member responses or other statistical results based on the one or more other member responses received by the information receiving unit 121, and acquires original data using the own member response or the own statistical results and the one or more other member responses or other statistical results.

[0092] The original data is information for acquiring proposal identification information that identifies the 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 responses and two or more answers included in the own member response. 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 response,..., answer to question N included in the own member response). 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, (average value of answers to two or more questions corresponding to group 1 included in two or more other members' answers, average value of answers to two or more questions corresponding to group 2 included in two or more other members' answers,..., average value of answers to two or more questions corresponding to group 1 included in one's own member's answer,..., average value of answers to two or more questions corresponding to group N included in one's 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. (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 acquisition 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 target variable is a flag (1-2-1) When likelihood (or score) is not used

[0096] The information acquisition unit 133 performs machine learning prediction processing for each of one or more pieces of 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 for which the 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 that satisfies the favorable condition. Each of the one or more pieces of candidate information is proposed identification information. The favorable 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 target 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 acquiring 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 reside in the storage unit 11, and may reside in another device not shown. 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 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 your 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 associated with 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 to acquire 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 conditions. Note that the score is, for example, the sum of the acquired one or more answers, or a representative value (e.g., average or median) 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 associated with 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 to acquire 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 conditions. Note that the score is, for example, the sum of the acquired one or more answers, or a representative value (e.g., 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 other statistical results

[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 associated with the candidate information and that are included in the member's own 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 to acquire 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 conditions. Note that the score is, for example, the sum of the acquired one or more answers, or a representative value (e.g., average or median) 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] Note that output here usually means transmission to terminal device 2, but it can 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 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 are associated with, for example, an organization identifier and a member identifier.

[0113] Here, reception is a concept that includes reception of information input from input devices such as a keyboard, mouse, or touch panel, reception of information transmitted via a wired or wireless communication line, and reception of 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, keyboard, mouse, or menu screen.

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

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

[0117] The terminal receiving unit 25 receives various types of information from the information processing device 1. The various types of information include, 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 types of information. The various types of information include, 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 types of information include, for example, proposal identification information or proposal content information. Here, output is a concept that includes displaying on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage on a recording medium, and delivery of processing results to another processing device, another program, etc.

[0119] The storage unit 11, questionnaire storage unit 111, candidate management unit 112, organizational information storage unit 113, response storage unit 114, learning information storage unit 115, and terminal storage unit 21 are preferably non-volatile recording media, but can also be realized with 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 input via an input device may be stored in the storage unit 11 etc.

[0121] The reception unit 12 and the information reception unit 121 are preferably realized by wireless or wired communication means, but 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, statistical processing unit 131, learning unit 132, information acquisition unit 133, and device processing unit 23 can usually be realized by a processor, memory, etc. The processing procedures of the processing unit 13, etc. are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type does not matter.

[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 also be realized by a means for receiving broadcasts.

[0127] The terminal output unit 26 may 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 by a combination of driver software for an output device and the 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) The information receiving unit 121 determines 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 determines whether or not it has received the member's own response from the terminal device 2. Furthermore, before receiving the member's own response, the output unit 14 normally transmits the first questionnaire to the terminal device 2. Furthermore, the received member's own response is associated with the member identifier of the member's response that has been sent.

[0131] (Step S302) The processing unit 13 stores the member's own 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 of 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. The information acquisition processing is processing for acquiring proposed identification information.

[0135] (Step S305) The information acquisition unit 133 acquires, from the candidate management 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 normally transmits the proposal content information acquired in step S305 to the terminal device 2 that has transmitted the member's own response. In other words, it is preferable that the target member can acquire the proposal content information in response to the transmission of the member's own response (response to the questionnaire).

[0138] (Step S307) The information receiving unit 121 determines 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 current 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 it has received other member responses from the terminal device 2. Furthermore, before receiving other member responses, 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's response received in step S307 with the member identifier of the target member and stores it in the response storage unit 114. The process returns to step S301.

[0141] (Step S309) The statistical processing unit 131 determines whether 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 responses from one or more other members to the second questionnaire scheduled for one member have been received.The timing of the statistical processing is, for example, when the processing unit 13 determines that responses from other members and the member's own response to the second questionnaire scheduled for one or more other members have been received for one member.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 whom statistical processing is to be performed. If there is an i-th member, proceed to step S312; if there is no i-th member, return to step S301. The i-th member for whom statistical processing is to be performed is, for example, a member for whom responses from other members to the second questionnaire of one or more other planned members have been accepted. The i-th member for whom statistical processing is to be performed is, for example, a member for whom responses from other members and the member's own response to the second questionnaire of one or more other planned members 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 determines whether or not to perform learning processing. If it is determined that learning processing should be performed, the process proceeds to step S315, and if it is determined that learning processing should not be performed, the process proceeds to step S320. Note that cases in which it is determined that learning processing should be performed include when a learning instruction is accepted or when a predetermined time has arrived.

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

[0149] (Step S316) The learning unit 132 determines 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 predetermined. There may be only one condition, or the condition may be "empty." An "empty" condition 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. Model conditions are conditions for adopting a learning model. 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 in FIG.

[0152] (Step S318) The learning unit 132 stores 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 determines 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. The information here is, for example, candidate information, organization information, and result information corresponding to the proposal content information presented to the member. The result information is, for example, information indicating whether the proposal content information presented to the member was good, or an evaluation value or score of the proposal content information presented to the member. Here, the reception unit 12 receives information from, for example, the terminal device 2.

[0155] (Step S321) The processing unit 13 stores the information received in step S320 in the storage unit 11.

[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 the member identifier of the 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 whom 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 determines whether or not to use an 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 an organization attribute value is usually determined in advance.

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

[0162] (Step S405) The information acquisition unit 133 acquires 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, from the organization information storage unit 113. Note that the organization attribute values ​​to be used are usually determined in advance.

[0163] (Step S406) The information acquisition unit 133 constructs original data using 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, and returns to the upper level processing.

[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, 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 using either the response of the member itself or the responses of one or more other members.

[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 acquisition unit 133 acquires the member identifier of the target member. The information acquisition unit 133 acquires one or more other statistical results paired with the member identifier from the response storage unit 114.

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

[0169] (Step S503) The information acquisition unit 133 determines 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, and if the organization attribute value is not to be used, the process proceeds to step S506.

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

[0171] (Step S505) The information acquisition 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 acquisition unit 133 constructs 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, one or more individual statistical results, and one or more organizational attribute values.

[0174] In the flowcharts of Figures 4 and 5, the information acquisition unit 133 may construct the 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 acquisition unit 133 acquires, from the learning information storage unit 115, a learning model to be used in the information acquisition process.

[0177] The information acquisition unit 133 acquires, for example, a learning model that matches 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. 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 acquisition unit 133 acquires, from the learning information storage unit 115, a learning model to be used in the information acquisition process.

[0183] The information acquisition 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 acquisition unit 133 assigns 1 to a counter i.

[0185] (Step S703) Information acquisition unit 133 determines 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 constructs 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 acquisition 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 acquisition unit 133 acquires the flag and score, which are the execution results in step S705.

[0189] (Step S707) The information acquisition 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 the good condition. Note that the good condition is, for example, a score that is equal to or greater than a threshold, or a score that is 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 acquisition unit 133 acquires, from the learning information storage unit 115, a learning model to be used in the information acquisition process.

[0194] The information acquisition 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 determines 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 constructs 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 acquisition 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 acquisition unit 133 acquires the score that is the execution result in step S805.

[0200] (Step S807) The information acquisition 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 paired with a score that satisfies a favorable condition. Note that the favorable 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 acquisition unit 133 assigns 1 to a counter i.

[0205] (Step S902) The information acquisition unit 133 determines whether the i-th correspondence information exists in the correspondence table, which 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 acquisition unit 133 acquires the similarity between the vector included in the i-th correspondence information and the original data (here, the vector).

[0207] (Step S904) The information acquisition 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 favorable 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 favorable 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. 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 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 determines whether or not to calculate the i-th statistical value. If the i-th statistical value is to be calculated, proceed to step S1006; if the i-th statistical value is not to be calculated, proceed 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 area, and is the sum of the answers included in the own member's answers. The i-th statistical value is, for example, one or more answers corresponding to each area for each area, and is the sum of the answers included in one or more other member's answers.

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

[0217] (Step S1007) The statistical processing unit 131 calculates the i-th statistical value 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 ​​themselves. The one or more statistical results to be accumulated are, for example, the sum of one or more answers from 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 stores 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 training data.

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

[0224] (Step S1103) The learning unit 132 determines whether 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 the counter j.

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

[0227] (Step S1106) The learning unit 132 constructs training data using the j-th proposed identification information corresponding to the i-th member identifier. An example of such training data construction processing will be described with reference to the flowcharts in 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. 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 determines 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 in 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 FIGS. 4 and 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 training 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 processing.

[0240] The training data here is, for example, information used in machine learning learning processing 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 training 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 of the response 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 i-th member identifier and the j-th 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 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 FIGS. 4 and 5.

[0244] (Step S1303) The learning unit 132 generates training 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 processing.

[0245] The training data here is, for example, data with a flag as a response 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 a flag and a pair of the original data and the j-th proposed identification information.

[0246] Next, an example of the third training 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 the score acquisition process 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 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 FIGS. 4 and 5.

[0249] (Step S1403) The learning unit 132 generates training 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 processing.

[0250] The training data here is, for example, data with a score as a response variable and original data and the jth 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 score and a pair of original data and the jth 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 post-proposal response of the member 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 response 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 response after the proposal content information has been presented to the member. The post-proposal score is, for example, the statistical processing result (e.g., sum, average, median) of each answer contained in the member's own response after the proposal content information has been 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 response 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 response before the proposal content information is presented to the member. The pre-proposal score is, for example, the statistical processing result (e.g., sum, average, median) of each answer contained in the member's own response before the proposal content information is presented to the member.

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

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

[0258] 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's responses, or may obtain the member's score using the member's own response and one or more other member's responses. The score obtained using one or more other member's responses is, for example, the result of statistical processing (e.g., sum, average, median) of the responses of one or more other member's 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 that identifies 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 each member answers questions directed to that member. The first questionnaire management table manages two or more records each 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. Answers to questions are, for example, on a four-point scale (any number from 1 to 4), but may also be on a ten-point scale, a five-point scale, a three-point scale, etc. Questions in the first questionnaire include, for example, a question asking about the member's attitude toward work.

[0261] It is assumed that 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 each having an "area," "item No.," "question No.," "expectation question," "question No.," "satisfaction question," and "growth task ID." The "area" indicates the category of the item and the question. The "item No." is an identifier for the item. Here, the item is the subject of the question. An item may have two questions, one asking about expectations and the other asking about satisfaction. An item may also be a meta-question. A meta-question is a question about a larger item that asks only about satisfaction. The "question No." is an identifier for the question. The "question No." may correspond to an expectation question or to an expectation question. The "expectation question" is a question asking about expectations for the item. The "Satisfaction Question" is a question asking about satisfaction with an item. The "Growth Task ID" indicates the growth task ID to which the item or question corresponds. 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. Furthermore, answers to the expectation question and answers to the satisfaction question are, for example, on a 5-point scale (any number from 1 to 5), but they can also be on a 10-point scale, 3-point scale, etc. For example, the answer to the expectation question indicates a higher expectation. For example, the answer to the satisfaction question indicates a higher satisfaction, the larger the number.

[0262] The candidate management unit 112 stores a candidate management table shown in FIG. 18. The candidate management table is a table for managing two or more pieces of candidate information. The candidate management table manages two or more records each having a "growth task ID," "title information," "proposal content information," and "weight." The "title information" is the title of the growth task. The "proposal content information" is information corresponding to the growth task and is presented to the target member for growth. The "proposal content information" is a file name in this example. It is assumed that the file indicated by the "proposal content information" also exists in the candidate management unit 112. The "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 = sum of answers to questions in the first questionnaire corresponding to the growth task / growth task weight." Furthermore, for example, the score of the growth task is calculated by "growth task score = (sum of answers to questions in one or more second questionnaires corresponding to the growth task + sum 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 also assumed that the reply storage unit 114 stores, in pairs with the "member identifier = U1101" of target member A, the other member replies answered by member A's superior and the other member replies answered 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 sent 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 own member response associated with the member identifier "U1101" from the terminal device 2 of member A. Next, the processing unit 13 stores the received own member 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 target member's own answers. 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. Here, 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 pairs with the growth task ID "3" from the candidate management table (Figure 18). Next, the information acquisition unit 133 acquires the file "file3" from the candidate management unit 112. Furthermore, the information acquisition unit 133 acquires the proposal content information "file1" that pairs with the growth task ID "1" from the candidate management table (Figure 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 combined information to the terminal device 2 of member A.

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

[0269] (Example 2) The information receiving unit 121 of the information processing device 1 receives the own member response associated with the member identifier "U1101" from the terminal device 2 of member A. Next, the processing unit 13 stores the received own member 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. In addition, the statistical processing unit 131 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 growth task weight 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 scores of the growth task. 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 (Figure 18). Next, the information acquisition unit 133 acquires the file "file3" from the candidate management unit 112. Furthermore, the information acquisition unit 133 acquires the proposal content information "file1" that pairs with the growth task ID "1" from the candidate management table (Figure 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 combined information to the terminal device 2 of member A.

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

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

[0275] In particular, according to this embodiment, it is possible to appropriately suggest information for the growth of members of an organization using the member's own response.

[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, machine learning processing can appropriately suggest information for the growth of members of an organization.

[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 implemented by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software implementing 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 results that are the result of statistical processing of 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 results, and an information output unit that outputs the proposal identification information or proposal content information corresponding to the proposal identification information.

[0282] 20 shows the appearance of a computer that executes the programs described herein to realize the information processing device 1 and the like according to the various embodiments described above. The above-described 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 that causes computer system 300 to execute the functions of information processing device 1 and the like of the above-described embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and then 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 when executed. The program may also 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-described embodiment. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve desired results. How the computer system 300 operates is well known, and a detailed description thereof 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 transmission step (processing that can only be performed by hardware).

[0288] The computer that executes the program may be a single computer or a plurality of computers, that is, it may perform centralized processing or distributed processing.

[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 the effect of being able to appropriately propose information for the growth of members of an organization, and is useful as a server or the like that proposes information for the growth of members of an organization. [Explanation of symbols]

[0293] A. Information Systems 1. Information processing equipment 2. Terminal Device 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 Unit 111 Questionnaire storage section 112 Candidate Management Department 113 Organization Information Storage Unit 114 Answer storage section 115 Learning information storage unit 121 Information Reception Department 131 Statistical Processing Unit 132 Learning Department 133 Information Acquisition Department 141 Information output unit

Claims

1. a learning information storage unit that stores learning information created using two or more pieces of teacher data that include 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 a result of statistically processing the member's own response, and proposal identification information that identifies information to be proposed for the growth of the member; an information receiving unit that receives responses from the target member to a first questionnaire about the target member; an information acquisition unit that acquires its own statistical results, which are the results of statistical processing of the own member answers or the own member answers received by the information acceptance unit, and acquires proposed identification information using original data including the own member answers or the own statistical results and the learning information; an information output unit that outputs the proposal content information stored in a candidate management unit that stores the proposal identification information or proposal content information corresponding to the proposal identification information, in association with the proposal identification information; The learning information is a learning model obtained by performing a machine learning learning process using the two or more pieces of teacher data, The system further includes a candidate management unit that stores two or more candidate information pieces that are proposed identification information pieces of the acquired candidates, The two or more teacher data are The training data has a score corresponding to the proposed identification information, The information acquisition unit For each of the two or more pieces of candidate information, a machine learning prediction process is performed using the original data, the candidate information, and the learning model, a score is obtained, and the proposed identification information is obtained as candidate information whose score satisfies a good condition. Information processing device.

2. An information processing method in which all processing performed by the information processing device described in claim 1 is performed by a computer.

3. A computer, A program for causing the information processing device according to claim 1 to function.

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