SCORE PREDICTION DEVICE, SCORE PREDICTION METHOD, AND PROGRAM

Abstract

To solve a conventional problem that a score of an organization cannot be predicted by using daily information of members of the organization.SOLUTION: A score prediction device includes: a daily information storage part for storing one or more pieces of prediction daily information being daily information being acquired by two or more respective members of an organization daily and being daily information in a period of a prediction object of a score of the organization; a score acquisition part for acquiring a prediction score being a score corresponding to the one or more pieces of prediction daily information by using a teacher score being a score of the organization acquired by using a correspondence relation between a plurality of pieces of organization answer information showing answers of questions to two or more respective members of the organization and one or more pieces of teacher daily information being daily information in a period corresponding to the answers or the questions and daily information of the two or more respective members, and the one or more pieces of prediction daily information; and a score output part for outputting a prediction score. The score prediction device can predict a score of the organization by using daily information of members of the organization.SELECTED DRAWING: Figure 13

Description

[Technical field]

[0001] The present invention relates to a score prediction device for predicting the score of an organization. [Background technology]

[0002] Conventionally, there has been a management diagnosis support device capable of comprehensively determining the soundness of management (see Patent Document 1).

[0003] This management diagnosis support device collects, analyzes and displays the results of a score-based questionnaire in which answers are selected from a number of options for a question item, and comprises a memory means for storing the answer results of the questionnaire collected from each hierarchical level constituting an organization, an analysis table in which a number of diagnostic elements related to corporate management are assigned to each of the plurality of question items, and in which a score indicating a weighting is set for each diagnostic element corresponding to the plurality of question items, an analysis means for analyzing the answer results of the questionnaire, and an output means for outputting the analysis results by the analysis means, wherein the analysis means multiplies the answer score based on the answer results of the question item by the score set for each diagnostic element in the analysis table corresponding to the question item to calculate an individual element evaluation value for each diagnostic element of each question item, collects the individual element evaluation values ​​of all questions for each diagnostic element to calculate an individual element summary value, and the output means collects the individual element summary value for each hierarchical level and outputs it in the form of a graph or a table. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6621903 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in conventional technology, the results of a questionnaire were compiled and analyzed to present information that could be used to judge the soundness of management, and it was not possible to predict the organization's score using the everyday information of its members. [Means for solving the problem]

[0006] The score prediction device of the first invention is a score prediction device comprising: a daily information storage unit which stores one or more predicted daily information, which is daily information that can be obtained in the daily lives of two or more members of an organization and is daily information for a period for which the organization's score is to be predicted; a teacher score, which is the score of the organization obtained using a plurality of organization answer information indicating answers to questions for each of two or more members of the organization, and a correspondence between the teacher score and one or more teacher daily information, which is daily information for a period corresponding to the questions or answers, and which is daily information of each of two or more members, and a predicted score, which is a score corresponding to the one or more predicted daily information, using the one or more predicted daily information; and a score output unit which outputs the predicted score.

[0007] With this configuration, the organization's score can be predicted using the daily information of the organization's members.

[0008] Furthermore, the score prediction device of the second invention is a score prediction device in which, compared to the first invention, each of the one or more teacher daily information includes teacher work information related to the work performed by the members, each of the one or more predicted daily information includes predicted work information related to the work performed by the members, and the score acquisition unit acquires a predicted score using the correspondence between the one or more teacher daily information including the teacher work information and the teacher score, and the one or more predicted daily information including the predicted work information.

[0009] With this configuration, the score of an organization can be predicted using work information related to the work performed by members of the organization.

[0010] In addition, the score prediction device of the third invention is a score prediction device in which, compared to the second invention, the teacher work information includes teacher input information entered by the members, the predicted work information includes predicted input information entered by the members, and the score acquisition unit acquires a predicted score using a correspondence between one or more teacher daily information including the teacher input information and the teacher score, and one or more predicted daily information including the predicted input information.

[0011] With this configuration, the score of an organization can be predicted using input information entered by members of the organization.

[0012] In addition, the score prediction device of the fourth invention is a score prediction device in which, compared to the third invention, the teacher input information includes teacher work input information related to input for the member to perform his or her own work, the predicted input information includes predicted work input information related to input for the member to perform his or her own work, and the score acquisition unit acquires a predicted score using a correspondence between one or more teacher daily information including the teacher work input information and the teacher score, and one or more predicted daily information including the predicted work input information.

[0013] With this configuration, the score of an organization can be predicted using supervisory work input information related to inputs made by members of the organization for carrying out their own work.

[0014] Furthermore, the score prediction device of the fifth invention is a score prediction device in which, compared to the second or third invention, the teacher input information includes teacher communication information regarding communication between two or more members, the predicted input information includes predicted communication information regarding communication between two or more members, and the score acquisition unit acquires a predicted score using a correspondence between one or more pieces of teacher daily information including the teacher communication information and a teacher score, and one or more pieces of predicted daily information including the predicted communication information.

[0015] With such an arrangement, communication information regarding communications between members of an organization can be used to predict the score of the organization.

[0016] In addition, the score prediction device of the sixth invention is a score prediction device in which, compared to any one of the third to fifth inventions, the score acquisition unit acquires one or more types of information from data amount information identifying the data amount of teacher input information, input frequency information identifying the input frequency of the teacher input information, analysis information indicating the analysis result of the teacher input information, and timing information identifying the timing of the input of the teacher input information, and acquires one or more types of information from data amount information, input frequency information of the predicted input information, analysis information of the predicted input information, and timing information of the predicted input information, acquires correspondence information identifying the correspondence between one or more teacher features including one or more types of information acquired from the teacher input information and the teacher score, and acquires a predicted score using the correspondence information and one or more prediction features including one or more types of information acquired from the predicted input information.

[0017] With this configuration, the score of an organization can be predicted using one or more types of information among data amount information of the input information, input frequency information, analysis information of the input information, and timing information of the input information.

[0018] In addition, the score prediction device of the seventh invention is a score prediction device in which, compared to the second invention, the teacher work information includes teacher attendance status information regarding the attendance of members, the predicted work information includes predicted attendance status information regarding the attendance of members, and the score acquisition unit acquires a predicted score using a correspondence between one or more teacher daily information including the teacher attendance status information and a teacher score, and one or more predicted daily information including the predicted attendance status information.

[0019] With this configuration, the organization's score can be predicted using the attendance status information of the organization's members.

[0020] In addition, the score prediction device of the eighth invention is a score prediction device in which, compared to the second invention, the teacher work information includes teacher work result information regarding the results of the members' work performance, the predicted work information includes predicted work result information regarding the results of the members' work performance, and the score acquisition unit acquires a predicted score using a correspondence between one or more teacher daily information including teacher work result information and a teacher score, and one or more predicted daily information including predicted work result information.

[0021] With this configuration, the organization's score can be predicted using business result information related to the results of the organization's business execution.

[0022] Furthermore, the score prediction device of the ninth invention is a score prediction device in which, compared to the first invention, each of the one or more pieces of teacher daily information includes teacher health information related to the health of the members, and each of the one or more pieces of predicted daily information includes predicted health information related to the health of the members, and the score acquisition unit acquires a predicted score using a correspondence between the one or more pieces of teacher daily information including the teacher health information and the teacher score, and the one or more pieces of predicted daily information including the predicted health information.

[0023] With this configuration, health information of members of an organization can be used to predict the score of the organization.

[0024] In addition, the score prediction device of the tenth invention is a score prediction device in which, compared to the ninth invention, the teacher health information includes teacher biometric information regarding the member's biometrics, the predicted health information includes predicted biometric information regarding the member's biometrics, and the score acquisition unit acquires a predicted score using a correspondence between one or more teacher daily information including the teacher biometric information and the teacher score, and one or more predicted daily information including the predicted biometric information.

[0025] With this configuration, the biometric information of members of the organization can be used to predict the score of the organization.

[0026] In addition, the score prediction device of the eleventh invention is a score prediction device in which, compared to the tenth invention, the teacher biometric information includes teacher stress information related to the stress of the members, the predicted biometric information includes predicted stress information related to the stress of the members, and the score acquisition unit acquires a predicted score using a correspondence between one or more pieces of teacher daily information including teacher stress information and a teacher score, and one or more pieces of predicted daily information including predicted stress information.

[0027] With this configuration, the organization's score can be predicted using stress information of the organization's members.

[0028] Furthermore, the score prediction device of the twelfth invention is a score prediction device in which, compared to the tenth or eleventh invention, the teacher biometric information includes teacher measurement information which is the measurement result of the member's biometric information, the predicted biometric information includes predicted measurement information obtained from the member's biometric information, and the score acquisition unit acquires a predicted score using a correspondence between one or more teacher daily information including the teacher measurement information and the teacher score, and one or more predicted daily information including the predicted measurement information.

[0029] With this configuration, the score of the organization can be predicted using measurement information that is the result of measuring the living bodies of members of the organization.

[0030] Furthermore, the score prediction device of the thirteenth invention is a score prediction device according to any one of the tenth to twelfth inventions, wherein the teacher biometric information includes teacher biometric characteristic information which is an image of a member, or a feature of the image of a member, or a sound uttered by a member, or a feature of the sound uttered by a member, and the predicted biometric information includes predicted biometric characteristic information which is an image of a member, or a feature of the image of a member, or a sound uttered by a member, or a feature of the sound uttered by a member, and the score acquisition unit is a score prediction device that acquires a predicted score using a correspondence between one or more teacher daily information including the teacher biometric characteristic information and a teacher score, and one or more predicted daily information including the predicted biometric characteristic information.

[0031] With this configuration, the score of an organization can be predicted using images of members of the organization or voices of the members.

[0032] Furthermore, the score prediction device of the fourteenth invention is a score prediction device according to any one of the ninth to thirteenth inventions, wherein the teacher health information includes teacher life information relating to the lives of the members, the predicted health information includes predicted life information relating to the lives of the members, and the score acquisition unit acquires a predicted score using a correspondence between one or more pieces of teacher daily information including the teacher life information and the teacher score, and one or more pieces of predicted daily information including the predicted life information.

[0033] With this configuration, the score of an organization can be predicted using lifestyle information of the organization's members.

[0034] Further, the score prediction device of the fifteenth invention is a score prediction device according to any one of the first to fourteenth inventions, wherein the score acquisition unit includes correspondence acquisition means for acquiring correspondence information which specifies a correspondence between one or more pieces of teacher daily routine information and a teacher score, and score acquisition means for acquiring a predicted score using the correspondence information and one or more pieces of predicted daily routine information.

[0035] With this configuration, the organization's score can be predicted using the daily information of the organization's members.

[0036] Furthermore, the score prediction device of the sixteenth invention is a score prediction device in which, compared to the fifteenth invention, one or more pieces of teacher routine information and the teacher score correspond to one or more organization attribute values, the correspondence acquisition means acquires correspondence information specifying the correspondence between the one or more pieces of teacher routine information and the teacher score for each organization condition configured using one or more organization attribute values, and the score acquisition means acquires a predicted score using the correspondence information corresponding to the organization condition matched by the organization to be predicted and the one or more pieces of predicted routine information.

[0037] With this configuration, a predicted score that matches the attribute values ​​of the organization can be obtained.

[0038] Further, the score prediction device of the seventeenth invention is a score prediction device according to the fifteenth or sixteenth invention, wherein the correspondence acquisition means performs a learning process by a machine learning algorithm using one or more pieces of teacher routine information and teacher scores, and acquires correspondence information which is a learning model having one or more pieces of teacher routine information as explanatory variables and a teacher score as a target variable, and the score acquisition means performs a prediction process by a machine learning algorithm using the learning model and one or more pieces of predicted routine information, and acquires a predicted score.

[0039] With this configuration, machine learning techniques can be used to predict an organization's score using the daily information of its members.

[0040] Further, the score prediction device of the 18th invention is a score prediction device according to the 15th or 16th invention, wherein the correspondence acquisition means uses one or more teacher routine information to acquire a teacher vector, which is a vector having two or more elements, and acquires correspondence relationship information which is a correspondence table having one or more correspondence information which is a pair of teacher vectors and teacher scores, and the score acquisition means uses one or more predicted routine information to acquire a predicted vector, which is a vector having two or more elements, and applies the predicted vector to the correspondence table to acquire a predicted score.

[0041] With this configuration, data mining techniques can be used to predict an organization's score using the daily information of its members.

[0042] Further, the score prediction device of the 19th invention is a score prediction device according to the 15th or 16th invention, wherein the correspondence acquisition means uses one or more teacher routine information to acquire a teacher vector, which is a vector having two or more elements, and acquires correspondence information which is an equation that takes each element of the teacher vector as an input and outputs a teacher score, and the score acquisition means uses one or more prediction routine information to acquire a prediction vector, which is a vector having two or more elements, and provides each element of the prediction vector to the equation, executes the equation, and acquires a predicted score.

[0043] With this configuration, data analysis techniques can be used to predict an organization's score using daily information about its members. Effect of the Invention

[0044] According to the score prediction device of the present invention, it is possible to predict the score of an organization using daily information of the members of the organization. [Brief description of the drawings]

[0045] [Figure 1] Conceptual diagram of information system A in embodiment 1. [Diagram 2] Block diagram of Information System A [Diagram 3] A flowchart illustrating an example of the operation of the information processing device 1. [Figure 4] A flowchart illustrating an example of the score calculation process [Diagram 5] A flowchart illustrating an example of the influence degree calculation process. [Figure 6] The figure shows the item information management table. [Figure 7] A diagram showing the organization's response information [Figure 8] A diagram showing the individual score table [Figure 9] A diagram showing an example of the output [Figure 10] A diagram showing an example of the output [Figure 11] Conceptual diagram of information system B in embodiment 2 [Figure 12] Block diagram of Information System B [Figure 13] Block diagram of the score prediction device 3 [Figure 14] A flowchart for explaining an example of the operation of the score prediction device 3. [Figure 15] A flowchart illustrating an example of the correspondence acquisition process. [Figure 16] Flowchart for explaining an example of isotropy group acquisition processing [Figure 17] A flowchart illustrating an example of the business input information processing [Figure 18] A flowchart illustrating an example of the communication information processing [Figure 19] A flowchart for explaining an example of the attendance status information processing [Figure 20] A flowchart illustrating an example of the stress information processing [Figure 21] A flowchart illustrating an example of the measurement information processing [Figure 22] A flowchart illustrating an example of the image information processing [Figure 23] A flowchart illustrating an example of the voice information processing [Figure 24] A flowchart for explaining an example of the lifestyle information processing [Diagram 25] A flowchart illustrating an example of the score acquisition process [Figure 26] A flowchart for explaining an example of the operation of the terminal device 4 [Figure 27] Overview of the computer system according to the above embodiment. [Figure 28] Block diagram of the computer system DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0046] Hereinafter, embodiments of a score prediction 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.

[0047] (Embodiment 1) In this embodiment, an information system including an information processing device that acquires and outputs the influence of each item on the organization's overall score calculated from responses to two or more items will be described. Note that an organization is broadly defined as, for example, a so-called company, a private business, a local government, etc. An organization is, for example, an organization that performs some kind of work. The organization's overall score may also be called an engagement score. The information system may also be called an engagement system. The information processing device may also be called an engagement device.

[0048] In the present embodiment, an information system including an information processing device that calculates item scores and an overall score for an organization by using the expectation level of each item will be described. The information system may be called an engagement system.

[0049] In addition, in this embodiment, an information system including an information processing device having a score adjustment function for adjusting a total score by using correlation information regarding the correlation between satisfaction level and expectation level will be described.

[0050] Furthermore, in this embodiment, an information system including an information processing device that takes into account the attribute values ​​of an organization and outputs a different degree of influence for each attribute value will be described.

[0051] 1 is a conceptual diagram of an information system A in this embodiment. The information system A includes an information processing device 1 and one or more terminal devices 2. The information processing device 1 is a so-called server device here. The information processing device 1 is, for example, a cloud server or an ASP server, but the type and installation location are not important. The terminal device 2 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, a so-called personal computer, etc., and the type is not important.

[0052] FIG. 2 is a block diagram of an information system A in this embodiment.

[0053] The information processing device 1 includes a storage unit 11, a reception unit 12, a processing unit 13, and an output unit 14. The storage unit 11 includes an item information storage unit 111, an organization response information storage unit 112, and an individual score table storage unit 113. The processing unit 13 includes an item score acquisition unit 131, a total score acquisition unit 132, and an influence degree acquisition unit 133. The output unit 14 includes an item score output unit 141, a total score output unit 142, and an influence degree output unit 143.

[0054] 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 .

[0055] Various types of information are stored in the storage unit 11 constituting the information processing device 1. The various types of information are, for example, item information described later, organizational response information described later, and an individual score table described later. The individual score table may also be called an engagement score table or the like.

[0056] The item information storage unit 111 stores two or more pieces of item information. The item information is information about items of an organization. The items may be questions about the organization. The items may correspond to questions about the organization. The two or more items include, for example, comprehensive items about comprehensive matters of the organization and individual items that are individual matters about the organization. The comprehensive items are usually items for questions with a high level of abstraction. The individual items are usually items for questions with a lower level of abstraction (more specific questions). The two or more pieces of item information in the item information storage unit 111 include, for example, item information of four comprehensive items and item information of 64 individual items. The items correspond to, for example, any of two or more targets. The targets may be called factors. The targets may be called matters related to the organization. The item information includes, for example, an item identifier that identifies the item and question information. The item identifier is, for example, an ID, an item name, or the like. The item identifier may be the question information itself. The question information is information indicating a question. The question is usually a question in a questionnaire. The matter may be called a subject or an item.

[0057] The organization response information storage unit 112 stores organization response information of two or more organizations. One organization response information is a collection of information on responses of two or more members of one organization. One organization response information has two or more member response information corresponding to each of two or more members. The member response information is information including the results of a member's response to a question for an item. The member response information has two or more item answer information. The member response information usually has item answer information for the number of items. The item answer information has an item identifier and answer information. The answer information is information on the answer to the question. The answer information has, for example, satisfaction level information. The satisfaction level information is information indicating the answer regarding the member's degree of satisfaction with an item. The satisfaction level information is, for example, information specifying the satisfaction level with an item. The satisfaction level information is classified into two or more classes. The satisfaction level information can be, for example, any natural number from 1 to 5. However, the satisfaction level information may be, for example, an evaluation value having a rank or order, such as A, B, C, or any natural number from 1 to 100. Furthermore, the answer information includes, for example, satisfaction level information and expectation level information. The expectation level information is information indicating an answer regarding the member's degree of expectation for an item. The expectation level information is, for example, information specifying the degree of expectation for an item. The expectation level information is classified into two or more classes. The expectation level information may be, for example, any natural number from 1 to 5. However, the expectation level information may also be an evaluation value having a rank or order, such as A, B, C, or any natural number from 1 to 100. Note that the members may be, for example, company employees, school staff, government staff, etc., but may also include company executives, etc.

[0058] In addition, examples of comprehensive items include company satisfaction indicating the degree of satisfaction with the company, job satisfaction indicating the degree of satisfaction with the job, boss satisfaction indicating the degree of satisfaction with the boss, workplace satisfaction indicating the degree of satisfaction with the workplace, etc. In addition, examples of individual items include the company's business advantages, the announcement and communication of strategic goals, the overall sense of solidarity, the appropriateness of evaluations and wages, etc.

[0059] Furthermore, the organization response information is usually associated with an organization identifier. The organization identifier is an organization name, an ID that identifies the organization, etc. Furthermore, it is preferable that the organization response information is associated with one or more attribute values ​​of the organization. The attribute values ​​are, for example, an industry identifier indicating the industry of the organization (e.g., bank, apparel, manufacturer, etc.), a scale identifier classifying the size of the organization (e.g., large company, small or medium-sized enterprise, sole proprietorship, etc.), a region identifier indicating the region of the head office, etc.

[0060] An individual score table is stored in the individual score table storage unit 113. The individual score table is information in which item scores are determined when satisfaction information and expectation information are given. The individual score table is, for example, a table having an axis of satisfaction information and an axis of expectation information, in which an individual score is entered in each cell of the table, and in which item scores are determined when satisfaction information and expectation information are determined. It is preferable that such an individual score table is a table in which the greater the satisfaction indicated by the satisfaction information, the greater the item score obtained, and the smaller the expectation indicated by the expectation information, the greater the item score obtained.

[0061] The item scores may be determined by an arithmetic expression having the satisfaction level information and the expectation level information as parameters. Preferably, the arithmetic expression is an increasing function having the satisfaction level information as a parameter and a decreasing function having the expectation level information as a parameter.

[0062] Furthermore, when determining item scores, multiple sets of satisfaction information, expectation information, and item scores may be learned by machine learning, and the acquired learned information may be used. In such a case, the satisfaction information, expectation information, and learning information are applied to acquire item scores by machine learning. Note that, for example, SVR, deep learning, decision trees, etc. may be used for the machine learning here. However, any machine learning algorithm may be used.

[0063] The reception unit 12 receives various information and instructions. The various information and instructions are, for example, an instruction to output scores or the like, organization response information, questionnaire response information (member response information), etc. An instruction to output scores or the like is an instruction to output scores or the like. Here, reception usually refers to reception from the terminal device 2, but may also be understood as a concept including reception of information input from an input device such as a keyboard, mouse, or touch panel, reception of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory, etc.

[0064] The processing unit 13 performs various types of processing. The various types of processing are, for example, processing performed by the item score acquisition unit 131, the total score acquisition unit 132, and the influence degree acquisition unit 133. The various types of processing are, for example, processing for accumulating the received organization response information, questionnaire response information, etc. in the storage unit 11.

[0065] The item score acquisition unit 131 statistically processes the response information of the members for each of the two or more items included in each of the two or more organization response information, and acquires an item score for each organization and each item.

[0066] The item score acquisition unit 131, for example, statistically processes the satisfaction information included in the response information of each of two or more items included in the organizational response information of the organization for each organization, and acquires an item score for each organization and each item. The item score acquisition unit 131 may, for example, calculate an average value of the satisfaction information for each organization and each item, and store the average value as an item score in a buffer or storage unit 11 (not shown) in a pair with the item identifier. The item score acquisition unit 131 may also, for example, calculate a weighted average value of the satisfaction information by applying different weights according to the attribute values ​​of the members for each organization and each item, and store the weighted average value as an item score in a buffer or storage unit 11 (not shown) in a pair with the item identifier. The attribute values ​​of the members are, for example, job titles, years of service, and gender. For example, the item score acquisition unit 131 may compare the satisfaction information of employees with long years of service with the satisfaction information of employees with short years of service, and weight the information to calculate a weighted average.

[0067] The item score acquisition unit 131 acquires an item score for each organization for at least some items by using the satisfaction level information and the expectation level information. Note that the items for which the item scores are acquired by using the satisfaction level information and the expectation level information are, for example, individual items.

[0068] It is preferable that the item score acquiring section 131 acquires the item scores such that, for example, the higher the satisfaction level information, the higher the score becomes, and the lower the expectation level information, the higher the score becomes.

[0069] The item score acquisition unit 131, for example, calculates the average value of the satisfaction level information contained in the answer information and the average value of the expectation level information contained in the answer information for each of two or more organizations and two or more items, and acquires an item score for each item using the two average values.

[0070] The item score acquisition unit 131 may, for example, apply the satisfaction level information and the expectation level information to the individual score table to acquire an item score for each item. The item score acquisition unit 131 may also, for example, apply the statistical processing result of the satisfaction level information and the statistical processing result of the expectation level information to the individual score table to acquire an item score for each item. The item score acquisition unit 131 may, for example, apply the average value of the satisfaction level information and the average value of the expectation level information to the individual score table to acquire an item score for each item. The item score acquisition unit 131 may, for example, apply the weighted average value of the satisfaction level information and the weighted average value of the expectation level information to the individual score table to acquire an item score for each item. The weighted average value is a weighted average value based on the attribute values ​​of the members.

[0071] The item score acquiring section 131 may calculate the item score for each item using, for example, an increasing function with the average satisfaction level information as a parameter and a decreasing function with the average expectation level information as a parameter.

[0072] The overall score acquisition unit 132 acquires an overall score using two or more item scores for two or more pieces of organization response information. The overall score is a comprehensive score for each organization. The overall score acquisition unit 132 usually acquires a higher overall score for better scores for each item score of two or more.

[0073] It is preferable that the total score acquisition unit 132 acquires the total score using a score adjustment function as follows. The score adjustment function is a function that adjusts the score using correlation information related to the degree of correlation between the satisfaction level information and the expectation level information. Note that it is preferable that the score adjustment function be such that the greater the correlation between the satisfaction level information and the expectation level information, the higher the total score becomes.

[0074] The total score acquisition unit 132, for example, acquires a provisional total score, which is a provisional total score for each organization, using two or more item scores for each organization, acquires correlation information regarding the degree of correlation between the satisfaction level information and the expectation level information of two or more items, and acquires a total score from the provisional total score using the correlation information such that the score increases as the degree of correlation increases. The correlation information may be a correlation value between a set of satisfaction level information of two or more items and a set of expectation level information, or may be a value calculated by an arithmetic expression in which the difference between the satisfaction level information of each of two or more items and the expectation level information of each of two or more items is an increasing function with the number of items being equal to or less than a threshold as a parameter, or may be a value calculated by an arithmetic expression in which the difference between the satisfaction level information of each of two or more items and the expectation level information of each of two or more items is equal to or less than a threshold as a decreasing function with the number of items being smaller than the satisfaction level information as a parameter. In other words, any algorithm may be used to acquire the correlation information.

[0075] It is preferable that the overall score acquisition unit 132 uses both the item scores for the overall items and the item scores for the individual items, and compares the item scores for the overall items with the item scores for the individual items, weighting them more heavily to acquire the overall score.

[0076] The overall score acquisition unit 132 may calculate the overall score by, for example, "total score = α × statistical score of item scores for overall items + β × statistical score of item scores for individual items." Here, it is preferable that (α>β). That is, it is preferable that the overall score acquisition unit 132 compares the item scores for overall items with the item scores for individual items, and increases the weighting to acquire the overall score. Also, for example, "α=0.7, β=0.3." The statistical score of the item scores for overall items is, for example, the average value or weighted average of the item scores for overall items. The statistical score of the item scores for individual items is, for example, the average value or weighted average of the item scores for individual items.

[0077] The overall score acquiring unit 132 may calculate a provisional overall score by, for example, "provisional overall score = α × statistical score of item scores for overall items + β × statistical score of item scores for individual items" and may further adjust the scores by the score adjustment function described above to calculate the overall score. Furthermore, the overall score acquiring unit 132 may calculate a standard deviation of the overall score of each organization by using the overall scores of multiple organizations, and may use the standard deviation as the final overall score.

[0078] The influence degree acquisition unit 133 acquires the influence degree of the item score on the total score for each item. Note that the influence degree of the item score on the total score may be referred to as the influence degree of the item on the total score.

[0079] The influence degree acquisition unit 133 acquires, for each item, the influence degree of the item score of each item on the overall score using the item scores of two or more items of each of two or more organizations and the overall score of each of the two or more organizations. The influence degree is, for example, information indicating the influence of the item score on the overall score, and is the correlation degree of the set of item scores of multiple organizations with respect to the set of overall scores of the multiple organizations. Note that the process of acquiring the correlation degree, correlation value, and correlation information is a publicly known technique, and therefore a detailed description thereof will be omitted.

[0080] The influence degree acquisition unit 133 uses the total score of each of the two or more organizations and the item score of each item of each of the two or more organizations to calculate the influence degree, which is information regarding the correlation between the item score of each item and the total score, for each item.

[0081] It is preferable that the influence degree acquisition unit 133 acquires the influence degree of the item score of each item on the total score for each attribute value of the organization for each item. In other words, when the attribute value of the organization is an industry identifier, it is preferable that the influence degree acquisition unit 133 acquires the influence degree of each item for each industry according to the industry identifier.

[0082] The output unit 14 outputs various information. The various information is, for example, a collection of pairs of information of item identifiers and item scores. The various information is, for example, a total score. The various information is, for example, a collection of pairs of information of item identifiers and influence degrees. Here, output is usually transmission to an external device such as the terminal device 2. However, output may be considered to be a concept including display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, delivery of processing results to other processing devices, other programs, etc.

[0083] The item score output unit 141 outputs one or more item scores. The item score output unit 141 usually outputs the item score of an item identified by an item identifier in association with the item identifier. For example, the item score output unit 141 outputs an item score paired with an organization identifier included in the score etc. output instruction in association with the item identifier. It is preferable that the item score output unit 141 outputs one or more item scores in association with the organization identifier. It is also preferable that the item score output unit 141 outputs the item scores in a manner that allows visual distinction between the item score of the overall item and the item score of the individual item.

[0084] The total score output unit 142 outputs the total score. It is preferable that the total score output unit 142 outputs the total score in association with the organization identifier.

[0085] The impact output unit 143 outputs the impact acquired by the impact acquisition unit 133 in association with each item. It is preferable that the impact output unit 143 outputs the impact acquired by the impact acquisition unit 133 in association with the item identifier of each item.

[0086] Moreover, it is preferable that the impact output unit 143 outputs the impact in association with each item so that the classification of the impact can be visually distinguished.

[0087] The terminal storage unit 21 constituting the terminal device 2 stores various information. The various information is, for example, an organization identifier that identifies the organization of the user. The various information is, for example, information received by the terminal receiving unit 25. The organization identifier may be considered as information that identifies the user.

[0088] The terminal reception unit 22 receives various instructions, information, etc. Here, reception is a concept that includes reception of information input from an input device such as a keyboard, mouse, or touch panel, reception of information transmitted via a wired or wireless communication line, reception of information read from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory, etc. Various instructions, information, etc. include, for example, an instruction to output a score, etc., organizational response information, questionnaire response information, etc.

[0089] The input means for various instructions and information may be anything, such as a touch panel, a keyboard, a mouse, a menu screen, etc. The terminal reception unit 22 may 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.

[0090] The various processes performed by the device processing unit 23 are, for example, a process of configuring the information received by the device receiving unit 25 into data to be displayed. The various processes are, for example, a process of configuring the instructions received by the terminal accepting unit 22 into instructions to be transmitted.

[0091] The terminal transmitting unit 24 transmits various instructions, information, and the like to the information processing device 1. The various instructions, information, and the like are, for example, instructions configured by the terminal processing unit 23, instructions and information accepted by the terminal accepting unit 22, and the like.

[0092] The terminal receiving unit 25 receives various types of information from the information processing device 1. The various types of information include, for example, item scores, total scores, and influence levels.

[0093] The terminal output unit 26 acquires various information. The various information is, for example, information accepted by the terminal acceptance unit 22, information received by the terminal reception unit 25, and information configured by the device processing unit 23. The various information is, for example, item scores, total scores, and influence degrees.

[0094] The storage unit 11 is preferably a non-volatile recording medium, but may also be realized as a volatile recording medium.

[0095] There is no restriction on the process by which information is stored in storage unit 11, item information storage unit 111, organizational response information storage unit 112, individual score table storage unit 113, and terminal storage unit 21. For example, information may be stored in storage unit 11 etc. via a recording medium, information transmitted via a communication line etc. may be stored in storage unit 11 etc., or information inputted via an input device may be stored in storage unit 11 etc.

[0096] The reception unit 12 and the terminal reception unit 25 are usually realized by wireless or wired communication means, but may be realized by means for receiving broadcasts.

[0097] The processing unit 13, the item score acquisition unit 131, the total score acquisition unit 132, the influence degree acquisition unit 133, and the device processing unit 23 can usually be realized by an MPU, a memory, etc. The processing procedure of the processing unit 13, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may also be realized by hardware (dedicated circuit).

[0098] The output section 14, item score output section 141, total score output section 142, influence level output section 143, and terminal transmission section 24 are usually realized by wireless or wired communication means, but may also be realized by broadcasting means.

[0099] The terminal output unit 26 may be considered to include, or may not include, an output device such as a display, a speaker, etc. The terminal output unit 26 may be realized by driver software for an output device, or a combination of driver software for an output device and an output device, etc.

[0100] Next, the operation of the information system A will be described. First, an example of the operation of the information processing device 1 will be described with reference to the flowchart in Fig. 3. It is assumed that the organization response information storage unit 112 stores organization response information of a plurality of organizations. It is also assumed that the individual score table storage unit 113 stores individual score tables.

[0101] (Step S301) Processing unit 13 judges whether it is time to calculate a score, etc. If it is time to calculate a score, etc., proceed to step S302, and if it is not time to calculate a score, etc., proceed to step S312. The timing for calculating a score, etc., is, for example, when an instruction is input from a user or administrator, when a predetermined timing is reached, when two or more pieces of organizational response information are received and stored in organizational response information storage unit 112, etc.

[0102] (Step S302) The processing unit 13 assigns 1 to a counter i.

[0103] (Step S303) The processing unit 13 judges whether or not the i-th attribute value exists. If the i-th attribute value exists, the process proceeds to step S304, and if the i-th attribute value does not exist, the process returns to step S301. Note that the i-th attribute value is the i-th attribute value of the organization, and is the attribute value for which the influence is to be calculated.

[0104] (Step S304) The processing unit 13 acquires two or more pieces of organization answer information, etc., paired with the i-th attribute value, from the organization answer information storage unit 112. The organization answer information, etc., are, for example, an organization identifier and organization answer information. The organization answer information, etc., are, for example, an organization identifier, the i-th attribute value, and organization answer information.

[0105] (Step S305) The processing unit 13 assigns 1 to the counter j.

[0106] (Step S306) The processing unit 13 judges whether or not the jth organization identifier is present in the two or more pieces of organization response information etc. acquired in step S304. If the jth organization identifier is present, the process proceeds to step S307, and if the jth organization identifier is not present, the process proceeds to step S310.

[0107] (Step S307) The processing unit 13 acquires organization response information paired with the j-th organization identifier.

[0108] (Step S308) The processing unit 13 calculates various scores corresponding to the j-th organization identifier using the organization response information acquired in step S307. Note that the calculation of the score is equivalent to the acquisition of the score. The score calculation process will be described with reference to the flowchart of FIG.

[0109] (Step S309) The processing unit 13 increments the counter j by 1. The process returns to step S306.

[0110] (Step S310) The impact degree obtaining unit 133 calculates the impact degree of each item. The impact degree calculation process will be described with reference to the flowchart of FIG.

[0111] (Step S311) The processing unit 13 increments the counter i by 1. The process returns to step S303.

[0112] (Step S312) The accepting unit 12 judges whether or not an instruction to output scores, etc. has been accepted. If an instruction to output scores, etc. has been accepted, the process proceeds to step S313, and if an instruction to output scores, etc. has not been accepted, the process returns to step S301.

[0113] (Step S313) The processing unit 13 acquires the organization identifier included in the score or the like output instruction accepted in step S312.

[0114] (Step S314) The processing unit 13 acquires an attribute value of an organization that is paired with the organization identifier acquired in step S313.

[0115] (Step S315) Processing unit 13 acquires two or more item scores paired with the organization identifier acquired in step S313, and item identifiers paired with the item scores, from storage unit 11 or a buffer (not shown). That is, processing unit 13 acquires pairs of item identifiers and item scores for the number of items.

[0116] (Step S316) The processing unit 13 obtains the total score paired with the organization identifier obtained in step S313 from the storage unit 11 or a buffer (not shown).

[0117] (Step S317) Processing unit 13 acquires two or more impact degrees paired with the attribute value acquired in step S314, and item identifiers paired with the impact degrees from storage unit 11 or a buffer (not shown). That is, processing unit 13 acquires pairs of item identifiers and impact degrees for the number of items.

[0118] (Step S318) The processing unit 13 configures information to be output from the information acquired in steps S315, S316, and S317.

[0119] (Step S319) The output unit 14 outputs the information configured in step S318. Return to step S301. Note that output here usually means transmission to the terminal device 2 that has transmitted the instruction to output the score or the like. The output information includes at least a pair of item identifiers and impact degrees. It is also preferable that the output information includes a pair of item identifiers and item scores, and a total score.

[0120] In the flowchart of FIG. 3, the route by which the organization response information is accumulated in the organization response information storage unit 112 does not matter.

[0121] In addition, in the flowchart of Fig. 3, the impact degree is obtained for each attribute value of the organization. However, it goes without saying that the impact degree of each item may be obtained using the organization response information of all organizations, regardless of the attribute value of the organization.

[0122] Furthermore, in the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt for ending the process occurs.

[0123] Next, an example of the score calculation process in step S308 will be described with reference to the flowchart in FIG.

[0124] (Step S401) The item score obtaining section 131 assigns 1 to a counter i.

[0125] (Step S402) The item score acquisition unit 131 judges whether or not the item identifier of the i-th individual item exists in the organization response information acquired in step S307. If the item identifier of the i-th individual item exists, the process proceeds to step S403, and if not, the process proceeds to step S409.

[0126] (Step S403) The item score obtaining unit 131 obtains satisfaction level information of all members that is paired with the item identifier of the i-th individual item from the organization response information obtained in step S307.

[0127] (Step S404) The item score acquiring unit 131 statistically processes the satisfaction level information acquired in step S403 to calculate statistical satisfaction level information. Note that here, the item score acquiring unit 131 calculates the statistical satisfaction level information that is, for example, the average value of the satisfaction level information acquired in step S403. Then, the item score acquiring unit 131 accumulates the calculated statistical satisfaction level information in the storage unit 11 or a buffer (not shown) in a pair with the item identifier of the i-th individual item.

[0128] (Step S405) The item score obtaining unit 131 obtains all member expectation information that is paired with the item identifier of the i-th individual item from the organization response information obtained in step S307.

[0129] (Step S406) The item score acquiring unit 131 statistically processes the expectation information acquired in step S405 to calculate statistical expectation information. Here, the item score acquiring unit 131 calculates statistical expectation information that is, for example, the average value of the expectation information acquired in step S403. Then, the item score acquiring unit 131 accumulates the calculated statistical expectation information in the storage unit 11 or a buffer (not shown) in a pair with the item identifier of the i-th individual item.

[0130] (Step S407) The item score acquisition unit 131 uses the statistical satisfaction information and the statistical expectation information to acquire the item score of the i-th individual item of the organization of interest. The item score acquisition unit 131 applies the statistical satisfaction information and the statistical expectation information to the individual score table in the individual score table storage unit 113 to acquire the item score of the i-th individual item. Then, the item score acquisition unit 131 accumulates the acquired item score in storage unit 11 or a buffer (not shown) in a pair with the item identifier of the i-th individual item.

[0131] (Step S408) The item score obtaining section 131 increments the counter i by 1. The process returns to step S402.

[0132] (Step S409) The item score obtaining section 131 assigns 1 to a counter j.

[0133] (Step S410) The item score acquisition unit 131 judges whether or not the item identifier of the jth comprehensive item is present in the organization response information acquired in step S307. If the item identifier of the jth comprehensive item is present, the process proceeds to step S411, and if not, the process proceeds to step S414.

[0134] (Step S411) The item score obtaining unit 131 obtains satisfaction level information of all members that is paired with the item identifier of the j-th comprehensive item from the organization response information obtained in step S307.

[0135] (Step S412) The item score acquiring unit 131 statistically processes the satisfaction level information acquired in step S411 to calculate statistical satisfaction level information. Note that here, the item score acquiring unit 131 calculates the statistical satisfaction level information that is, for example, the average value of the satisfaction level information acquired in step S411. Then, the item score acquiring unit 131 accumulates the calculated statistical satisfaction level information in the storage unit 11 or a buffer (not shown) in a pair with the item identifier of the j-th comprehensive item.

[0136] (Step S413) The item score obtaining section 131 increments the counter j by 1. The process returns to step S410.

[0137] (Step S414) The total score acquiring unit 132 acquires all the item scores of the individual items from the storage unit 11 or a buffer (not shown). Note that the item scores of the individual items are the scores acquired in step S407.

[0138] (Step S415) The overall score acquiring unit 132 acquires an overall score of the individual items from all the item scores acquired in step S414. For example, the overall score acquiring unit 132 calculates an average value of all the item scores acquired in step S414, and acquires the average value as an overall score of the individual items.

[0139] (Step S416) The overall score acquiring unit 132 acquires statistical satisfaction level information of all items in the overall item from the storage unit 11 or a buffer (not shown).

[0140] (Step S417) The overall score acquiring unit 132 statistically processes the statistical satisfaction information of all items acquired in step S416, and calculates the statistical satisfaction information of the overall item. For example, the overall score acquiring unit 132 calculates the average value of the statistical satisfaction information of all items acquired in step S416, and acquires it as the statistical satisfaction information of the overall item.

[0141] (Step S418) The overall score acquisition unit 132 calculates a provisional overall score from the overall scores of the individual items acquired in step S415 and the statistical satisfaction information of the overall items acquired in step S417. The overall score acquisition unit 132 calculates the provisional overall score, for example, by the formula "provisional overall score = α × statistical satisfaction information of the overall items + β × overall score of the individual items."

[0142] (Step S419) The total score acquiring unit 132 acquires correlation information regarding the correlation between the set of satisfaction level information and the set of expectation level information from the set of satisfaction level information of all the individual items and the set of expectation level information of all the individual items.

[0143] (Step S420) The total score acquisition unit 132 adjusts the provisional total score acquired in step S418 using the correlation information acquired in step S419 to acquire a total score. Return to upper level processing. The total score acquisition unit 132 acquires a total score such that the greater the degree of correlation indicated by the correlation information, the higher the total score becomes.

[0144] Next, an example of the influence degree calculation process in step S310 will be described with reference to the flowchart in FIG.

[0145] (Step S501) The impact degree obtaining unit 133 assigns 1 to a counter i.

[0146] (Step S502) The impact degree acquisition unit 133 judges whether or not the item identifier of the i-th individual item exists in the two or more pieces of organization response information, etc. acquired in step S304. If the item identifier of the i-th individual item exists, the process proceeds to step S503, and if the item identifier of the i-th individual item does not exist, the process returns to the upper process.

[0147] (Step S503) The influence degree obtaining unit 133 obtains item scores of a plurality of organizations that are paired with the item identifier of the i-th individual item from the two or more pieces of organization response information obtained in step S304.

[0148] (Step S504) The influence degree obtaining unit 133 obtains a total score of a plurality of organizations from the two or more pieces of organization response information obtained in step S304.

[0149] (Step S505) The impact degree acquiring unit 133 acquires an impact degree indicating the degree of correlation between the multiple item scores acquired in step S503 and the multiple overall scores acquired in step S504. Then, the impact degree acquiring unit 133 accumulates the acquired impact degree in the storage unit 11 or a buffer (not shown) in combination with the item identifier of the i-th individual item.

[0150] (Step S506) The impact degree obtaining unit 133 increments the counter i by 1. The process returns to step S502.

[0151] Next, the operation of the terminal device 2 will be described. The terminal reception unit 22 of the terminal device 2 receives various instructions, information, etc. Next, the terminal processing unit 23 composes the instructions, etc. received by the terminal reception unit 22 into instructions, etc. to be transmitted. The terminal transmission unit 24 transmits the instructions, etc. composed by the device processing unit 23 to the information processing device 1. Then, the terminal reception unit 25 receives information from the information processing device 1 in response to the transmission of the instructions, etc. Next, the device processing unit 23 composes the information received by the terminal reception unit 25 into data to be output. Next, the terminal output unit 26 outputs the information composed by the device processing unit 23.

[0152] A specific operation of the information system A in this embodiment will be described below. A conceptual diagram of the information system A is shown in FIG.

[0153] Assume now that the item information management table shown in FIG. 6 is stored in the item information storage unit 111. The item information management table is a table that manages a large number of item information items indicating the items of a questionnaire for members (employees in this case) of an organization (a company in this case). The item information here has "Question No.", "Type," "Factor," "Item," "Question: Expectation," and "Question: Satisfaction." "Question No." is an ID that identifies a question, and is an example of an item identifier. "Type" is information indicating the type of item, and here, it can be either a comprehensive item or an individual item. "Factor" is an intermediate concept of an item, and may be called an object. "Item" is information indicating the contents of an item. Incidentally, "Item" may be considered as an item identifier. "Question: Expectation" is a question for acquiring expectation information. "Question: Satisfaction" is a question for acquiring satisfaction information.

[0154] In addition, the organization response information storage unit 112 stores, for example, the organization response information shown in FIG. 7. In the organization response information storage unit 112, two or more pieces of organization response information are stored. FIG. 7 shows the organization response information of an organization identified by the organization identifier "Company A". Also, 701 is the member response information of one employee of the organization identified by the organization identifier "Company A". The organization response information of the organization identified by the organization identifier "Company A" includes the member response information of two or more employees. The member response information has a large number (63 or more) of records having "item identifier", "expectation information", and "satisfaction information". Note that the records of items with item identifiers 1 to 4 are records of overall items and do not have expectation information. The expectation information and satisfaction information constituting the member response information of 701 are information acquired from the answers given by the employee committee members to "question: expectation" and "question: satisfaction" in the item information management table shown in FIG. 6. In this case, such answers are answers with any of the natural numbers from 1 to 5. In addition, here, when the expectation information for "Question: Expectation" is 1, the expectation level is the lowest, and when it is 5, the expectation level is the highest. When the satisfaction information for "Question: Satisfaction" is 1, the satisfaction level is the lowest, and when it is 5, the satisfaction level is the highest. Also, it is assumed that the attribute value of the industry of the organization identified by the organization identifier "Company A" is "manufacturer."

[0155] Furthermore, the individual score table storage unit 113 stores an individual score table shown in FIG. 8. The individual score table manages two or more records having "expectation information", "satisfaction information", and "score". "Expectation information" is, for example, an average value of expectation information. "Expectation information" may be, for example, information indicating a range of the average value of expectation information. "Expectation value 1", "expectation value 2", ... "expectation value N", which are attribute values ​​of "expectation information", are information of specific values ​​or ranges. Also, "satisfaction information" is, for example, an average value of satisfaction information. "Satisfaction information" may be, for example, information indicating a range of the average value of satisfaction information. "Satisfaction value 1", "satisfaction value 2", ... "satisfaction value N", which are attribute values ​​of "satisfaction information", are information of specific values ​​or ranges. "Score" is information indicating an item score here. "Score 1", "Score 2", ... "Score N", which are attribute values ​​of "score", are specific values.

[0156] In this situation, it is assumed that the administrator of the information processing device 1 inputs an instruction to calculate the score, etc. Then, the reception unit 12 receives the instruction to calculate the score, etc. Next, the processing unit 13 determines that it is time to calculate the score, etc.

[0157] Next, the processing unit 13 acquires two or more pieces of organization response information paired with the attribute value "manufacturer" from the organization response information management table (FIG. 7).

[0158] Next, the processing unit 13 calculates the item score of each item and the total score for the organization identifiers paired with the attribute value "manufacturer", including "Company A", using the organization response information paired with the organization identifier. The calculation of the item score and the total score will be described below using "Company A" as an example.

[0159] That is, the item score acquisition unit 131 acquires member response information paired with the organization identifier "Company A". Then, the item score acquisition unit 131 acquires satisfaction information of all members for each individual item from the acquired member response information. Next, the item score acquisition unit 131 acquires the average value of the acquired satisfaction information as statistical satisfaction information for each individual item. Also, the item score acquisition unit 131 acquires the average value of the acquired expectation information as statistical expectation information for each individual item. Next, the item score acquisition unit 131 applies the statistical satisfaction information and the statistical expectation information to the individual score table of FIG. 8 to acquire an item score for each individual item. Then, the item score acquisition unit 131 accumulates the acquired item scores in a buffer paired with the item identifier of each individual item.

[0160] Next, the item score acquisition unit 131 acquires satisfaction information of all members corresponding to the item identifier of each overall item.Then, the item score acquisition unit 131 calculates the average value of the acquired satisfaction information for each overall item and acquires the average value as statistical satisfaction information.The item score acquisition unit 131 then stores the statistical satisfaction information of each overall item in a buffer in pairs with the item identifier of each overall item.

[0161] Next, the overall score acquisition unit 132 acquires an overall score of all the item scores of all the individual items. Here, the overall score acquisition unit 132 acquires the average value of all the item scores of all the individual items.

[0162] Next, the overall score acquisition unit 132 statistically processes the statistical satisfaction information of all items to calculate the statistical satisfaction information of the overall item. Here, the overall score acquisition unit 132 acquires the average value of the statistical satisfaction information of all items as the statistical satisfaction information of the overall item.

[0163] Next, the overall score acquisition unit 132 calculates a provisional overall score according to the formula "provisional overall score=0.7×statistical satisfaction information of overall item+0.3×overall score of individual items."

[0164] Next, the total score acquiring section 132 acquires correlation information regarding the correlation between the set of satisfaction level information and the set of expectation level information from the set of satisfaction level information for all individual items and the set of expectation level information for all individual items.

[0165] Next, the overall score acquisition unit 132 adjusts the acquired provisional overall score using the acquired correlation information. The overall score acquisition unit 132 also acquires a standard deviation using the adjusted provisional overall score of other companies. This standard deviation is the overall score. Here, it is assumed that the overall score acquisition unit 132 calculates the overall score of the organization identified by the organization identifier "Company A" to be "68.0". Then, the overall score acquisition unit 132 stores the overall score "68.0" in the buffer in association with the organization identifier "Company A". The calculation of the adjusted provisional overall score of other companies is performed in the same manner as the calculation of the value for "Company A".

[0166] The above process is then performed on other organization response information that is paired with the attribute value "manufacturer". Then, for all organization response information (organizations) that is paired with the attribute value "manufacturer", the item scores of each individual item and the total score are calculated and are temporarily stored in at least a buffer.

[0167] Next, the impact acquisition unit 133 acquires, for each individual item, an item score paired with an organization identifier paired with the attribute value "manufacturer" from the buffer. That is, the impact acquisition unit 133 acquires item scores from the buffer in the same number as the number of organization identifiers. Next, the impact acquisition unit 133 acquires, from the buffer, a total score paired with an organization identifier paired with the attribute value "manufacturer".

[0168] Next, the influence degree acquisition unit 133 calculates the correlation degree between the set of the acquired multiple item scores and the set of the acquired multiple overall scores for each individual item. The correlation degree may be a correlation coefficient. Then, the influence degree acquisition unit 133 stores the influence degree, which is the correlation degree, in a buffer, pairing it with the item identifier for each individual item. Note that such information has a structure of "item identifier, influence degree", and here, for example, it is assumed that it is "influence within the industry, 0.84", "stability of customer base, 0.78", "topicality and recognition, 0.65", "soundness of financial situation, 0.86", "transmission and communication of philosophy, 0.50", "on-site penetration of philosophy, 0.82", "transmission and communication of strategic objectives, 0.78", "satisfaction with strategic objectives, 0.50", etc.

[0169] The above process is also performed on organization response information corresponding to other attribute values ​​(for example, "trading company" or "bank").

[0170] Then, item scores and total scores are accumulated for each company, and in this case, the impact of each item is accumulated for each attribute value of the organization.

[0171] In this situation, it is assumed that the user of "Company A" inputs an instruction to output scores, etc., to the terminal device 2. Next, the terminal device 2 accepts the instruction to output scores, etc., and configures an instruction to output scores, etc., having the organization identifier "Company A". Then, the terminal device 2 transmits the instruction to output scores, etc., to the information processing device 1.

[0172] Next, the reception unit 12 of the information processing device 1 receives an instruction to output scores, etc. from the terminal device 2. Next, the processing unit 13 acquires the organization identifier "Company A" contained in the instruction to output scores, etc. Next, the processing unit 13 acquires the attribute value "manufacturer" of the organization paired with the organization identifier "Company A".

[0173] Next, the processing unit 13 acquires two or more item scores paired with the organization identifier "Company A" and item identifiers paired with the item scores from the storage unit 11 or a buffer (not shown).

[0174] Next, the processing unit 13 obtains the total score "68.0" paired with the organization identifier "Company A" from the storage unit 11 or a buffer (not shown).

[0175] Next, the processing unit 13 acquires, from the storage unit 11 or a buffer (not shown), an impact degree of 2 or more paired with the acquired attribute value "manufacturer" and an item identifier paired with the impact degree.

[0176] Next, the processing unit 13 composes information to be output from the acquired information. Then, the output unit 14 outputs the composed information. An example of such output is shown in FIG. 9. 901 in FIG. 9 is the overall score of Company A, "68.0". Also, 902 is the item score of each overall item. Here, the overall items are the company, the boss, the work, and the workplace. Also, 903 is the item score and the influence of the individual item. Note that FIG. 10 is an enlarged view of the area of ​​903. In FIG. 10, 1001 is the average value of the expectation information of the individual items. 1002 is the average value of the satisfaction information of the individual items. The column of 1003 is the influence of each individual item. Note that, as can be seen from 1004 to 1006 in FIG. 10, the influence output unit 143 outputs the influence in association with each item so that the classification of the influence can be visually distinguished. That is, here, for example, the darkest background color is used when the influence is 0.8 or more, a normal background color is used when the influence is 0.6 or more and less than 0.8, and a light background color is used when the influence is less than 0.6, thereby outputting the influence classification so as to be visually distinguishable. Note that the method of outputting the influence classification so as to be visually distinguishable is not limited to the background color, and it goes without saying that various attribute values ​​such as font, size, etc. can be considered.

[0177] As described above, according to this embodiment, it is possible to easily grasp items that are important to an organization.

[0178] Furthermore, according to this embodiment, it is possible to obtain the degree of influence for each attribute value of an organization, and it is possible to grasp important items with high accuracy.

[0179] Furthermore, according to the present embodiment, it is possible to provide an information processing device 1 that can acquire an engagement score and be used as a so-called engagement device.

[0180] According to the specific example of this embodiment, the influence degree is obtained for each attribute value of the organization. However, the influence degree for each item may be obtained regardless of the attribute value of the organization. The technology for obtaining the influence degree for each attribute value of the organization focuses on the fact that the characteristics of a company differ for each attribute value of the organization. This technology is extremely useful in engagement, for example, in which employees voluntarily exert their own capabilities toward achieving the organization's goals, leading to growth for both the employees and the organization.

[0181] Needless to say, the above-mentioned "satisfaction" in the questionnaire item also includes similar terms such as satisfaction and current situation, and the above-mentioned "expectations" also includes similar terms such as degree of expectation, importance, significance, etc.

[0182] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to the other embodiments in this specification. The software that realizes the information processing device 1 in this embodiment is a program such as the following. That is, this program is a program for making a computer that can access a recording medium having an organization response information storage unit in which organization response information including two or more item response information, for each of two or more members belonging to an organization, containing item identifiers that identify items related to the organization and response information including satisfaction information that is a response regarding the degree of satisfaction of the members with respect to the items, function as an item score acquisition unit that statistically processes the response information for each of the members for each of the two or more items included in each of the two or more organization response information and acquires item scores that are the scores of the items for each organization and for each item, a total score acquisition unit that acquires a total score that is a total score for each of the two or more organization response information using the two or more item scores for each of the two or more organization response information, an influence acquisition unit that acquires the influence of the item score of each item on the total score for each item using the item scores of each of the two or more items of each of the two or more organizations and the total score of each of the two or more organizations, and an influence output unit that outputs the influence acquired by the influence acquisition unit in association with each item. Note that such a program can be said to be a program that realizes an engagement system.

[0183] (Embodiment 2) In this embodiment, an information system including a score prediction device that predicts a score using the correspondence relationship and daily information for the period for which the score is predicted will be described. Note that the correspondence relationship is a correspondence relationship between past scores (e.g., engagement scores) based on responses to a questionnaire for members of an organization and daily information corresponding to the period corresponding to the questionnaire. Also, the daily information is information about members, and details will be described later.

[0184] In addition, in this embodiment, an information system including a score prediction device that predicts a score using a machine learning algorithm will be described.

[0185] In addition, in this embodiment, an information system including a score prediction device that predicts a score using a correspondence table acquired by data mining will be described.

[0186] Furthermore, in this embodiment, an information system including a score prediction device that predicts a score using an arithmetic expression will be described.

[0187] FIG. 11 is a conceptual diagram of an information system B in this embodiment. The information system B includes a score prediction device 3 and one or more terminal devices 4. The score prediction device 3 is a device that acquires the score of an organization. Here, the score is, for example, an engagement score. The score is, for example, a score related to the motivation of members of the organization. The score is, for example, a score related to the members' evaluation of the organization. The score may be a numerical value or information indicating a level (for example, A, B, C). In addition, the score here is usually the above-mentioned overall score, but it may be a single item score or a representative value (for example, average, median) of two or more item scores.

[0188] The score prediction device 3 here is a so-called server device. The score prediction device 3 is, for example, a cloud server or an ASP server, but the type and installation location are not important. The terminal device 4 is a terminal used by a user who wants to obtain a score. The terminal device 4 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, a so-called personal computer, etc., and the type is not important.

[0189] Fig. 12 is a block diagram of information system B. Fig. 13 is a block diagram of score prediction device 3 constituting information system B.

[0190] The score prediction device 3 includes a storage unit 31, a reception unit 32, a processing unit 33, and an output unit .

[0191] The storage unit 31 includes an organization information storage unit 311 , a daily information storage unit 312 , a teacher score storage unit 313 , and a correspondence relationship information storage unit 314 .

[0192] The daily information storage section 312 includes a work input information storage means 3121, a communication information storage means 3122, an attendance status information storage means 3123, a work result information storage means 3124, a stress information storage means 3125, a measurement information storage means 3126, an image storage means 3127, an audio storage means 3128, and a lifestyle information storage means 3129.

[0193] The processing unit 33 includes a score acquiring unit 331. The score acquiring unit 331 includes a correspondence acquiring means 3311 and a score acquiring means 3312.

[0194] The output unit 34 includes a score output unit 341 .

[0195] The terminal device 4 includes a terminal storage unit 41, a terminal reception unit 42, a terminal processing unit 43, a terminal transmission unit 44, a terminal reception unit 45, and a terminal output unit 46.

[0196] Various types of information are stored in the storage unit 31 constituting the score prediction device 3. The various types of information are, for example, organizational information described later, daily information described later, teacher scores described later, correspondence relationship information described later, and two or more member information. The various types of information in the storage unit 31 may be information received from the terminal device 4, may be information received from a server (not shown), or may be information acquired and accumulated by the score prediction device 3.

[0197] Member information is information about members. A member has a member identifier that identifies the member. A member has one or more member attribute values. A member attribute value is an attribute value of a member. One or more member attribute values ​​are, for example, name, age, position, year of joining the company, email address, telephone number, and member identifier of a superior. Member information such as management, administrative positions, and superiors is also stored in storage unit 31. Two or more pieces of member information may also be stored in a member information storage unit not shown.

[0198] The organization information storage unit 311 stores one or more pieces of organization information. The organization information is information about an organization. For example, an organization is an organization that performs some kind of work. For example, an organization is a so-called company, a personal business, a local government, etc., and is broadly understood. The organization may be an organization within a company. That is, an organization may be, for example, a department, workplace, section, or division within a company. The organization information has, for example, an organization identifier and one or more organization attribute values. The organization identifier is information that identifies an organization. The organization identifier is, for example, an organization name, an ID, or the like. The organization attribute value is an attribute value of an organization. For example, the organization attribute value is an industry identifier that indicates the industry of the organization (for example, bank, apparel, manufacturer, etc.), a scale identifier that classifies the size of the organization (for example, large company, small and medium-sized enterprise, personal business, etc.), a region identifier that indicates the region of the head office, the number of employees, a classification based on the number of employees (for example, five levels from 1 to 5), the number of business establishments, a classification based on the number of business establishments (for example, three levels: "large", "medium", and "small").

[0199] The daily information storage unit 312 stores one or more pieces of daily information. Daily information is information about members. Daily information is information that can be acquired in the daily life of members. Daily information does not include answers to questions such as questionnaires. Daily information is, for example, a file, but may also be a record in a database. The data structure of the daily information is not important. Note that a member is someone who belongs to an organization. A member is usually someone who works for an organization. A member is, for example, a full-time employee, a part-time worker, a part-timer, etc. Daily information is, for example, associated with an organization identifier. It is preferable that the daily information is associated with a member identifier. Daily information is usually associated with time information. Time information is information that specifies the time corresponding to the daily information. Time information is, for example, information that specifies the time when the daily information was acquired, or information that specifies the time when the daily information was measured. Time information is usually information that specifies a point in time, but may also be period information that specifies a period. Time information is, for example, information that specifies the date and time, information that specifies the day, or information that specifies the month.

[0200] Note that one piece of information being associated with other pieces of information may mean that one piece of information includes the other pieces of information, that one piece of information and the other pieces of information are linked, or that the other pieces of information include one piece of information. In other words, it is sufficient if other pieces of information can be obtained from one piece of information, or one piece of information can be obtained from other pieces of information.

[0201] The daily information is, for example, business information and health information. Business information is information about the business performed by the member. Health information is information about the health of the member.

[0202] Business information is, for example, input information, attendance status information, and business result information. Input information is information entered by members. Input information is, for example, information entered by members into devices used in business (e.g., personal computers, tablet terminals, servers, etc.). Input information is, for example, business input information and communication information. Details of business input information and communication information will be described later. Details of attendance status information and business result information will also be described later.

[0203] The health information is information relating to the health of the member. The health information is, for example, biological information and lifestyle information. The biological information is information relating to the member's body. The biological information is, for example, stress information and measurement information. The details of the stress information and the measurement information will be described later. The details of the lifestyle information will also be described later.

[0204] The everyday information used to obtain the correspondence is called teacher everyday information, and the everyday information used in the score prediction process is called predicted everyday information.

[0205] The teacher daily information is, for example, teacher work information and teacher health information. The teacher work information is, for example, teacher input information, teacher attendance status information and teacher work result information. The teacher input information is, for example, teacher work input information and teacher communication information. The teacher health information is, for example, teacher biological information and teacher lifestyle information. The teacher biological information is, for example, teacher stress information and teacher measurement information.

[0206] The predicted daily information is, for example, predicted work information and predicted health information. The predicted work information is, for example, predicted input information, predicted attendance status information, and predicted work result information. The predicted input information is, for example, predicted work input information and predicted communication information. The predicted health information is, for example, predicted biological information and predicted lifestyle information. The predicted biological information is, for example, predicted stress information and predicted measurement information.

[0207] One or more pieces of task input information are stored in the task input information storage means 3121 constituting the daily information storage unit 312. The task input information is information related to input for the execution of a member's own task.

[0208] The business input information is, for example, an operation log of a device used by a member for business, a program created by a member, a document created by a member, or a daily report created by a member. The business input information is associated with time information. The business input information is associated with, for example, an organization identifier or a member identifier. The business input information may be information that can be acquired from, for example, an operation log, a program, a document, or a daily report. The information that can be acquired from an operation log is, for example, an operation log attribute value. The operation log attribute value is, for example, an operation amount indicating the amount of operation logs, the number of types acquired using the operation log, the average number of operation logs per day, and change information on the number of operation logs. The change information on the number of operation logs is, for example, information indicating whether the number of operation logs per day is on the increase, whether the number of operation logs per day is on the decrease, and the variation in the number of operation logs per day (for example, variance). The information that can be acquired from a program is, for example, a program attribute value. The program attribute value is, for example, the number of steps of a program, the average number of steps of a program per day, and change information on the number of steps of a program created per day. The information on the change in the number of program steps is, for example, information indicating whether the number of program steps per day is on the rise, whether the number of program steps per day is on the fall, and the variation in the number of program steps per day (for example, variance). Information that can be acquired from a document is, for example, a document attribute value. The document attribute value is, for example, the amount of document data, the average amount of document data per day, and information on the change in the amount of document data created per day. The information on the change in document data amount is, for example, information indicating whether the amount of document data per day is on the rise, whether the amount of document data per day is on the fall, and the variation in the amount of document data per day (for example, variance). Information that can be acquired from a daily report is, for example, a daily report attribute value. The daily report attribute value is, for example, the amount of daily report data, the average amount of daily report data per day, information on the change in data amount, the number of negative words appearing in a daily report for a specified period, the rate of appearance of negative words in a daily report for a specified period, the number of positive words appearing in a daily report for a specified period, and the rate of appearance of positive words in a daily report for a specified period.The information on changes in data volume is, for example, information indicating whether the data volume of daily reports per day is on an increasing trend, whether the data volume of daily reports per day is on a decreasing trend, and information indicating the variation in the data volume of daily reports from day to day (for example, variance). Note that the operation log attribute value, the program attribute value, the document attribute value, and the daily report attribute value are included in the business input attribute value, which is the attribute value of business input information.

[0209] The business input information in the business input information storage means 3121 is, for example, information received from the terminal device 4 or a server (not shown). The operation log attribute value, the program attribute value, the document attribute value, and the daily report attribute value are, for example, information acquired by the processing unit 33 or information received from the terminal device 4 or a server (not shown).

[0210] The communication information storage means 3122 stores one or more pieces of communication information. The communication information is associated with time information. The communication information is associated with, for example, an organization identifier and a member identifier. The time information here is information that specifies the time when the communication information was input, transmitted, or received. The organization identifier here is the identifier of the organization to which the member who inputted the communication information belongs. The member identifier here is the identifier of the member who inputted the communication information. The communication information is associated with, for example, the member identifier of one or more members who are the other party of communication.

[0211] The communication information is information about communication between two or more members. For example, the communication information is chat information entered by a member into a chat system (or a chat app), SNS information entered by a member into an SNS, emails created by a member, and text information obtained by converting voices uttered by members during video conferences into text. Note that the technology for converting voices into text by speech recognition processing is a well-known technology, and a description thereof will be omitted.

[0212] The communication information may include communication attribute values ​​that can be obtained from chat information, SNS information, e-mail, or text information. The communication attribute values ​​are the amount of data of the communication information for a predetermined period, the number of occurrences of negative words in the communication information for a predetermined period, the occurrence rate of negative words in the communication information for a predetermined period, the number of occurrences of positive words in the communication information for a predetermined period, the occurrence rate of positive words in the communication information for a predetermined period, and the response rate of members to messages from management, managers, or superiors. The predetermined period is a period determined in advance.

[0213] The communication information in the communication information storage means 3122 is, for example, information received from the terminal device 4 or a server not shown. The communication attribute value is, for example, information acquired by the processing unit 33, or information received from the terminal device 4 or a server not shown.

[0214] The attendance status information storage means 3123 stores one or more pieces of attendance status information. Attendance status information is information relating to attendance status. Attendance status information may be attendance information, or may be information that can be obtained from one or more pieces of attendance information. Attendance information has work days, work start times, and work end times. Attendance information includes, for example, information indicating annual leave days. Attendance information includes, for example, information indicating holiday work. Attendance information includes, for example, information of normal working hours. Attendance information includes, for example, information identifying normal work days.

[0215] The attendance status information is, for example, the number of annual holidays taken by the member, the number of overtime hours worked by the member, the number of days off worked by the member, etc. The attendance status information is associated with, for example, an organization identifier and a member identifier.

[0216] The attendance status information in the attendance status information storage means 3123 is, for example, information acquired by the processing unit 33, or information received from the terminal device 4 or a server (not shown).

[0217] The business result information storage means 3124 stores one or more business result information. Business result information is information on the results of business execution. Examples of business result information include team goal achievement rate, individual goal achievement rate, sales volume, and sales. Business result information is usually associated with an organization identifier and period information.

[0218] The task result information in the task result information storage means 3124 is, for example, information acquired by the processing unit 33, or information received from the terminal device 4 or a server (not shown).

[0219] The stress information storage means 3125 stores one or more pieces of stress information. Stress information is information related to the stress of a member. Stress information is a stress level indicating the degree of stress, and information indicating the presence or absence of stress. Stress information is, for example, the results of a stress check. Stress information is, for example, a response to a questionnaire for a member (for example, "Please select your stress level from 1 to 5"). Stress information is, for example, information obtained using other health information of the member (for example, heart rate, blood pressure), and information obtained using responses to two or more questions in a questionnaire for the member. Stress information is associated with, for example, an organization identifier and a member identifier.

[0220] The stress information in the stress information storage means 3125 is, for example, information acquired by the processing unit 33, or information received from the terminal device 4 or a server (not shown).

[0221] The measurement information storage means 3126 stores one or more pieces of measurement information. The measurement information is the measurement results of the member's living body. The measurement information is, for example, heart rate and blood pressure. The measurement information is, for example, (a) information acquired using a wearable terminal worn by the member or a measuring device not shown and transmitted to the score prediction device 3, or (b) information acquired using a wearable terminal worn by the member or a measuring device not shown and input by the member. The measurement information is associated with, for example, an organization identifier and a member identifier.

[0222] The measurement information in the measurement information storage means 3126 is, for example, information received from the terminal device 4, a wearable terminal (not shown), or a server (not shown).

[0223] The image storage means 3127 stores one or more images. The images are images of one or more members. For example, the images are images recorded from a video conference in which the members participate. The images are associated with, for example, an organization identifier and a member identifier.

[0224] The image in the image storage means 3127 is, for example, an image received from the terminal device 4 or a server (not shown), or an image acquired by the score prediction device 3.

[0225] The audio storage means 3128 stores one or more audios. The audio is audio uttered by a member. The audio is, for example, an image recorded during a video conference in which the member participates. The audio is associated with, for example, an organization identifier and a member identifier.

[0226] The voice in the voice storage means 3128 is, for example, a voice received from the terminal device 4 or a server (not shown), or a voice acquired by the score prediction device 3.

[0227] The life information storage means 3129 stores one or more pieces of life information. The life information is information related to the life of a member. For example, the life information is the amount of sleep time, whether or not alcohol was consumed, and the number of cigarettes smoked. For example, the life information is (a) information input by the member, (b) information automatically acquired by a sleep measuring device (not shown) and transmitted to the score prediction device 3, and (c) information automatically acquired by a sleep measuring device (not shown) and input by the member. The life information is associated with, for example, an organization identifier and a member identifier. Furthermore, the life information is associated with, for example, time information (for example, date).

[0228] The teacher score storage unit 313 stores one or more teacher scores. The teacher score is usually an organization score obtained using a plurality of organization response information indicating answers to questions for each of two or more members of the organization. However, the teacher score may be a score manually input by a user or a predicted score previously obtained by the score prediction device 3. The questions are, for example, questionnaire questions or questions in a questionnaire. The teacher score is, for example, a total score obtained by the information processing device 1 described in the first embodiment. The teacher score may be, for example, any item score obtained by the information processing device 1 described in the first embodiment. In addition, the teacher score may be a representative value (for example, an average value, a weighted average, a median value) of two or more members of a simpler questionnaire (for example, a questionnaire with one question such as "Please select the degree of satisfaction with the company from 1 to 5") than the questionnaire described in the first embodiment. In other words, the teacher score may be, for example, a score of an organization obtained using a plurality of organization response information indicating answers to questions for each of two or more members. The teacher score is associated with, for example, period information and an organization identifier. The period information associated with the teacher score is information on the period covered by the survey (e.g., one year, six months, three months). The period information may be time information that specifies when the survey was conducted. The organization identifier associated with the teacher score is the identifier of the organization whose score the teacher score indicates.

[0229] One or more pieces of correspondence information are stored in the correspondence information storage unit 314. The correspondence information is associated with, for example, an organization identifier. Also, the correspondence information is associated with, for example, time information or period information.

[0230] The correspondence information is information on the correspondence between one or more teacher characteristics and a teacher score. The correspondence information is, for example, information that specifies the correspondence between one or more teacher characteristics and a teacher score. The teacher characteristics are one or more pieces of teacher routine information, information acquired using one or more pieces of teacher routine information, or information acquired using one or more pieces of teacher routine information and one or more pieces of teacher routine information.

[0231] The teacher feature is information including one or more of the following: data amount information for specifying the data amount of the teacher input information, input frequency information for specifying the input frequency of the teacher input information, analysis information showing the analysis result of the teacher input information, and timing information for specifying the input timing of the teacher input information. Note that the analysis information is, for example, a task input attribute value and a communication attribute value.

[0232] The correspondence information includes, for example, (1) a learning model, (2) a correspondence table, and (3) an arithmetic expression. The correspondence information is acquired by, for example, the correspondence acquisition unit 3311 described later.

[0233] The learning model is data acquired by a learning process of machine learning. The learning model may be called a prediction model, a classification model, a learner, or a predictor. In machine learning, for example, algorithms such as deep learning, decision tree, random forest, SVR, and SVM can be used. In machine learning, multiple regression analysis, neural network, and the like can be used. In other words, any machine learning algorithm can be used. The learning model is information acquired by a machine learning algorithm using two or more pieces of teacher data having a teacher vector and a teacher score. The teacher vector is a vector having two or more teacher features as elements. In addition, the vector is a collection of two or more pieces of information, and its structure does not matter.

[0234] The correspondence table is a table having two or more pieces of correspondence information, which are pairs of teacher vectors and teacher scores. Note that the structure of the table is not limited.

[0235] The formula is (S = f(a, b, c, )) which takes two or more teacher features as input and outputs a teacher score. Note that a, b, c, etc. in the formula are teacher features, and S is the score (teacher score or predicted score).

[0236] The reception unit 32 receives various information and instructions. The various information and instructions are, for example, prediction instructions and daily information. A prediction instruction is an instruction to output a prediction score. A prediction instruction is, for example, The prediction instruction has a teacher target condition. The teacher target condition is information that identifies an organization identifier that pairs with the daily information that is the source of the teacher data. The teacher target condition is, for example, an organization identifier, a condition using the organization identifier. The prediction instruction has, for example, information that identifies the correspondence information to be used. The prediction instruction has, for example, teacher period information that identifies the period of the daily information that is the source of the teacher data.

[0237] Also, here, acceptance usually means receiving from terminal device 4, but it may also be understood as a concept including acceptance of information input from an input device such as a keyboard, mouse, or touch panel, and acceptance of information read from recording media such as an optical disk, magnetic disk, or semiconductor memory.

[0238] The processing unit 33 performs various types of processing. The various types of processing are, for example, processing performed by the score acquiring unit 331, the correspondence acquiring means 3311, and the score acquiring means 3312.

[0239] The processing unit 33, for example, stores the daily information accepted by the accepting unit 32 in the daily information storage unit 312. The processing unit 33, for example, stores the daily information accepted by the accepting unit 32 in the daily information storage unit 312 in association with the member identifier of the member corresponding to the daily information. Note that such a member identifier is, for example, the identifier of the member who inputted or transmitted the daily information, or a member identifier associated with the daily information.

[0240] The score acquisition unit 331 acquires a predicted score by using a correspondence relationship between the teacher score and one or more pieces of teacher routine information, and one or more pieces of predicted routine information. The predicted score is a score corresponding to one or more pieces of predicted routine information.

[0241] The score acquisition unit 331 acquires a predicted score, for example, by using the correspondence information in the correspondence information storage unit 314 and one or more pieces of predicted daily life information.

[0242] The score acquisition unit 331 may acquire features, for example, as follows. The features are teacher features used to acquire correspondence information, or predicted features used to acquire predicted scores. In addition, in the process of acquiring features by the score acquisition unit 331 described below, if the feature is a teacher feature, the correspondence acquisition means 3311 performs the process, and if the feature is a predicted feature, the score acquisition means 3312 performs the process. In addition, the features are, for example, daily information, or attribute values ​​of daily information that can be acquired from daily information (for example, an operation log attribute value, a program attribute value, a document attribute value, a daily report attribute value, etc.). In addition, a teacher vector is configured from two or more teacher features. A predicted vector is configured from two or more predicted features. In addition, it does not matter which information is used as a feature among the information acquired by the score acquisition unit 331 described below. The score acquisition unit 331 may use any one of two or more features to acquire correspondence information and to acquire a predicted score.

[0243] The score acquisition unit 331, for example, for each member, acquires an operation log corresponding to the time information of each day of the period to be processed from the task input information storage means 3121, acquires one or more types of operation log attribute values ​​from the operation log, and at least temporarily stores the one or more types of operation log attribute values ​​in the task input information storage means 3121. Note that the technology for acquiring task input attribute values ​​such as the data volume of information and the above-mentioned change information from task input information such as an operation log is a publicly known technology, so a detailed description will be omitted. Also, the operation log is, for example, a member's operation history such as login and logout dates and times of PCs operating in an organization, viewing and editing of various files, network connection, etc.

[0244] The score acquisition unit 331, for example, acquires from the business input information storage means 3121 a program that is paired with each member identifier for each member and corresponds to the time information for each day in the period to be processed, acquires one or more types of program attribute values ​​from the program, and stores the one or more types of program attribute values ​​in the business input information storage means 3121, at least temporarily.

[0245] The score acquiring unit 331 acquires, for example, the number of steps of one or more program files for each date information corresponding to the program file (for each day of creation).Then, the score acquiring unit 331 acquires, for example, the average number of steps of the program file for each day, information indicating the variation in the number of steps created for each day in the target period, and change information indicating whether the number of steps of the program created per day is on an increasing or decreasing trend.

[0246] The score acquisition unit 331, for example, for each member, acquires a document that is paired with each member identifier and corresponds to the time information for each day of the period to be processed from the business input information storage means 3121, acquires one or more types of document attribute values ​​from the document, and at least temporarily stores the one or more types of document attribute values ​​in the business input information storage means 3121. Note that a document is a written document that is a direct deliverable of the performance of work. A document is, for example, a design document in a technical department, or accounting information in an accounting department.

[0247] The score acquiring unit 331 acquires, for example, the data amount of one or more documents for each date information corresponding to the document (for each creation date).Then, the score acquiring unit 331 acquires, for example, the average data amount of the documents for each day, information indicating the variation in the data amount for each day in the target period, and change information indicating whether the data amount of documents created in one day is on an increasing or decreasing trend.

[0248] The score acquisition unit 331, for example, acquires from the business input information storage means 3121 a daily report that is paired with each member identifier and corresponds to the time information for each day in the period to be processed, acquires one or more types of daily report attribute values ​​from the daily report, and stores the one or more types of daily report attribute values ​​in the business input information storage means 3121, at least temporarily.

[0249] The score acquiring unit 331 acquires, for example, the amount of data of each of the one or more acquired daily reports. Then, the score acquiring unit 331 acquires, for example, the average value of the amount of data, information indicating the variation of the amount of data, and change information indicating whether the amount of data is increasing or decreasing. In addition, the score acquiring unit 331 performs, for example, morphological analysis on the one or more acquired daily reports, refers to a dictionary of negative words, and acquires the number of occurrences of negative words. In addition, the score acquiring unit 331 acquires, for example, the number of independent words in the one or more acquired daily reports, and acquires the appearance rate of negative words, which is the number of negative words in the number of independent words, and the appearance rate of positive words, which is the number of positive words in the number of independent words.

[0250] The score acquisition unit 331, for example, for each member, acquires communication information that is paired with each member identifier and corresponds to the time information of each day of the period to be processed from the communication information storage means 3122, and acquires, for example, one or more types of communication attribute values ​​of the communication information. The score acquisition unit 331, for example, acquires the amount of data of the acquired communication information. In addition, the score acquisition unit 331, for example, performs morphological analysis on the communication information, acquires one or more independent words, and acquires the number of negative words, which is the number of words present in a negative word dictionary (not shown) among the one or more independent words. In addition, the score acquisition unit 331, for example, performs morphological analysis on the communication information, acquires one or more independent words, and acquires the number of positive words, which is the number of words present in a positive word dictionary (not shown) among the one or more independent words. In addition, the score acquisition unit 331, for example, performs morphological analysis on the communication information, and acquires the ratio of the number of negative words and the ratio of the number of positive words among the number of acquired independent words.

[0251] The score acquisition unit 331, for example, for each member, obtains the sent e-mails that are paired with each member identifier and correspond to the time information of each day of the period to be processed, obtains the e-mails that cite the e-mails, judges whether the identifier (e.g., email address) of the sender of the e-mail that cites the e-mail is stored in the organization information storage unit 311 in association with the member identifier, and if it is stored, increments the counter of the replies to the message from the reply target person (management, manager, superior, etc.). In addition, the score acquisition unit 331 obtains the number of e-mails from the reply target person among the received e-mails that are paired with each member identifier and correspond to the time information of each day of the period to be processed. In more detail, for example, the score acquisition unit 331 obtains, for example, the member identifier of the superior, the member identifier of the management team, and the member identifier of the manager that are paired with the member identifier, obtains the email address of each member identifier from the collection of member information, and obtains the number of e-mails that are sent from the email address (the number of e-mails from the reply target person). Then, the score acquisition unit 331 calculates, for example, for each member, the response rate of the member to the message from the response target from the number of emails from the response target and the response counter value. Note that, for example, the storage unit 31 stores the identifier (e.g., email address) of the response target of each member in association with each member identifier.

[0252] The score acquisition unit 331, for example, pairs each member with each member identifier and acquires attendance information for each day of the period to be processed for each member. Then, the score acquisition unit 331 acquires attendance status information (e.g., number of annual leave days, number of overtime hours, number of holiday work days) for the period to be processed for each member. Note that the technology for acquiring the number of annual leave days, number of overtime hours, number of holiday work days, etc. from a collection of attendance information is a publicly known technology.

[0253] The score acquisition unit 331 reads out one or more pieces of business result information from the business result information storage means 3124 for each organization, for example.

[0254] The score acquisition unit 331 reads out from the storage unit 31, for example, one or more answers to a questionnaire that is paired with each member identifier for each member and corresponds to time information for the period to be processed. Next, the score acquisition unit 331 acquires stress information from, for example, one or more answers for each member. The content of the questionnaire and the acquisition algorithm of the stress information are not important. The score acquisition unit 331 acquires, for example, an answer to a question inquiring about a stress level. For example, the score acquisition unit 331 substitutes the answers to each of two or more questions about the stress level into an arithmetic formula for calculating the stress level to calculate the stress level. For example, the score acquisition unit 331 constructs a vector from the answers to each of two or more questions about the stress level, and acquires a stress level that is paired with the vector that is most similar to the vector. In this case, it is assumed that two or more pieces of correspondence information that are pairs of vectors and stress levels are stored in the storage unit 31.

[0255] The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires from the measurement information storage means 3126 the heart rate or / and blood pressure corresponding to the time information of the period to be processed. Next, the score acquisition unit 331 acquires, for example, for each member, stress information indicating a higher stress as the value of the heart rate or / and blood pressure is larger. The score acquisition unit 331 acquires, for example, the number of times the value of the heart rate or / and blood pressure is larger than a threshold, and acquires a stress level based on the number of times. Note that the stress level based on the number of times becomes higher as the number of times becomes larger. In addition, the score acquisition unit 331 acquires, for example, continuous times during which the value of the heart rate or / and blood pressure is larger than a threshold, and acquires a stress level based on the sum of the continuous times. Note that the stress level based on the sum of the continuous times becomes higher as the sum of the continuous times becomes larger.

[0256] The score acquiring unit 331, for example, for each member, acquires one or more pieces of measurement information that are paired with each member identifier and correspond to time information of the period to be processed from the measurement information storage means 3126. The score acquiring unit 331, for example, acquires a representative value (for example, an average value, a median value) of the measurement information for each type of measurement information.

[0257] The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires one or more images for a period to be processed from the image storage means 3127. Next, the score acquisition unit 331, for example, performs facial expression analysis processing on each of the one or more images, and acquires a facial expression identifier (for example, any of joy, anger, sadness, and happiness, normal, etc.). In addition, the score acquisition unit 331, for example, performs statistical processing on the acquired one or more facial expression identifiers, etc., and acquires the proportion of negative expressions (angry, crying, etc.). Note that such a proportion is an example of an attribute. In addition, the processing of performing facial expression analysis and acquiring a facial expression identifier is a publicly known technique.

[0258] The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires one or more sounds for a period to be processed from the sound storage means 3128. Next, the score acquisition unit 331, for example, acquires one or more types of features (e.g., spectrum information, speech interval, silent interval, etc.) from each of the one or more sounds, and acquires the characteristics of the sound using the features. For example, the score acquisition unit 331 acquires the ratio of speech time in a conference attended and an emotion identifier (laughing, neutral, angry, crying, etc.) using the features. Note that the technology for acquiring the features is a well-known technology, for example, LPC analysis, cepstrum analysis, etc.

[0259] The score acquiring unit 331, for example, for each member, pairs with each member identifier and reads out life information for a period to be processed from the life information storage means 3129. Then, the score acquiring unit 331 performs statistical processing on the life information for each member, for example, to acquire the characteristics of the life information. The characteristics of the life information are, for example, the average sleep time, the variance of the sleep time, the number of days when alcohol was consumed, the ratio of days when alcohol was consumed, and the average number of cigarettes smoked per day.

[0260] The score acquiring unit 331 may acquire the correspondence information using two or more pieces of teacher data having a teacher vector having two or more acquired teacher features and a teacher score. The teacher vector is information acquired by the score acquiring unit 331 through the above-mentioned process. The correspondence information may be stored in the correspondence information storage unit 314 in advance.

[0261] The score acquisition unit 331, for example, provides a learning model, which is correspondence information, and a prediction vector, which is a vector having two or more prediction features as elements, to a machine learning module, executes the module, performs prediction processing, and acquires a prediction score. Note that the prediction feature is predicted daily information or information that can be acquired from the predicted daily information. Also, the prediction vector is information acquired by the score acquisition unit 331 through the above-mentioned process. Also, the machine learning module may be, for example, Tiny_SVM, a TensorFlow function, a MicrosoftML function, or the like.

[0262] The score acquisition unit 331 acquires a predicted score, for example, by using a correspondence table that is correspondence information and a predicted vector having two or more predictive features as elements. More specifically, the score acquisition unit 331 searches the correspondence table for a teacher vector that is most similar to a predicted vector having two or more predictive features as elements, and acquires a teacher score paired with the teacher vector as a predicted score.

[0263] Furthermore, the score acquisition unit 331 determines, for example, from a correspondence table, two or more teacher vectors whose distance to a predictive vector having two or more predictive features as elements is close enough to satisfy a predetermined condition, and acquires a representative value of the teacher scores corresponding to the two or more teacher vectors. The representative value is, for example, an average value, a median value, or a weighted average. The weighted average is, for example, calculated by using a weight that is inversely proportional to the distance between the vectors. The predetermined condition is, for example, within a threshold value or less than the threshold value, or within the top N in terms of closeness (N is a natural number equal to or greater than 2), etc.

[0264] Moreover, the score acquiring unit 331 substitutes each of the two or more predictive features into an arithmetic expression, executes the arithmetic expression, and calculates a predicted score. Note that the arithmetic expression is stored in the correspondence relationship information storage unit 314.

[0265] The score acquisition unit 331 acquires a predicted score by using, for example, one or more pieces of teacher daily information including teacher task information and a corresponding relationship between the teacher score and one or more pieces of predicted daily information including predicted task information. Note that using the corresponding relationship usually means using the above-mentioned corresponding relationship information.

[0266] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher routine information including teacher input information and a teacher score, and one or more pieces of predicted routine information including predicted input information.

[0267] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher routine information including teacher work input information and a teacher score, and one or more pieces of predicted routine information including predicted work input information.

[0268] The score acquisition unit 331 acquires the predicted score using, for example, a correspondence relationship between one or more pieces of teacher daily routine information including teacher communication information and a teacher score, and one or more pieces of predicted daily routine information including predicted communication information.

[0269] The score acquisition unit 331 acquires, for example, one or more types of information among data amount information identifying the data amount of teacher input information, input frequency information identifying the input frequency of teacher input information, analysis information indicating the analysis result of teacher input information, and timing information identifying the timing of input of teacher input information, and acquires one or more types of information among data amount information of predicted input information, input frequency information of predicted input information, analysis information of predicted input information, and timing information of predicted input information, acquires correspondence information identifying the correspondence between one or more teacher features including one or more types of information acquired from the teacher input information and a teacher score, and acquires a predicted score using the correspondence information and a prediction vector having two or more prediction features including one or more types of information acquired from the predicted input information.

[0270] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher daily information including teacher attendance status information and a teacher score, and one or more pieces of predicted daily information including predicted attendance status information.

[0271] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher daily information including teacher work result information and a teacher score, and one or more pieces of predicted daily information including predicted work result information.

[0272] The score acquisition unit 331 acquires the predicted score using, for example, a correspondence relationship between one or more pieces of teacher daily routine information including teacher health information and a teacher score, and one or more pieces of predicted daily routine information including predicted health information.

[0273] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher routine information including teacher biological information and a teacher score, and one or more pieces of predicted routine information including predicted biological information.

[0274] The score acquisition unit 331 acquires the predicted score using, for example, a correspondence relationship between one or more pieces of teacher daily routine information including teacher stress information and a teacher score, and one or more pieces of predicted daily routine information including predicted stress information.

[0275] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher routine information including teacher measurement information and a teacher score, and one or more pieces of predicted routine information including predicted measurement information.

[0276] The score acquisition unit 331 acquires a predicted score using, for example, a correspondence relationship between one or more pieces of teacher routine information including teacher biometric characteristic information and a teacher score, and one or more pieces of predicted routine information including predicted biometric characteristic information.

[0277] The score acquisition unit 331 acquires the predicted score using, for example, a correspondence relationship between one or more pieces of teacher daily life information including teacher life information and a teacher score, and one or more pieces of predicted daily life information including predicted life information.

[0278] The score acquisition unit 331 may, for example, read out a past score corresponding to the acquired predicted score from the storage unit 31, and acquire score fluctuation information regarding an increase or decrease in the score using the past score and the acquired predicted score. The score fluctuation information is, for example, "predicted score-past score", "past score-predicted score", "information indicating whether the score is increasing or decreasing", and "information indicating the degree of increase or decrease in the score".

[0279] The correspondence acquisition means 3311 constituting the score acquisition unit 331 acquires correspondence information that specifies the correspondence between one or more pieces of teacher daily information and a teacher score.

[0280] The correspondence acquisition means 3311 may acquire correspondence information that specifies the correspondence between one or more pieces of teacher daily information and a teacher score for each organizational condition configured using, for example, one or more organizational attribute values. The organizational condition is, for example, one organizational attribute value. The organizational condition is, for example, a logical expression having two or more organizational conditions as elements (for example, "organization A or organization B"). The organizational condition is, for example, an industry identifier such as "electrical manufacturer," "bank," or "trading company." The organizational condition is, for example, a scale identifier such as "large company," "small and medium-sized enterprise," or the like.

[0281] An example of a process in which the correspondence acquisition means 3311 acquires the learning model, the correspondence table, and the arithmetic expression will be described below. (1) When obtaining a learning model

[0282] The correspondence acquisition means 3311 performs a learning process using, for example, one or more pairs of one or more pieces of teacher routine information and teacher scores, according to a machine learning algorithm, and acquires correspondence information that is a learning model.

[0283] More specifically, the correspondence acquisition means 3311 acquires, for example, two or more sets of teacher daily information for acquiring a learning model from the daily information storage unit 312. The two or more sets of teacher daily information are, for example, sets of teacher daily information of different organizations. The two or more sets of teacher daily information are, for example, sets of teacher daily information of different periods.

[0284] Next, for each set of teacher daily information, the correspondence acquisition means 3311 acquires two or more teacher characteristics using, for example, the acquired one or more pieces of teacher daily information, and acquires a teacher vector having each of the two or more teacher characteristics as elements. In addition, the correspondence acquisition means 3311 acquires, for example, a teacher score corresponding to the set of teacher daily information to be processed from the teacher score storage unit 313. Note that, for example, the teacher score is associated with an organization identifier and / or time information that is paired with the set of teacher daily information to be processed.

[0285] Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each set of teacher daily information. Then, the correspondence acquisition means 3311 acquires two or more teacher data. Note that in the teacher data, each teacher feature of the teacher vector may be said to be an explanatory variable, and the teacher score may be said to be a target variable. Also, the teacher feature is teacher daily information or information acquired from the teacher daily information.

[0286] Next, the correspondence acquisition means 3311, for example, provides two or more pieces of teacher data to a module that performs machine learning learning processing, executes the module, acquires a learning model, and stores the learning model in the correspondence information storage unit 314, at least temporarily.

[0287] This learning model is a learning model that inputs predicted everyday information and / or a prediction vector, which is information obtained from the predicted everyday information, and outputs a prediction score. In addition, algorithms such as deep learning, decision tree, random forest, SVR, and SVM can be used for the machine learning learning process. In other words, the algorithm of the machine learning learning process is not limited. In addition, the module that performs the machine learning learning process can be, for example, Tiny_SVM, a TensorFlow function, a MicrosoftML function, or the like. (2) When obtaining a correspondence table

[0288] The correspondence acquisition means 3311 acquires two or more pieces of teacher data each having a teacher vector and a teacher score, for example, in the same manner as the process described in (1). Next, the correspondence acquisition means 3311 creates a correspondence table having two or more pieces of correspondence information each having a teacher vector and a teacher score, for example, and accumulates the correspondence table in the correspondence relationship information storage unit 314 at least temporarily. (3) To obtain an arithmetic expression

[0289] The correspondence acquisition means 3311 acquires two or more pieces of teacher data having a teacher vector and a teacher score, for example, in the same manner as the process described in (1). Next, the correspondence acquisition means 3311 acquires an arithmetic expression that receives two or more teacher vectors as input and outputs a teacher score corresponding to each teacher vector.

[0290] The correspondence acquisition unit 3311 acquires, for example, an equation element that is the basis of an equation (for example, “score S=ax1+bx2+cx3+···+(n-1)x n +n(a,b,c···n are parameters, x1,x2,x3,···x n The correspondence acquisition means 3311 then reads out the corresponding equation (variables) from the storage unit 31. Next, the correspondence acquisition means 3311 substitutes, for example, two or more pieces of teacher data into the original variables of the arithmetic formula, and acquires parameters for obtaining each teacher score by multiple regression analysis, multivariate analysis, or the like. Then, the correspondence acquisition means 3311 stores the acquired arithmetic formula in the correspondence information storage unit 314, at least temporarily.

[0291] The score acquisition means 3312 acquires a predicted score using the correspondence information and one or more pieces of predicted daily life information.

[0292] The score acquisition means 3312, for example, acquires correspondence information corresponding to the organizational conditions that the organization to be predicted meets from the correspondence information storage unit 314, and acquires a predicted score using the correspondence information and one or more pieces of predicted daily information.

[0293] More specifically, the score acquiring means 3312 acquires, for example, one or more pieces of predicted routine information, which are routine information of an organization and period corresponding to the received prediction instruction, from the routine information storage unit 312. Next, the score acquiring means 3312 acquires, for example, two or more predictive features using one or more pieces of predicted routine information, and acquires a predictive vector having the two or more predictive features as elements. Note that the method of acquiring the predictive features and the method of acquiring the predictive vector using the predicted routine information have been described above. Next, the score acquiring means 3312 acquires a predictive score using the correspondence relationship information and the predictive vector.

[0294] An example of processing in which the score acquiring means 3312 acquires a predicted score using any one of the three types of correspondence relationship information will be described below. (1) When using a learning model

[0295] The score acquisition means 3312 performs a prediction process using, for example, a learning model and one or more pieces of predicted daily information according to a machine learning algorithm, and acquires a predicted score.

[0296] More specifically, the score acquisition means 3312 acquires a prediction vector using one or more pieces of predicted daily information. Next, the score acquisition means 3312 acquires a learning model corresponding to the received prediction instruction from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the prediction vector and the learning model to a module that performs machine learning prediction processing, executes the module, and acquires a prediction score. Note that the algorithm for the machine learning prediction processing can be deep learning, decision tree, random forest, SVR, or other algorithms. In other words, the algorithm for the machine learning prediction processing is not important. Also, the module that performs the machine learning prediction processing is not important, as described above. (2) When using the correspondence table

[0297] The score acquisition means 3312 acquires a prediction vector, which is a vector having two or more predictive features, by using, for example, one or more pieces of predicted daily information. Next, the score acquisition means 3312 applies the prediction vector to a correspondence table to acquire a prediction score.

[0298] More specifically, the score acquisition means 3312 acquires a predicted vector using, for example, one or more pieces of predicted daily information. Next, the score acquisition means 3312 searches, for example, a vector that is most similar to the predicted vector from a correspondence table, and acquires a teacher score paired with the vector as a predicted score.

[0299] Furthermore, the score acquisition means 3312 may, for example, determine from a correspondence table vectors whose distance from the acquired predictive vector is close enough to satisfy a predetermined condition, and acquire a representative value of the teacher scores corresponding to each of the two or more vectors. The representative value may be, for example, an average value, a median value, or a weighted average. The weighted average is acquired, for example, by calculating a weight inversely proportional to the distance between the vectors, and using the weight. The predetermined condition may be, for example, that the distance is within or smaller than a threshold value, or that the shortest distance is in the top N (N is 2 or more). (3) When using an arithmetic expression

[0300] The score acquisition means 3312 acquires a predicted vector using, for example, one or more pieces of predicted daily information, provides each feature of the predicted vector to an arithmetic expression, executes the arithmetic expression, and acquires a predicted score. Note that the arithmetic expression is an example of the correspondence information stored in the correspondence information storage unit 314.

[0301] The score output unit 341 outputs the predicted score acquired by the score acquisition unit 331. The score output unit 341 may output score variation information acquired by the score acquisition unit 331.

[0302] Here, output usually means transmission to the terminal device 4. However, output here may be considered to be a concept including display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, delivery of processing results to other processing devices or other programs, etc.

[0303] The score output unit 341 may pass the predicted score acquired by the score acquiring unit 331 to the score acquiring unit 331, cause the score acquiring unit 331 to acquire score variation information using the predicted score, and output the score variation information.

[0304] The terminal storage unit 41 constituting the terminal device 4 stores various types of information. The various types of information are, for example, an organization identifier and a member identifier that identify the organization of the user. The various types of information are, for example, information (for example, a predicted score) received by the terminal receiving unit 45. The organization identifier may be considered as information that identifies the user.

[0305] The terminal reception unit 42 receives various instructions and information. The various instructions and information are, for example, predicted instructions and daily information. Here, reception is a concept including reception of information input from an input device such as a keyboard, mouse, or touch panel, reception of information transmitted via a wired or wireless communication line, reception of information read from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory, and the like.

[0306] The input means for various instructions and information may be anything, such as a touch panel, a keyboard, a mouse, a menu screen, etc. The terminal reception unit 42 may 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.

[0307] The various processes performed by the device processing unit 43 include, for example, a process of configuring the information received by the device receiving unit 45 into data to be displayed. The various processes include, for example, a process of configuring the instructions received by the device accepting unit 42 into instructions to be transmitted.

[0308] The terminal transmitting unit 44 transmits various instructions, information, and the like to the score prediction device 3. The various instructions, information, and the like are, for example, instructions configured by the terminal processing unit 43, instructions and information accepted by the terminal accepting unit 42, and the like.

[0309] The terminal receiving unit 45 receives various information from the score prediction device 3. The various information is, for example, a predicted score.

[0310] The terminal output unit 46 acquires various information. The various information is, for example, information accepted by the terminal acceptance unit 42, information received by the terminal reception unit 45, and information configured by the device processing unit 43. The various information is, for example, a predicted score.

[0311] Here, output is a concept that includes display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage on a recording medium, and handing over the processing results to other processing devices or other programs, etc.

[0312] The storage unit 31, organizational information storage unit 311, daily information storage unit 312, business input information storage means 3121, communication information storage means 3122, business result information storage means 3124, stress information storage means 3125, measurement information storage means 3126, image storage means 3127, audio storage means 3128, lifestyle information storage means 3129, teacher score storage unit 313, correspondence information storage unit 314, and terminal storage unit 41 are preferably non-volatile recording media, but can also be realized using volatile recording media.

[0313] There is no restriction on the process by which information is stored in the storage unit 31, etc. For example, information may be stored in the storage unit 31, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 31, etc., or information inputted via an input device may be stored in the storage unit 31, etc.

[0314] The acceptance unit 32 and the terminal receiving unit 45 are usually realized by wireless or wired communication means, but may be realized by means for receiving broadcast.

[0315] The processing unit 33, the score acquisition unit 331, the correspondence acquisition means 3311, the score acquisition means 3312, and the device processing unit 43 can usually be realized by a processor, a memory, etc. The processing procedure of the processing unit 33, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type is not important.

[0316] The output unit 34, the score output unit 341, and the terminal transmission unit 44 are usually realized by wireless or wired communication means, but may be realized by broadcasting means.

[0317] The terminal output unit 46 may or may not include an output device such as a display, a speaker, etc. The terminal output unit 46 may be realized by driver software for an output device, or a combination of driver software for an output device and an output device, etc.

[0318] Next, an example of the operation of the score prediction device 3 will be described with reference to the flowchart of FIG.

[0319] (Step S1401) The reception unit 32 judges whether or not daily information has been received from the terminal device 4 in association with a member identifier. If daily information has been received, the process proceeds to step S1402, and if daily information has not been received, the process proceeds to step S1403. Note that daily information is usually associated with a member identifier or an organization identifier.

[0320] (Step S1402) The processing unit 33 accumulates the daily information received in step S1401 in the daily information storage unit 312. Return to step S1401. The accumulated daily information is usually associated with a member identifier or an organization identifier. The accumulated daily information is usually associated with time information. In other words, it is preferable for the processing unit 33 to obtain date and time information from a clock (not shown), and accumulate the daily information in the daily information storage unit 312 in association with the date and time information.

[0321] (Step S1403) The accepting unit 32 judges whether or not a prediction instruction has been received from the terminal device 4. If a prediction instruction has been received, the process proceeds to step S1404, and if a prediction instruction has not been received, the process returns to step S1401.

[0322] (Step S1404) The correspondence acquisition unit 3311 performs correspondence acquisition processing. An example of the correspondence acquisition processing will be described with reference to the flowchart in Fig. 15. Note that the correspondence acquisition processing is processing for acquiring correspondence relationship information.

[0323] (Step S1405) The score acquisition means 3312 performs a score acquisition process. An example of the score acquisition process will be described with reference to the flowchart in Fig. 25. Note that the score acquisition process is a process for acquiring a predicted score.

[0324] (Step S1406) The score output unit 341 transmits the predicted score acquired in step S1405 to the terminal device 4 that transmitted the prediction instruction. The process returns to step S1401.

[0325] 14, the correspondence acquisition process in step S1404 may be performed before the prediction instruction is received. In other words, the correspondence acquisition process and the score acquisition process do not need to be performed consecutively.

[0326] In the flowchart of FIG. 14, the process ends when the power is turned off or an interrupt occurs to end the process.

[0327] Next, an example of the correspondence acquisition process in step S1404 will be described with reference to the flowchart in FIG.

[0328] (Step S1501) The correspondence acquisition means 3311 acquires teacher period information. For example, the correspondence acquisition means 3311 acquires teacher period information included in the received prediction instruction. For example, the correspondence acquisition means 3311 acquires predetermined teacher period information from the storage unit 31. Note that the teacher period information is information that specifies a period of teacher daily information used to acquire correspondence relationship information.

[0329] (Step S1502) The correspondence acquisition means 3311 acquires the teacher target condition. The correspondence acquisition means 3311 acquires, for example, the teacher target condition included in the received prediction instruction. The correspondence acquisition means 3311 acquires, for example, predetermined teacher target conditions from the storage unit 31. Note that the teacher target condition is a condition of a target organization used when predicting a score. The teacher target condition is, for example, an organization identifier of a target organization from which a predicted score is to be obtained. The teacher target condition is, for example, an organization identifier of all organizations. The teacher target condition is, for example, a condition of an organization attribute value of a target from which a predicted score is to be obtained (for example, an industry identifier, a size identifier, etc.).

[0330] (Step S1503) The correspondence obtaining unit 3311 assigns 1 to the counter i.

[0331] (Step S1504) The correspondence acquisition means 3311 judges whether or not the i-th teacher routine set that matches the conditions including the teacher period information and the teacher target clause exists in the routine information storage unit 312. If the i-th teacher routine set exists, the process proceeds to step S1505, and if not, the process proceeds to step S1510. Note that the teacher routine set is a set of one or more pieces of teacher routine information.

[0332] (Step S1505) The correspondence acquiring means 3311 acquires, from the daily routine information storage unit 312, the i-th teacher daily routine set that matches the conditions including the teacher period information and the teacher target condition.

[0333] (Step S1506) The correspondence acquisition means 3311 performs a process of acquiring a feature group using the i-th teacher routine set acquired in step S1505. An example of such a feature group acquisition process will be described with reference to the flowchart in Fig. 16. Note that a feature group is usually a set of two or more features. Also, the features here are teacher features.

[0334] (Step S1507) The correspondence acquisition means 3311 acquires a teacher score corresponding to the i-th teacher routine set from the teacher score storage unit 313. Note that the teacher routine set and the teacher score are associated with each other. For example, teacher routine information associated with time information of a period specified by period information associated with the teacher score is the teacher routine information associated with the teacher score.

[0335] (Step S1508) The correspondence acquisition means 3311 constructs the i-th teacher data having the teacher vector made up of the feature group acquired in step S1506 and the teacher score acquired in step S1507, and temporarily stores it in a buffer (not shown).

[0336] (Step S1509) The correspondence obtaining unit 3311 increments the counter i by 1. The process returns to step S1504.

[0337] (Step S1510) The correspondence acquisition means 3311 acquires correspondence information using two or more pieces of teacher data temporarily stored in step S1508. Note that the method of acquiring correspondence information using two or more pieces of teacher data has been described above. Also, as described above, the correspondence acquisition means 3311 acquires, for example, a learning device, a correspondence table, or an arithmetic expression.

[0338] (Step S1511) The correspondence acquisition unit 3311 at least temporarily accumulates the correspondence information acquired in step S1510 in the correspondence information storage unit 314. Then, the process returns to the upper level process.

[0339] Next, an example of the feature group acquisition process in step S1506 will be described with reference to the flowchart in Fig. 16. In the flowchart in Fig. 16, for example, the type of everyday information to be used is determined in advance. Also, information specifying the everyday information to be used is stored in association with, for example, correspondence information. In other words, the everyday information used when the correspondence information is acquired is usually the same as the everyday information used to acquire the predicted score.

[0340] (Step S1601) The score acquisition unit 331 judges whether or not to use the business input information to acquire the predicted score. If the business input information is to be used, the process proceeds to step S1602. If the business input information is not to be used, the process proceeds to step S1603.

[0341] (Step S1602) The score acquisition unit 331 performs a task input information process. An example of the task input information process will be described with reference to the flowchart in Fig. 17. Note that the task input information process is a process of acquiring one or more features related to the task input information by using the task input information.

[0342] (Step S1603) The score acquisition unit 331 judges whether or not to use communication information to acquire a predicted score. If the communication information is to be used, the process proceeds to step S1604. If the communication information is not to be used, the process proceeds to step S1605.

[0343] (Step S1604) The score obtaining unit 331 performs communication information processing. An example of the communication information processing will be described with reference to the flowchart in Fig. 18. The communication information processing is processing for obtaining one or more features related to the communication information by using the communication information.

[0344] (Step S1605) The score acquisition unit 331 judges whether or not to use attendance status information to acquire a predicted score. If attendance status information is to be used, the process proceeds to step S1606. If attendance status information is not to be used, the process proceeds to step S1607.

[0345] (Step S1606) The score acquisition unit 331 performs attendance status information processing. An example of the attendance status information processing will be described with reference to the flowchart in Fig. 19. The attendance status information processing is processing for acquiring one or more features related to the attendance status information using the attendance status information.

[0346] (Step S1607) The score acquisition unit 331 judges whether or not to use the task result information to acquire the predicted score. If the task result information is to be used, the process proceeds to step S1608. If the task result information is not to be used, the process proceeds to step S1609.

[0347] (Step S1608) The score acquiring unit 331 reads out from the task result information storage unit 3124 task result information to be used, which is associated with the organization identifier of the target organization and associated with period information of the target period.

[0348] (Step S1609) Score acquiring unit 331 judges whether or not to use stress information to acquire a predicted score. If stress information is to be used, the process proceeds to step S1610, and if stress information is not to be used, the process proceeds to step S1611.

[0349] (Step S1610) The score acquiring section 331 performs stress information processing. An example of the stress information processing will be described with reference to the flowchart in Fig. 20. The stress information processing is processing for acquiring one or more features related to the stress information.

[0350] (Step S1611) The score acquiring unit 331 judges whether or not to use the measurement information. If the measurement information is to be used, the process proceeds to step S1611, and if the measurement information is not to be used, the process proceeds to step S1612.

[0351] (Step S1612) The score acquiring unit 331 performs measurement information processing. An example of the measurement information processing will be described with reference to the flowchart in Fig. 21. The measurement information processing is processing for acquiring one or more features related to the measurement information by using the measurement information.

[0352] (Step S1613) The score acquiring unit 331 judges whether or not to use an image to acquire a predicted score. If an image is to be used, the process proceeds to step S1614, and if an image is not to be used, the process proceeds to step S1615.

[0353] (Step S1614) The score obtaining unit 331 performs image information processing. An example of the image information processing will be described with reference to the flowchart in Fig. 22. The image information processing is processing for obtaining one or more features related to an image using the image.

[0354] (Step S1615) The score acquisition unit 331 judges whether or not to use voice to acquire the predicted score. If voice is to be used, the process proceeds to step S1616, and if voice is not to be used, the process proceeds to step S1617.

[0355] (Step S1616) The score acquiring unit 331 performs voice information processing. An example of the voice information processing will be described with reference to the flowchart in Fig. 23. The voice information processing is processing for acquiring one or more features related to the voice using the voice.

[0356] (Step S1617) The score acquiring unit 331 judges whether or not to use the life information to acquire the predicted score. If the life information is to be used, the process proceeds to step S1618, and if the life information is not to be used, the process returns to the upper process.

[0357] (Step S1618) The score acquiring unit 331 performs life information processing. Then, the process returns to the upper level processing. An example of the life information processing will be described with reference to the flowchart in Fig. 24. The life information processing is a process of acquiring one or more features related to the life information by using the life information.

[0358] Next, an example of the task input information processing in step S1602 will be described with reference to the flowchart in FIG.

[0359] (Step S1701) The score acquiring unit 331 assigns 1 to a counter i.

[0360] (Step S1702) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S1703, and if the i-th member identifier does not exist, the process proceeds to step S1712.

[0361] (Step S1703) The score acquiring unit 331 acquires from the task input information storage unit 3121 a daily report for a target period that is paired with the i-th member identifier.

[0362] (Step S1704) The score acquiring unit 331 acquires one or more daily report attribute values ​​from the acquired one or more daily reports. The daily report attribute values ​​are, for example, the data volume of each daily report, the average data volume of one daily report, information on changes in the data volume, the number of occurrences of negative words, the occurrence rate of negative words, the number of occurrences of positive words, and the occurrence rate of positive words.

[0363] (Step S1705) The score acquiring unit 331 acquires from the business input information storage unit 3121 a document that is paired with the i-th member identifier and is for a target period.

[0364] (Step S1706) The score acquiring unit 331 acquires one or more document attribute values ​​from the acquired one or more documents. The document attribute values ​​are, for example, an average value of the data amount of the document for each creation date, and information on changes in the data amount.

[0365] (Step S1707) The score acquiring unit 331 acquires a program for a target period, which is a program paired with the i-th member identifier, from the task input information storage means 3121. Note that the program is a program created by a software developer.

[0366] (Step S1708) The score acquiring unit 331 acquires one or more program attribute values ​​from the acquired one or more programs. The program attribute values ​​are, for example, the average number of steps of the program for each day it was created, and information on changes in the number of steps.

[0367] (Step S1709) The score acquiring unit 331 acquires an operation log for a target period, which is an operation log paired with the i-th member identifier, from the task input information storage means 3121. Note that the operation log is an operation log for a device (for example, a personal computer) used by a member.

[0368] (Step S1710) The score acquisition unit 331 acquires one or more operation log attribute values ​​from the acquired one or more operation logs. The operation log attribute values ​​are, for example, an average value of the number of operation logs created on each day, and information on changes in the number of operation logs.

[0369] (Step S1711) The score obtaining unit 331 increments the counter i by 1. The process returns to step S1702.

[0370] (Step S1712) The score acquiring unit 331 acquires a representative value for each type of daily report attribute value using one or more daily report attribute values ​​of all target members acquired in step S1704. The representative value is, for example, an average value, a weighted average value, a median value, or a sum. The score acquiring unit 331 calculates, for example, a representative value of the amount of daily report data of all members, a representative value of change information of the amount of data of all members, a representative value of the number of occurrences of negative words of all members, a representative value of the occurrence rate of negative words, a representative value of the number of occurrences of positive words, and a representative value of the occurrence rate of positive words. Note that all members are, for example, members identified by a member identifier that pairs with a specified organization identifier.

[0371] (Step S1713) The score acquiring unit 331 acquires a representative value for each type of document attribute value using one or more document attribute values ​​of all target members acquired in step S1706. The score acquiring unit 331 acquires, for example, a representative value of the document data volume for each day of all members, a representative value of information indicating the variance in the data volume for each day of all members in the target period, the percentage of all members whose document data volume created per day is on the rise, and the percentage of all members whose document data volume created per day is on the decline.

[0372] (Step S1714) The score acquiring unit 331 acquires a representative value for each type of program attribute value using one or more program attribute values ​​of all target members acquired in step S1708. The score acquiring unit 331 acquires, for example, a representative value of the number of program steps per day for all members, a representative value of information indicating the variance in the number of steps per day for all members in the target period, the percentage of all members whose data volume of the number of steps in programs created per day is on the rise, and the percentage of all members whose data volume of the number of steps in programs created per day is on the decline.

[0373] (Step S1715) The score acquisition unit 331 acquires a representative value for each type of operation log attribute value using one or more operation log attribute values ​​of all target members acquired in step S1710. The score acquisition unit 331 acquires, for example, a representative value of the number of operation logs for each day of all members, a representative value of information indicating the variation in the number of operation logs for each day in the target period of all members, the percentage of all members whose number of daily operation logs is on an increasing trend, and the percentage of all members whose number of daily operation logs is on a decreasing trend. Return to upper level processing.

[0374] Next, an example of the communication information processing in step S1604 will be described with reference to the flowchart in FIG.

[0375] (Step S1801) The score acquiring unit 331 assigns 1 to a counter i.

[0376] (Step S1802) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S1803, and if the i-th member identifier does not exist, the process proceeds to step S1812.

[0377] (Step S1803) The score acquiring unit 331 acquires chat information for a target period, which is chat information paired with the i-th member identifier, from the communication information storage unit 3122.

[0378] (Step S1804) The score acquisition unit 331 acquires one or more chat attribute values ​​from the one or more pieces of chat information acquired in step S1803. The chat attribute values ​​are, for example, the data amount of the chat information, the number of negative words appearing in the chat information, the appearance rate of the negative words in the chat information, the number of positive words appearing in the chat information, and the appearance rate of the positive words in the chat information.

[0379] (Step S1805) The score acquiring unit 331 acquires, from the communication information storage means 3122, SNS information for a target period, which is SNS information paired with the i-th member identifier.

[0380] (Step S1806) The score acquisition unit 331 acquires one or more SNS attribute values ​​from the one or more SNS information acquired in step S1805. The SNS attribute values ​​are, for example, the amount of data of the SNS information created by the member, the number of negative words appearing in the SNS information created by the member, the appearance rate of the negative words in the SNS information created by the member, the number of positive words appearing in the SNS information created by the member, and the appearance rate of the positive words in the SNS information created by the member.

[0381] (Step S1807) The score acquiring unit 331 acquires, from the communication information storage unit 3122, emails that are paired with the i-th member identifier and are for the target period.

[0382] (Step S1808) The score acquisition unit 331 acquires one or more email attribute values ​​from one or more emails acquired in step S1807. The email attribute values ​​are, for example, a response rate, the number of emails sent by the member, the data volume of the emails sent by the member, the number of occurrences of negative words in the emails sent by the member, the occurrence rate of negative words in the emails sent by the member, the number of occurrences of positive words in the emails sent by the member, and the occurrence rate of positive words in the emails sent by the member.

[0383] (Step S1809) The score acquiring unit 331 acquires, from the communication information storage means 3122, speech text information for a target period, which is speech text information paired with the i-th member identifier.

[0384] (Step S1810) The score acquisition unit 331 acquires one or more speech text attribute values ​​from one or more pieces of speech text information acquired in step S1809. The speech text attribute values ​​are, for example, the data amount of the speech text information, the number of negative words appearing in the speech text information of the member, the appearance rate of the negative words in the speech text information of the member, the number of positive words appearing in the speech text information of the member, and the appearance rate of the positive words in the speech text information of the member.

[0385] (Step S1811) The score obtaining unit 331 increments the counter i by 1. The process returns to step S1802.

[0386] (Step S1812) The score acquiring unit 331 acquires a representative value for each type of chat attribute value using one or more chat attribute values ​​of all target members acquired in step S1804. The representative value is, for example, an average value, a weighted average value, a median value, or a sum. The score acquiring unit 331 calculates, for example, a representative value of the data volume of chat information of all members, a representative value of the number of occurrences of negative words of all members, a representative value of the occurrence rate of negative words, a representative value of the number of occurrences of positive words, and a representative value of the occurrence rate of positive words. Note that all members are members identified by member identifiers that are paired with a specified organization identifier, for example.

[0387] (Step S1813) The score acquiring unit 331 acquires a representative value for each type of SNS attribute value using one or more SNS attribute values ​​of all target members acquired in step S1806. The score acquiring unit 331 acquires, for example, a representative value of the data amount of SNS information for each day of all members, a representative value of information showing the variation in the data amount of SNS information for each day in the target period of all members, a percentage of all members whose data amount of SNS information created in one day is on the rise, a percentage of all members whose data amount of SNS information created in one day is on the decline, a representative value of the number of negative words appearing in the SNS information, a representative value of the rate of appearance of negative words in the SNS information, a representative value of the number of positive words appearing in the SNS information, and a representative value of the rate of appearance of positive words in the SNS information.

[0388] (Step S1814) The score acquiring unit 331 acquires a representative value for each type of email attribute value using one or more email attribute values ​​of all target members acquired in step S1808. The score acquiring unit 331 acquires, for example, a representative value of the response rate of all members, a representative value of the number of emails, a representative value of the data volume of emails, a representative value of the number of occurrences of negative words in emails, a representative value of the occurrence rate of negative words in emails, a representative value of the number of occurrences of positive words in emails, and a representative value of the occurrence rate of positive words in emails.

[0389] (Step S1815) The score acquiring unit 331 acquires a representative value for each type of voice text attribute value using one or more voice text attribute values ​​of all target members acquired in step S1810. The score acquiring unit 331 returns to upper level processing. Note that the score acquiring unit 331 acquires, for example, a representative value of the data volume of the voice text information of all members, a representative value of the number of occurrences of negative words in the voice text information of all members, a representative value of the occurrence rate of negative words in the voice text information of all members, a representative value of the number of occurrences of positive words in the voice text information of all members, and a representative value of the occurrence rate of positive words in the voice text information of all members.

[0390] Next, an example of the attendance status information processing in step S1606 will be described with reference to the flowchart in FIG.

[0391] (Step S1901) The score acquisition unit 331 assigns 1 to a counter i.

[0392] (Step S1902) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S1903, and if the i-th member identifier does not exist, the process proceeds to step S1906.

[0393] (Step S1903) The score acquiring unit 331 acquires, from the attendance status information storage means 3123, attendance information for a target period, which is attendance information paired with the i-th member identifier.

[0394] (Step S1904) The score acquisition unit 331 uses the acquired attendance information to acquire one or more attendance attribute values. The attendance attribute values ​​are, for example, the number of annual holidays of the member, the number of overtime hours of the member, and the number of days off of the member.

[0395] (Step S1905) The score obtaining unit 331 increments the counter i by 1. The process returns to step S1902.

[0396] (Step S1906) The score acquisition unit 331 acquires a representative value for each type of attendance attribute value using one or more attendance attribute values ​​of all target members acquired in step S1704. It returns to higher-level processing. The representative value may be, for example, an average value, a weighted average value, a median value, or a total value. The score acquisition unit 331 acquires, for example, a representative value of the number of annual paid leave days for all members, a representative value of the number of overtime hours for members, and a representative value of the number of days off for members.

[0397] Next, an example of the stress information processing in step S1610 will be described with reference to the flowchart of FIG.

[0398] (Step S2001) The score acquisition unit 331 assigns 1 to a counter i.

[0399] (Step S2002) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S2003, and if the i-th member identifier does not exist, the process proceeds to step S2006.

[0400] (Step S2003) The score acquiring unit 331 acquires one or more answers for a target period, which are paired with the i-th member identifier, from the stress information storage means 3125. The answers are responses to a questionnaire for acquiring stress information (for example, stress level).

[0401] (Step S2004) Score acquisition unit 331 acquires stress information using the one or more answers acquired in step S2003. The stress information may be one answer itself, or a stress level obtained by substituting one or more answers (e.g., numerical values) into an arithmetic formula and executing an arithmetic operation, or stress information paired with a vector that is most similar to a vector having two or more answers as elements. It is assumed that such an arithmetic formula or a table having two or more pairs of such vectors and stress information is stored in storage unit 31.

[0402] (Step S2005) The score acquiring unit 331 acquires one or more pieces of biological information (e.g., heart rate, blood pressure) paired with the i-th member identifier, for a target period, from the measurement information storage means 3126. The answer is a response to a questionnaire for acquiring stress information (e.g., stress level).

[0403] (Step S2006) The score acquiring unit 331 acquires stress information by using one or more pieces of biological information acquired in step S2003.

[0404] (Step S2007) The score obtaining unit 331 increments the counter i by 1. The process returns to step S2002.

[0405] (Step S2008) The score acquisition unit 331 acquires a representative value of the first stress information using the stress information based on the questionnaire of all target members acquired in step S2004. The representative value is, for example, an average value, a weighted average value, a median value, or a total value.

[0406] (Step S2009) The score acquisition unit 331 acquires a representative value of the second stress information using the stress information based on the biological information of all the target members acquired in step S2006. The process returns to the upper level process. The representative value is, for example, an average value, a weighted average value, a median value, or a total value.

[0407] Next, an example of the measurement information processing in step S1612 will be described with reference to the flowchart in FIG.

[0408] (Step S2101) The score acquisition unit 331 assigns 1 to a counter i.

[0409] (Step S2102) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S2103, and if the i-th member identifier does not exist, the process proceeds to step S2108.

[0410] (Step S2103) The score acquiring unit 331 acquires from the measurement information storage means 3126 one or more heart rate numbers paired with the i-th member identifier, which are one or more heart rate numbers for the target period.

[0411] (Step S2104) The score acquisition unit 331 acquires one or more heart rate attribute values ​​using the one or more heart rates acquired in step S2103. The heart rate attribute values ​​are the average heart rate, information indicating the variation of the heart rate (e.g., variance), and the maximum heart rate.

[0412] (Step S2105) The score acquiring unit 331 acquires from the measurement information storage means 3126 one or more blood pressures for a target period which are paired with the i-th member identifier.

[0413] (Step S2106) The score acquisition unit 331 acquires a blood pressure attribute value using one or more blood pressures acquired in step S2103. The blood pressure attribute value is, for example, one or more average values ​​of blood pressure, information indicating the variation of blood pressure (for example, variance), and the maximum value of blood pressure. Note that the blood pressure is the maximum blood pressure, the minimum blood pressure, or the maximum and minimum blood pressures.

[0414] (Step S2107) The score obtaining unit 331 increments the counter i by 1. The process returns to step S2102.

[0415] (Step S2108) The score acquisition unit 331 acquires a representative value of each heart rate attribute value for each type of heart rate attribute value using one or more heart rate attribute values ​​of all the target members acquired in step S2104. The representative value may be, for example, an average value, a weighted average value, a median value, or a sum.

[0416] (Step S2109) The score acquisition unit 331 acquires a representative value of each blood pressure attribute value for each type of blood pressure attribute value using one or more blood pressure attribute values ​​of all the target members acquired in step S2106. The score acquisition unit 331 returns to the upper process. The representative value is, for example, the average value, the weighted average value, the median value, or the sum.

[0417] Next, an example of the image information processing in step S1614 will be described with reference to the flowchart in FIG.

[0418] (Step S2201) The score acquisition unit 331 assigns 1 to a counter i.

[0419] (Step S2202) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S2203, and if the i-th member identifier does not exist, the process proceeds to step S2210.

[0420] (Step S2203) The score acquiring unit 331 acquires from the image storing means 3127 one or more images for the target period, which are one or more images paired with the i-th member identifier.

[0421] (Step S2204) The score obtaining unit 331 assigns 1 to the counter j.

[0422] (Step S2205) The score acquisition unit 331 judges whether or not the j-th processing unit exists in the acquired image. If the j-th field exists, the process proceeds to step S2206, and if the j-th field does not exist, the process proceeds to step S2208. The processing unit is, for example, a field or a file including one or more fields.

[0423] (Step S2206) The score acquisition unit 331 acquires one or more types of image attribute values ​​of the image of the j-th processing unit.

[0424] (Step S2207) The score obtaining unit 331 increments the counter j by 1. The process returns to step S2105.

[0425] (Step S2208) The score acquisition unit 331 acquires the image attribute value of the i-th member by using each image attribute value for each type of one or more image attribute values ​​of the image of the j-th processing unit. Note that the image attribute value is, for example, a specific facial expression identifier (for example, an identifier of a negative facial expression) and a ratio of negative facial expressions. Also, the identifier of a negative facial expression is, for example, "anger" or "sorrow".

[0426] (Step S2209) The score obtaining unit 331 increments the counter i by 1. The process returns to step S2102.

[0427] (Step S2210) The score acquisition unit 331 acquires a representative value of each image attribute value for each type of image attribute value using one or more image attribute values ​​of all target members acquired in step S2108. The process returns to the upper level. The representative value may be, for example, an average value, a weighted average value, a median value, or a total value.

[0428] Next, an example of the voice information processing in step S1616 will be described with reference to the flowchart in FIG.

[0429] (Step S2301) The score acquisition unit 331 assigns 1 to a counter i.

[0430] (Step S2302) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S2303, and if the i-th member identifier does not exist, the process proceeds to step S2310.

[0431] (Step S2303) The score acquiring unit 331 acquires from the voice storage means 3128 one or more voices for a target period, which are paired with the i-th member identifier.

[0432] (Step S2304) The score obtaining unit 331 assigns 1 to a counter j.

[0433] (Step S2305) The score acquisition unit 331 judges whether or not the j-th processing unit exists in the acquired voice. If the j-th field exists, the process proceeds to step S2306, and if the j-th field does not exist, the process proceeds to step S2308. The processing unit is, for example, voice information of a unit time, or a file.

[0434] (Step S2306) The score acquiring unit 331 acquires one or more types of voice attribute values ​​of the voice of the j-th processing unit.

[0435] (Step S2307) The score obtaining unit 331 increments the counter j by 1. The process returns to step S2105.

[0436] (Step S2308) The score acquisition unit 331 acquires the voice attribute value of the i-th member by using each voice attribute value of the j-th processing unit for each type of voice attribute value. The voice attribute value is, for example, a voice feature amount, a ratio of speaking time in a conference attended, or an emotion identifier.

[0437] (Step S2309) The score obtaining unit 331 increments the counter i by 1. The process returns to step S2102.

[0438] (Step S2310) The score acquisition unit 331 acquires a representative value of the voice attribute values ​​by using one or more voice attribute values ​​of all the members of interest acquired in step S2108 for each type of voice attribute value. The score acquisition unit 331 returns to the upper process. The representative value may be, for example, an average value, a weighted average value, a median value, or a total value.

[0439] Next, an example of the life information processing in step S1618 will be described with reference to the flowchart in FIG.

[0440] (Step S2401) The score acquisition unit 331 assigns 1 to a counter i.

[0441] (Step S2402) The score acquisition unit 331 judges whether or not the i-th member identifier exists among the member identifiers of the target member. If the i-th member identifier exists, the process proceeds to step S2403, and if the i-th member identifier does not exist, the process proceeds to step S2410.

[0442] (Step S2403) The score acquiring unit 331 acquires, from the life information storage means 3129, the sleeping hours for each day of the target period, which are paired with the i-th member identifier.

[0443] (Step S2404) The score acquisition unit 331 acquires one or more sleep attribute values ​​using one or more sleep hours acquired in step S2403. The sleep attribute values ​​are, for example, an average value of a day's sleep hours, information indicating the variation in sleep hours (for example, variance), the number of days when the sleep hours are below a threshold, and the percentage of days when the sleep hours are below a threshold.

[0444] (Step S2405) The score acquiring unit 331 acquires, from the life information storage means 3129, drinking dates during the target period that are paired with the i-th member identifier.

[0445] (Step S2406) The score acquisition unit 331 acquires one or more drinking attribute values ​​using the one or more drinking days acquired in step S2405. The drinking attribute values ​​are, for example, the number of drinking days, the percentage of drinking days, and the maximum number of consecutive drinking days.

[0446] (Step S2407) The score acquiring unit 331 acquires from the life information storage means 3129 the number of cigarettes smoked for each day of the target period, which is the number of cigarettes paired with the i-th member identifier.

[0447] (Step S2408) The score acquisition unit 331 acquires one or more tobacco attribute values ​​using the one or more numbers acquired in step S2407. The tobacco attribute values ​​are, for example, the total number of cigarettes smoked during the period, the average number of cigarettes smoked per day, the number of days on which the number of cigarettes smoked is equal to or greater than a threshold, and the percentage of days on which the number of cigarettes smoked is equal to or greater than a threshold.

[0448] (Step S2409) The score obtaining unit 331 increments the counter i by 1. The process returns to step S2402.

[0449] (Step S2410) The score acquiring unit 331 acquires a representative value for each type of sleep attribute value using one or more sleep attribute values ​​of all target members acquired in step S2404. The representative value is, for example, the average sleep time, the average information showing the variation in sleep time, the average number of days when the sleep time is below a threshold, or the average percentage of days when the sleep time is below a threshold. The score acquiring unit 331 may acquire, for example, the number or percentage of members whose sleep time is so short that it satisfies a predetermined condition.

[0450] (Step S2411) The score acquisition unit 331 acquires a representative value for each type of drinking attribute value using one or more drinking attribute values ​​of all target members acquired in step S2406. The representative value is, for example, the average number of drinking days, the average percentage of drinking days, or the average maximum number of consecutive drinking days. The score acquisition unit 331 may acquire, for example, the number or percentage of members who have a large number of drinking days that meets a predetermined condition.

[0451] (Step S2412) The score acquisition unit 331 acquires a representative value for each type of tobacco attribute value using one or more of each of the tobacco attribute values ​​of all target members acquired in step S2408. The process returns to the upper level process. The representative value is, for example, the average total number of cigarettes smoked, the average number of cigarettes smoked, the average number of days on which the number of cigarettes smoked is equal to or greater than a threshold, or the average percentage of days on which the number of cigarettes smoked is equal to or greater than a threshold. The score acquisition unit 331 may acquire, for example, the number or percentage of members who smoke a large enough number of cigarettes to satisfy a predetermined condition.

[0452] Next, an example of the score acquisition process in step S1405 will be described with reference to the flowchart in FIG.

[0453] (Step S2501) The score obtaining means 3312 obtains inter-prediction information corresponding to a prediction instruction.

[0454] (Step S2502) The score obtaining means 3312 obtains a predicted organization identifier that is an organization identifier corresponding to the predicted instruction.

[0455] (Step S2503) The score acquisition means 3312 acquires a predicted daily information set used to acquire a predicted score. The predicted daily information set corresponds to the period specified by the predicted interval information acquired in step S2501, and is one or more pieces of daily information corresponding to the predicted organization identifier acquired in step S2502.

[0456] (Step S2504) The score acquiring means 3312 acquires a feature group using one or more pieces of predicted everyday life information acquired in step S2503. Such feature group acquisition processing has been described with reference to the flowchart of FIG.

[0457] (Step S2505) The score acquiring means 3312 acquires the correspondence information to be used in the prediction process from the correspondence information storage unit 314.

[0458] (Step S2506) The score acquisition means 3312 acquires a predicted vector having each feature of the feature group acquired in step S2504 as an element, and acquires a predicted score using the predicted vector and the correspondence relationship information acquired in step S2505. The process returns to the upper process. An example of the algorithm for acquiring the predicted score has been described above.

[0459] Next, an example of the operation of the terminal device 4 will be described with reference to the flowchart of FIG.

[0460] (Step S2601) The terminal reception unit 42 judges whether or not daily information has been acquired. If daily information has been acquired, the process proceeds to step S2602, and if daily information has not been acquired, the process proceeds to step S2605.

[0461] (Step S2602) The terminal processing unit 43 acquires the member identifier of the terminal storage unit 41.

[0462] (Step S2603) The terminal processing unit 43 configures information to be transmitted, which is information that associates the daily information acquired in step S2601 with the member identifier acquired in step S2602.

[0463] (Step S2604) The terminal transmitting unit 44 transmits the information constructed in step S2603 to the score prediction device 3. The process returns to step S2601.

[0464] (Step S2605) The terminal receiving unit 42 judges whether or not a prediction instruction has been received. If a prediction instruction has been received, the process proceeds to step S2606, and if a prediction instruction has not been received, the process returns to step S2601.

[0465] (Step S2606) The device processing unit 43 configures a prediction instruction to be sent. The prediction instruction to be sent includes, for example, an organization identifier and period information. The organization identifier is an identifier of the organization for which the score is to be predicted. The period information is information that specifies the period of the daily information used to predict the score. The organization identifier and the period information may have default values ​​and may be stored in the device storage unit 41.

[0466] (Step S2607) The terminal transmitting unit 44 transmits the prediction instruction constructed in step S2606 to the score prediction device 3.

[0467] (Step S2608) Terminal receiving unit 45 determines whether or not a predicted score has been received. If a predicted score has been received, the process proceeds to step S2609, and if a predicted score has not been received, the process returns to step S2608.

[0468] (Step S2609) The device processing unit 43 uses the predicted score received in step S2608 to configure a predicted score to be output. The terminal output unit 46 outputs the predicted score. Return to step S2601.

[0469] In the flowchart of FIG. 26, the process ends when the power is turned off or an interrupt occurs to end the process.

[0470] Specific operation examples of the score prediction device 3 in this embodiment will be described below. There are eight specific examples below. In each specific example, it is assumed that the daily information required for processing is stored in the daily information storage unit 312. In addition, in the following specific examples, a method of obtaining a predicted score using a learning device will be described, but as described above, a predicted score may be obtained using other correspondence relationship information such as a correspondence table or an arithmetic expression.

[0471] (Example 1) In a first specific example, a predictive score is obtained using a member's email response rate to emails from management and superiors.

[0472] The correspondence acquisition means 3311 of the score prediction device 3 acquires two or more member identifiers paired with the organization identifier "A" of the target organization. Next, the correspondence acquisition means 3311 acquires, for each unit period, emails that are paired with two or more member identifiers and are used to configure a learning device (here, one year) (for example, each year from 2000 to 2019) from the communication information storage means 3122 in association with the member identifiers.

[0473] Then, through the above-mentioned processing, the correspondence acquisition means 3311 acquires one or more response target member identifiers of the response recipients (management, superiors, etc.) that are paired with each member identifier from a member information database (a database of two or more member information) not shown in the figure for the organizational identifier "A".

[0474] Next, the response acquisition means 3311 calculates the response rate for each member identifier and for each unit period. Next, the response acquisition means 3311 acquires a representative value (for example, an average value) of the response rates of all members for each unit period.

[0475] Next, the correspondence acquisition means 3311 reads out the teacher score corresponding to each year from the teacher score storage unit 313. Note that the teacher score is associated with, for example, an identifier of the unit period (here, year) and an organization identifier "A".

[0476] Next, the correspondence acquisition means 3311 creates two or more pieces of teacher data with the representative value of the response rate as an explanatory variable and the teacher score as a response variable.

[0477] Then, the correspondence acquisition means 3311 provides the two or more pieces of teacher data to a module that performs machine learning learning processing, executes the module, acquires a learning device, and stores the learning device in the correspondence information storage unit 314 in a pair with the organization identifier "A". Note that the correspondence acquisition means 3311 may acquire a learning device by using two or more pieces of information such as the average response rate of all members, the median response rate of all members, and variance as explanatory variables for each unit period.

[0478] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 acquires, from the communication information storage means 3122, predicted daily information that is an email paired with the above organization identifier and is an email dated on or after January 1, 2020.

[0479] Next, the score acquisition means 3312 calculates the response rate for each member identifier using the acquired email for each member identifier. Next, the score acquisition means 3312 acquires a representative value (for example, an average value) of the response rates of all members.

[0480] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired response rate for 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a predicted score by executing the module. Note that the score acquisition means 3312 may perform prediction processing using two or more pieces of information, for example, the average response rate of all members, the median response rate of all members, and variance.

[0481] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0482] Then, the terminal device 4 receives and outputs the predicted score.

[0483] (Example 2) In the specific example 2, a predicted score is obtained using a daily report. In the specific example 2, the unit period is one year.

[0484] The correspondence acquisition means 3311 of the score prediction device 3 acquires two or more member identifiers paired with the organization identifier "A" of the target organization. Next, the correspondence acquisition means 3311 acquires daily reports paired with two or more member identifiers, for each year (for example, from 2000 to 2019), from the task input information storage means 3121 in association with the member identifiers and time identifiers.

[0485] Next, the correspondence acquisition means 3311 acquires one or more daily report attribute values ​​using each daily report for each member identifier and each year. The acquired daily report attribute values ​​are, for example, information on changes in data volume, the occurrence rate of negative words in daily reports for a specified period, and the occurrence rate of positive words in daily reports for a specified period.

[0486] Next, the correspondence acquisition means 3311 acquires a representative value (e.g., average value) of the daily report attribute values ​​in the daily reports of each member identifier for each year and for each type of daily report value. Next, the correspondence acquisition means 3311 acquires a representative value (e.g., average value) of the daily report attribute values ​​of all members for each year and for each type of daily report value. In other words, the correspondence acquisition means 3311 acquires, for example, the average of the change information of the data amount of all members of the organization with organization identifier "A" for each year, the average of the occurrence rate of negative words in the daily reports for a predetermined period (here, one year), and the average of the occurrence rate of positive words in the daily reports for a predetermined period.

[0487] Next, the correspondence acquisition means 3311 acquires a teacher vector for each year, the element of which is each acquired daily report attribute value. Next, the correspondence acquisition means 3311 reads out a teacher score corresponding to each year from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each year.

[0488] Next, the correspondence acquisition means 3311 provides the acquired multiple teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with the organization identifier “A” and stored in the correspondence information storage unit 314.

[0489] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires, from the task input information storage means 3121, predicted daily information that is a daily report paired with the above organization identifier “A” and is a daily report from January 1, 2020 onward.

[0490] Next, the score acquisition means 3312 acquires two or more daily report attribute values ​​for each member identifier using the acquired daily report for each member identifier. Next, the score acquisition means 3312 acquires a representative value (e.g., an average value) of each of the two or more daily report attribute values ​​of all members, and configures a predictive vector having the representative value of each of the two or more daily report attribute values ​​as elements. Note that the predictive vector is a vector having, for example, the average value of the change information of the data amount, the occurrence rate of negative words, and the occurrence rate of positive words as elements.

[0491] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired prediction vector for 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a prediction score by executing the module.

[0492] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0493] Then, the terminal device 4 receives and outputs the predicted score.

[0494] (Example 3) In the specific example 3, a predicted score is obtained by using overtime hours in the attendance status information. In the specific example 3, the unit period is one month.

[0495] The correspondence acquisition means 3311 of the score prediction device 3 acquires two or more member identifiers that are paired with the organization identifier "A" of the target organization. Next, the correspondence acquisition means 3311 acquires attendance information that is paired with two or more member identifiers, for one year for each year (for example, from 2000 to 2019), from the attendance status information storage means 3123, in association with the member identifier and the time identifier.

[0496] Next, the correspondence acquisition means 3311 calculates the average monthly overtime hours for each member identifier and each year using the attendance information for each member identifier and each year. Next, the correspondence acquisition means 3311 acquires the average and variance of the overtime hours for all members of organization "A" for each year.

[0497] Next, the correspondence acquisition means 3311 acquires a teacher vector for each year, the elements of which are the average value and variance of the overtime hours of all the acquired members. Next, the correspondence acquisition means 3311 reads out the teacher score corresponding to each year from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each year.

[0498] Next, the correspondence acquisition means 3311 provides the acquired multiple teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with the organization identifier “A” and stored in the correspondence information storage unit 314.

[0499] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires, from the attendance status information storage means 3123, predicted daily information, which is attendance information paired with the above organization identifier “A” and is attendance information from January 1, 2020 onwards.

[0500] Next, the score acquisition means 3312 calculates the average monthly overtime hours for each member identifier using the attendance information acquired for each member identifier. Next, the score acquisition means 3312 acquires the average and variance of the overtime hours of all members. Next, the score acquisition means 3312 constructs a prediction vector whose elements are the average and variance of the overtime hours of all members.

[0501] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired prediction vector for 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a prediction score by executing the module.

[0502] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0503] Then, the terminal device 4 receives and outputs the predicted score.

[0504] (Example 4) In this example, a predicted score is obtained using stress information and measurement information. In specific example 4, the unit period is one year. In this specific example, the stress information (e.g., stress level) is obtained by the score obtaining unit 331 using the method described above, and stored in the stress information storage means 3125 in association with the member identifier and the time identifier. In addition, the score obtaining unit 331 obtains attribute values ​​(e.g., average value, variance, etc.) of the stress information of all members for each year, and stores them in the stress information storage means 3125 in association with the organization identifier "A" and the identifier for each year.

[0505] It is also assumed that the measurement information (heart rate, blood pressure) is received by the score prediction device 3 from a wearable device (not shown) worn by the member, and stored in the measurement information storage means 3126 in association with the member identifier and the time identifier. It is also assumed that the score acquisition unit 331 acquires the representative heart rate value and representative blood pressure value for each member identifier and each year, and also acquires the representative heart rate value and representative blood pressure value of all members for each year, and stores them in the measurement information storage means 3126 in association with the organization identifier "A" and the identifier for each year.

[0506] In this situation, the correspondence acquisition means 3311 of the score prediction device 3 acquires, for each year, a teacher vector whose elements are the attribute value of the stress information, the representative value of the heart rate, and the representative value of the blood pressure for each year that are paired with the organization identifier "A" of the target organization. Next, the correspondence acquisition means 3311 reads out the teacher score corresponding to each year from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each year.

[0507] Next, the correspondence acquisition means 3311 provides the acquired multiple teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with the organization identifier “A” and stored in the correspondence information storage unit 314.

[0508] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires stress information paired with the above organization identifier "A" and dated on or after January 1, 2020 from the stress information storage means 3125. In addition, the score acquisition means 3312 acquires attribute values ​​of the stress information (e.g., average value, variance, etc.) from the acquired stress information.

[0509] In addition, the score acquisition means 3312 acquires the measurement information (heart rate, blood pressure) paired with the organization identifier "A" on or after January 1, 2020 from the measurement information storage means 3126. Next, the score acquisition means 3312 acquires a representative value of each of the acquired measurement information.

[0510] Next, the score acquisition means 3312 constructs a prediction vector whose elements are the attribute value of the stress information and the representative value of each piece of measurement information.

[0511] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired prediction vector for 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a prediction score by executing the module.

[0512] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0513] Then, the terminal device 4 receives and outputs the predicted score.

[0514] (Example 5) Specific example 5 is a case where a predicted score is obtained using communication information. In specific example 5, the unit period is one month.

[0515] The correspondence acquisition means 3311 of the score prediction device 3 acquires two or more member identifiers paired with the organization identifier "A" of the target organization. Next, the correspondence acquisition means 3311 acquires communication information paired with the two or more member identifiers from the communication information storage means 3122 in association with time information. Note that the communication information is, for example, one or more types of information among chat information, SNS information, e-mail, and text information.

[0516] Next, the correspondence acquisition means 3311 acquires one or more types of communication attribute values ​​using communication information for each member identifier and for each month. The acquired communication attribute values ​​are, for example, the number of occurrences of negative words, the rate of occurrence of negative words, the number of occurrences of positive words, and the rate of occurrence of positive words. Next, the correspondence acquisition means 3311 acquires a representative value (for example, an average value) of the communication attribute values ​​of all members for each month.

[0517] Next, the correspondence acquisition means 3311 acquires a teacher vector for each month, the elements of which are each of the acquired communication attribute values. Next, the correspondence acquisition means 3311 reads out a teacher score corresponding to each month from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each month. In this case, it is preferable that the teacher score is stored in the teacher score storage unit 313 in association with an identifier for each month, but, for example, only one teacher score may be stored in the teacher score storage unit 313 for each year. When only one teacher score is stored in the teacher score storage unit 313 for each year, the teacher score for each month of a certain year will be the same teacher score.

[0518] Next, the correspondence acquisition means 3311 provides the acquired large number of teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with the organization identifier “A” and stored in the correspondence information storage unit 314.

[0519] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires, from the communication information storage means 3122, communication information for May 2020 that is paired with the above organization identifier “A”.

[0520] Next, the score acquisition means 3312 acquires a communication attribute value for each member identifier using the acquired communication information. Next, the score acquisition means 3312 acquires a representative value (for example, an average value) of each communication attribute value of all members, and configures a prediction vector having the representative value of each of the two or more communication attribute values ​​as an element.

[0521] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired prediction vector for May 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a prediction score by executing the module.

[0522] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0523] Then, the terminal device 4 receives and outputs the predicted score.

[0524] (Example 6) Specific example 6 is a case where a predicted score is obtained using audio information from a video call. Assume that a large number of pieces of audio information (audio files) are stored in the audio storage means 3128 in association with an organization identifier "A" and time information. In specific example 6, the unit period is one year.

[0525] In such a situation, the correspondence acquisition means 3311 of the score prediction device 3 acquires, for each year, speech information paired with the organization identifier "A" of the target organization.

[0526] Next, the correspondence acquisition means 3311 performs spectrum analysis on each acquired voice information for each year, and acquires two or more voice features. The correspondence acquisition means 3311 also acquires a representative value of the feature for each year and for each type of feature from the two or more voice features. Next, the correspondence acquisition means 3311 acquires a teacher vector whose elements are the representative values ​​of each type of feature for each year. Next, the correspondence acquisition means 3311 reads out the teacher score corresponding to each year from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each year.

[0527] Next, the correspondence acquisition means 3311 provides the acquired multiple teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with the organization identifier “A” and stored in the correspondence information storage unit 314.

[0528] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires, from the voice storage means 3128, voice information that is paired with the above organization identifier “A” and is voice information from January 1, 2020 onward.

[0529] Next, the correspondence acquisition means 3311 performs spectrum analysis on each piece of acquired voice information after January 1, 2020, and acquires two or more voice features. The correspondence acquisition means 3311 also acquires a representative value of each type of feature from the two or more voice features. Next, the correspondence acquisition means 3311 acquires a prediction vector whose elements are the representative values ​​of each type of feature.

[0530] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired prediction vector for 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a prediction score by executing the module.

[0531] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0532] Then, the terminal device 4 receives and outputs the predicted score.

[0533] (Example 7) In this case, facial expression analysis is performed on a videophone image, and a predicted score is obtained from a specific facial expression. It is assumed that a large number of images (video files) are stored in the image storage means 3127 in association with an organization identifier "A" and time information. In the specific example 7, the unit period is one year.

[0534] In such a situation, the correspondence acquisition means 3311 of the score prediction device 3 acquires an image paired with the organization identifier "A" of the target organization for each year. Next, the correspondence acquisition means 3311 performs facial expression analysis processing on each image to acquire a facial expression identifier (e.g., any of joy, anger, sadness, and happiness, normal, etc.). Next, the correspondence acquisition means 3311 acquires the proportion of negative facial expressions ("anger", "sadness") for each year.

[0535] Next, the correspondence acquisition means 3311 acquires teacher data for each year, with the ratio of negative facial expressions as an explanatory variable and the teacher score for each year as a response variable. The teacher score for each year is stored in the teacher score storage unit 313.

[0536] Next, the correspondence acquisition means 3311 provides the acquired multiple teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with the organization identifier “A” and stored in the correspondence information storage unit 314.

[0537] Next, it is assumed that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires, from the audio storage means 3128, images paired with the above organization identifier “A” and taken on or after January 1, 2020.

[0538] Next, the correspondence acquisition means 3311 performs facial expression analysis processing on each acquired image after January 1, 2020, and acquires a facial expression identifier. Next, the correspondence acquisition means 3311 acquires the proportion of negative facial expressions ("anger" and "sorrow") after January 1, 2020.

[0539] Next, the score acquisition means 3312 acquires the learning device paired with the organization identifier "A" from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired ratio of negative facial expressions in 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a predicted score by executing the module.

[0540] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0541] Then, the terminal device 4 receives and outputs the predicted score.

[0542] (Example 8) In the eighth specific example, a predicted score is obtained by using the daily information of other organizations that match the condition of the organization identifier of the relevant organization. In the eighth specific example, the unit period is one year.

[0543] It is assumed that the score prediction device 3 has received, from the terminal device 4, a prediction instruction including organization conditions having an industry identifier "trading company" and a scale identifier "large company", and an organization identifier "A" of the organization to be predicted.

[0544] Next, the correspondence acquisition means 3311 acquires one or more types of daily information corresponding to the business type identifier "trading company" and the scale identifier "large company" from the daily information storage section 312 for each organization identifier and for each year.

[0545] Next, the correspondence acquisition means 3311 constructs a teacher vector using the acquired daily information for each organization identifier and each year. The correspondence acquisition means 3311 also reads out the teacher score corresponding to each organization identifier and each year from the teacher score storage unit 313.

[0546] Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each organization identifier and each year.

[0547] Next, the correspondence acquisition means 3311 provides the acquired multiple pieces of teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device. The learning device is paired with organizational conditions (here, the industry identifier "trading company" and the size identifier "large company") and stored in the correspondence information storage unit 314.

[0548] Next, the score acquisition means 3312 pairs with the organization identifier “A” included in the received prediction instruction and acquires one or more types of daily information for the prediction period (e.g., after January 1, 2020) from the daily information storage unit 312.

[0549] Next, the score acquisition means 3312 constructs a prediction vector for 2020 using the acquired daily information.

[0550] Next, the score acquisition means 3312 acquires the learning device from the correspondence information storage unit 314. Next, the score acquisition means 3312 provides the acquired prediction vector for 2020 and the learning device to a module that performs machine learning prediction processing, and acquires a prediction score by executing the module.

[0551] Next, the score output unit 341 transmits the predicted score to the terminal device 4.

[0552] Then, the terminal device 4 receives and outputs the predicted score.

[0553] As described above, according to this embodiment, the score of an organization can be predicted using daily information about the members of the organization.

[0554] Furthermore, according to this embodiment, the score of an organization can be predicted using one or more types of information from among business information, input information, communication information, attendance status information, work result information, health information, biological information, stress information, measurement information, audio, images, lifestyle information, etc.

[0555] Furthermore, according to this embodiment, it is possible to predict an organization's score using scores based on past questionnaires and daily information, without conducting a questionnaire.

[0556] Furthermore, according to this embodiment, the score of an organization can be predicted using machine learning technology, data mining technology, and the like.

[0557] In this embodiment, the type of daily information to be used does not matter, but it is preferable to use two or more types of daily information in this embodiment.

[0558] Furthermore, the processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software for realizing the score prediction device 3 in this embodiment is the following program. That is, this program causes a computer to function as a score acquisition unit that acquires a predicted score that is a score corresponding to the one or more predicted daily information using a correspondence relationship between a teacher score, which is an organization score acquired using a plurality of organization answer information indicating answers to questions for each of two or more members of the organization, and one or more teacher daily information, which is daily information in a period corresponding to the questions or the answers and can be acquired in the daily lives of each of the two or more members, and one or more predicted daily information, which is daily information of each of the two or more members of the organization and is daily information in a period to be predicted for the score of the organization, and a score output unit that outputs the predicted score.

[0559] FIG. 27 shows the appearance of a computer that executes the program described in this specification to realize the score prediction device 3 and the like of the various embodiments described above. The above-mentioned embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 27 is an overview of this computer system 300, and FIG. 28 is a block diagram of the system 300.

[0560] In FIG. 27, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0561] 28, 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.

[0562] A program for causing computer system 300 to execute functions such as score prediction device 3 of the above-mentioned embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and further transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 when executed. The program may be loaded directly from CD-ROM 3101 or the network.

[0563] 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 score prediction device 3 of the above-mentioned embodiment. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner to obtain a desired result. How the computer system 300 operates is well known, and a detailed description will be omitted.

[0564] In addition, in the above program, the steps of transmitting information and receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmitting step (processing that is performed only by hardware).

[0565] The program may be executed by a single computer or a plurality of computers, that is, centralized processing or distributed processing may be performed.

[0566] 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.

[0567] 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.

[0568] 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]

[0569] INDUSTRIAL APPLICABILITY As described above, the score prediction device according to the present invention has an effect of being able to predict the score of an organization by using daily information of the members of the organization, and is useful as a score prediction device, etc. [Explanation of symbols]

[0570] 3. Score Predictor 4 Terminal Equipment 31 Storage area 32 Reception 33 Processing section 34 Output section 41 Terminal storage section 42 Terminal Reception 43 Terminal Processing Section 44 Terminal transmitter 45 Terminal receiving section 46 Terminal Output Section 311 Organization Information Storage Unit 312 Daily Information Storage Unit 313 Teacher score storage 314 Correspondence information storage section 331 Score Acquisition Section 341 Score Output Unit 3121 Business input information storage means 3122 Communication information storage means 3123 Attendance status information storage means 3124 Business result information storage means 3125 Stress information storage means 3126 Measurement information storage means 3127 Image storage means 3128 Audio storage means 3129 Lifestyle information storage means 3311 Correspondence acquisition means 3312 Score acquisition method

Claims

1. a correspondence information storage unit for storing correspondence information relating to a correspondence between a teacher score, which is an organization score obtained using a plurality of organization answer information indicating answers to questions for each of two or more members of the organization, and two or more pieces of teacher daily information, which are daily information during a period corresponding to the questions or the answers and can be obtained in the daily lives of each of the two or more members; a score acquisition unit that acquires a predicted score, which is the score of the organization corresponding to the two or more predicted daily information, using the correspondence information and two or more predicted daily information, which is daily information of two or more members of the organization and is daily information during a period for which the score of the organization is to be predicted; A score output unit that outputs the predicted score, The two or more pieces of teacher daily information and the two or more pieces of predicted daily information include an operation log of a device used by each of the two or more members of the organization for work, a program created by each of the two or more members of the organization, a document created by each of the two or more members of the organization, or a daily report created by each of the two or more members of the organization. Alternatively, the two or more pieces of teacher daily information and the two or more pieces of predicted daily information include chat information entered by each of the two or more members of the organization into a chat system or chat app, SNS information entered by each of the two or more members of the organization into an SNS, emails created by each of the two or more members of the organization, or text information obtained by converting voices uttered by each of the two or more members of the organization in a video conference into text, Or, each of the two or more teacher daily information and each of the two or more predicted daily information includes attendance status information regarding attendance of each of the two or more members of the organization, or each of the two or more teacher daily information and each of the two or more predictive daily information includes health information relating to the health of two or more members of the organization; The score acquisition unit, A correspondence acquisition means for acquiring correspondence information that specifies a correspondence between the two or more pieces of teacher daily information and the teacher score; A score prediction device comprising: a score acquisition means for acquiring the predicted score using the correspondence information and the two or more pieces of predicted daily life information.

2. A score acquisition unit that acquires a teacher score, which is the score of an organization obtained using a plurality of organization answer information indicating answers to questions for each of two or more members of the organization, and a correspondence between two or more teacher daily information, which is daily information for a period corresponding to the questions or the answers and which can be acquired in the daily lives of each of the two or more members, and two or more predicted daily information, which is daily information for each of the two or more members of the organization and is daily information for a period for which the score of the organization is predicted, to acquire a predicted score, which is the score of the organization corresponding to the two or more predicted daily information; A score output unit that outputs the predicted score, The two or more pieces of teacher daily information and the two or more pieces of predicted daily information include an operation log of a device used by each of the two or more members of the organization for work, a program created by each of the two or more members of the organization, a document created by each of the two or more members of the organization, or a daily report created by each of the two or more members of the organization. Alternatively, the two or more pieces of teacher daily information and the two or more pieces of predicted daily information include chat information entered by each of the two or more members of the organization into a chat system or chat app, SNS information entered by each of the two or more members of the organization into an SNS, emails created by each of the two or more members of the organization, or text information obtained by converting voices uttered by each of the two or more members of the organization in a video conference into text, Or, each of the two or more teacher daily information and each of the two or more predicted daily information includes attendance status information regarding attendance of each of the two or more members of the organization, or each of the two or more teacher daily information and each of the two or more predictive daily information includes health information relating to the health of two or more members of the organization; The teacher daily information includes teacher input information input by the member, The predicted daily information includes predicted input information to be input by the member, The score acquisition unit, a score prediction device that acquires one or more types of information among data amount information that identifies the data amount of the teacher input information, input frequency information that identifies the input frequency of the teacher input information, analysis information that indicates an analysis result of the teacher input information, and timing information that identifies the timing of the input of the teacher input information, and acquires one or more types of information among data amount information of the predicted input information, input frequency information of the predicted input information, analysis information of the predicted input information, and timing information of the predicted input information, acquires correspondence information that identifies a correspondence between one or more teacher features including one or more types of information acquired from the teacher input information and the teacher score, and acquires the predicted score using the correspondence information and one or more prediction features including one or more types of information acquired from the predicted input information.

3. Each of the two or more teacher daily information includes teacher health information related to the health of the member; each of the two or more pieces of predictive daily information includes predictive health information regarding the health of the member; 3. The score prediction device according to claim 1, wherein the teacher health information includes teacher biological information relating to a biological body of the member, and the predicted health information includes predicted biological information relating to a biological body of the member.

4. The score acquisition unit, The score prediction device according to claim 3 , wherein the predicted score is obtained using the correspondence between two or more pieces of teacher routine information including the teacher biometric information and the teacher score, and two or more pieces of predicted routine information including the predicted biometric information.

5. The teacher biological information includes teacher stress information related to the stress of the member, The predicted biological information includes predicted stress information regarding the stress of the member, The score acquisition unit, The score prediction device according to claim 4 , wherein the predicted score is obtained using the correspondence between two or more pieces of teacher daily routine information including the teacher stress information and the teacher score, and two or more pieces of predicted daily routine information including the predicted stress information.

6. The teacher biometric information includes teacher measurement information which is a measurement result of the member's biometric information, The predicted biological information includes predicted measurement information obtained from the biological body of the member, The score acquisition unit, The score prediction device according to claim 4 , wherein the predicted score is obtained using the correspondence between two or more pieces of teacher routine information including the teacher measurement information and the teacher score, and two or more pieces of predicted routine information including the predicted measurement information.

7. The teacher biometric information includes teacher biometric feature information which is an image of the member, or a feature of the image of the member, or a voice uttered by the member, or a feature of the voice uttered by the member, The predicted biometric information includes predicted biometric feature information that is an image of the member, or a feature of the image of the member, or a voice uttered by the member, or a feature of the voice uttered by the member, The score acquisition unit, The score prediction device according to claim 4, wherein the predicted score is obtained using the correspondence between two or more pieces of teacher routine information including the teacher biometric characteristic information and the teacher score, and two or more pieces of predicted routine information including the predicted biometric characteristic information.

8. The teacher health information includes teacher life information relating to the life of the member; the predictive health information includes predictive lifestyle information regarding the member's lifestyle; The score acquisition unit, The score prediction device according to claim 4 , wherein the predicted score is obtained using the correspondence between two or more pieces of teacher daily life information including the teacher daily life information and the teacher score, and two or more pieces of predicted daily life information including the predicted daily life information.

9. The two or more pieces of teacher daily information and the teacher score correspond to one or more organizational attribute values, The correspondence acquisition means includes: Obtaining correspondence information that specifies a correspondence between the two or more pieces of teacher daily information and the teacher score for each organization condition configured using the one or more organization attribute values; The score acquisition means includes: The score prediction device according to claim 1 , wherein the predicted score is obtained using the correspondence information corresponding to an organizational condition that the organization to be predicted meets and the two or more pieces of predicted daily information.

10. The correspondence acquisition means includes: Using the two or more pieces of teacher routine information and the teacher score, a learning process is performed by a machine learning algorithm, and correspondence information is obtained which is a learning model having the two or more pieces of teacher routine information as explanatory variables and the teacher score as a target variable; The score acquisition means includes: The score prediction device according to claim 1 or claim 9, further comprising: a machine learning algorithm that uses the learning model and the two or more pieces of predicted everyday information to perform a prediction process and obtain the predicted score.

11. The correspondence acquisition means includes: Using the two or more pieces of teacher routine information, a teacher vector is obtained, which is a vector having two or more elements, and correspondence relationship information is obtained, which is a correspondence table having two or more pieces of correspondence information that are pairs of the teacher vector and the teacher score; The score acquisition means includes: The score prediction device according to claim 1 or claim 9, further comprising: a prediction vector, which is a vector having two or more elements, obtained by using the two or more pieces of predicted everyday information; applying the prediction vector to the correspondence table; and obtaining the predicted score.

12. The correspondence acquisition means includes: Using the two or more pieces of teacher routine information, a teacher vector is obtained, which is a vector having two or more elements, and correspondence information is obtained which is an arithmetic formula in which each element of the teacher vector is input and the teacher score is output; The score acquisition means includes: A score prediction device as described in claim 1 or claim 9, which uses the two or more pieces of predicted everyday information to obtain a prediction vector, which is a vector having two or more elements, provides each element of the prediction vector to the arithmetic expression, executes the arithmetic expression, and obtains the predicted score.

13. A score prediction method which executes all of the processing performed by a score prediction device described in any one of claims 1 to 12.

14. Computer, A program for causing the score prediction device according to claim 1 to function as the score prediction device.

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