JP7651272B2 - SCORE PREDICTION DEVICE, SCORE PREDICTION METHOD, AND PROGRAM - Google Patents

Description

The present invention relates to a score prediction device that predicts the score of an organization.

Previously, there was a management diagnostic support device that could comprehensively assess the soundness of management (see Patent Document 1).

This management diagnostic support device compiles and analyzes the results of a score-based questionnaire in which answers to questions are selected from multiple options, and is equipped with a storage means for storing the results of the questionnaire answered by each level of an organization, an analysis table in which multiple diagnostic elements related to corporate management are assigned to each of the multiple questions, and a score allocation indicating a weighting is set for each diagnostic element corresponding to the multiple questions, an analysis means for analyzing the results of the questionnaire, and an output means for outputting the analysis results by the analysis means, in which the analysis means multiplies the answer score based on the answer result to the question item by the score allocation for each diagnostic element set in the analysis table corresponding to the question item to calculate an individual element evaluation value for each diagnostic element of each question item, and calculates an individual element summary value by tallying up the individual element evaluation values of all questions for each diagnostic element, and the output means tallying up the individual element summary value for each level and outputting it as a graph or table.

Patent No. 6621903

However, conventional technology only presented information that could be used to judge the soundness of management by compiling and analyzing the results of a survey, and it was not possible to predict an organization's score using the daily information of its members.

The score prediction device of the first invention is a score prediction device that includes: a daily information storage unit that 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 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 score acquisition unit that acquires a predicted score, which is a score corresponding to the one or more predicted daily information, using a correspondence relationship between the teacher daily information, which is daily information for each of two or more members, and the one or more predicted daily information; and a score output unit that outputs the predicted score.

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

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 pieces of teacher daily information includes teacher task information related to the tasks performed by the members, each of the one or more pieces of predicted daily information includes predicted task information related to the tasks performed by the members, and the score acquisition unit acquires a predicted score using the correspondence between the one or more pieces of teacher daily information including the teacher task information and the teacher score, and the one or more pieces of predicted daily information including the predicted task information.

With this configuration, the organization's score can be predicted using work information related to the work performed by members of the organization.

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 input by members, the predicted work information includes predicted input information input by 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 input information and the teacher score, and one or more pieces of predicted daily information including the predicted input information.

With this configuration, the organization's score can be predicted using the input information entered by members of the organization.

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 performance of the member's own work, the predicted input information includes predicted work input information related to input for the performance of the member's own work, and the score acquisition unit acquires a predicted score using a correspondence between one or more pieces of teacher daily information including the teacher work input information and the teacher score, and one or more pieces of predicted daily information including the predicted work input information.

With this configuration, the organization's score can be predicted using teacher work input information related to inputs made by members of the organization in carrying out their own work.

The score prediction device of the fifth invention is a score prediction device according to the second or third invention, in which the teacher input information includes teacher communication information relating to communication between two or more members, the predicted input information includes predicted communication information relating to 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 the teacher score, and one or more pieces of predicted daily information including the predicted communication information.

With this configuration, communication information regarding communication between members of an organization can be used to predict the organization's score.

The score prediction device of the sixth invention is a score prediction device according to any one of the third to fifth inventions, in which the score acquisition unit acquires one or more types of information from 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 from 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 relationship information identifying the correspondence relationship 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 relationship information and one or more prediction features including one or more types of information acquired from the predicted input information.

With this configuration, the score of an organization can be predicted using one or more types of information from the following: data volume information of the input information, input frequency information, analysis information of the input information, and timing information of the input information.

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 relating to the attendance of members, the predicted work information includes predicted attendance status information relating to the attendance of 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 attendance status information and teacher scores, and one or more pieces of predicted daily information including the predicted attendance status information.

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

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 relating to the results of the members' work performance, the predicted work information includes predicted work result information relating to the results of the members' work performance, and the score acquisition unit acquires a predicted score using a correspondence between one or more pieces of teacher daily information including teacher work result information and teacher scores, and one or more pieces of predicted daily information including predicted work result information.

With this configuration, the organization's score can be predicted using business result information regarding the results of the organization's business operations.

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, 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.

With this configuration, the health information of the organization's members can be used to predict the organization's score.

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 relating to the member's biometrics, the predicted health information includes predicted biometric information relating to the member's biometrics, and the score acquisition unit acquires a predicted score using a correspondence between one or more pieces of teacher daily information including the teacher biometric information and the teacher score, and one or more pieces of predicted daily information including the predicted biometric information.

With this configuration, the organization's score can be predicted using the biometric information of the organization's members.

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 the teacher stress information and the teacher score, and one or more pieces of predicted daily information including the predicted stress information.

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

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 a measurement result of the member's biometrics, the predicted biometric information includes predicted measurement information obtained from the member's biometrics, and the score acquisition unit acquires a predicted score using a correspondence relationship between one or more pieces of teacher daily information including the teacher measurement information and the teacher score, and one or more pieces of predicted daily information including the predicted measurement information.

With this configuration, the organization's score can be predicted using measurement information that is the biological measurement results of members of the organization.

The score prediction device of the thirteenth invention is a score prediction device according to any one of the tenth to twelfth inventions, in which the teacher biometric information includes teacher biometric characteristic information which is an image of a member, or a feature of an 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 an 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 which acquires a predicted score using a correspondence between one or more pieces of teacher daily information including the teacher biometric characteristic information and the teacher score, and one or more pieces of predicted daily information including the predicted biometric characteristic information.

With this configuration, an organization's score can be predicted using images of the organization's members or their voices.

The score prediction device of the fourteenth invention is a score prediction device according to any one of the ninth to thirteenth inventions, in which 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.

With this configuration, the organization's score can be predicted using lifestyle information about its members.

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

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

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 organizational 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 organizational condition configured using one or more organizational attribute values, and the score acquisition means acquires a predicted score using the correspondence information corresponding to the organizational condition matched by the organization to be predicted and one or more pieces of predicted routine information.

This configuration makes it possible to obtain a prediction score that matches the organization's attribute values.

The score prediction device of the seventeenth invention is a score prediction device according to the fifteenth or sixteenth invention, in which the correspondence acquisition means performs a learning process using one or more pieces of teacher routine information and teacher scores with a machine learning algorithm to acquire correspondence information that is a learning model with one or more pieces of teacher routine information as explanatory variables and teacher scores as objective variables, and the score acquisition means performs a prediction process using the learning model and one or more pieces of predicted routine information with a machine learning algorithm to acquire a predicted score.

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

The score prediction device of the eighteenth invention is a score prediction device according to the fifteenth or sixteenth invention, in which the correspondence acquisition means acquires a teacher vector, which is a vector having two or more elements, using one or more pieces of teacher routine information, and acquires correspondence relationship information, which is a correspondence table having one or more pieces of correspondence information that are pairs of teacher vectors and teacher scores, and the score acquisition means acquires a predicted vector, which is a vector having two or more elements, using one or more pieces of predicted routine information, and applies the predicted vector to the correspondence table to acquire a predicted score.

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

The score prediction device of the 19th invention is a score prediction device according to the 15th or 16th invention, in which the correspondence acquisition means acquires a teacher vector, which is a vector having two or more elements, using one or more pieces of teacher routine information, and acquires correspondence information which is an arithmetic formula that takes each element of the teacher vector as an input and outputs a teacher score, and the score acquisition means acquires a prediction vector, which is a vector having two or more elements, using one or more pieces of predicted routine information, and provides each element of the prediction vector to the arithmetic formula, executes the arithmetic formula, and acquires a predicted score.

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

The score prediction device of the present invention can predict an organization's score using daily information about its members.

Conceptual diagram of information system A in embodiment 1. Block diagram of the information system A A flowchart illustrating an example of the operation of the information processing device 1. A flowchart illustrating an example of the score calculation process A flowchart illustrating an example of the influence degree calculation process. The figure shows the item information management table. A diagram showing the organization's response information A diagram showing the individual score table A diagram showing an example of the output A diagram showing an example of the output Conceptual diagram of information system B in embodiment 2 Block diagram of information system B Block diagram of the score prediction device 3 A flowchart illustrating an example of the operation of the score prediction device 3. A flowchart illustrating an example of the correspondence acquisition process. Flowchart for explaining an example of isotropy group acquisition processing A flowchart illustrating an example of the business input information processing A flowchart illustrating an example of the communication information processing A flowchart for explaining an example of the attendance status information processing A flowchart illustrating an example of the stress information processing A flowchart illustrating an example of the measurement information processing A flowchart illustrating an example of the image information processing A flowchart illustrating an example of the voice information processing A flowchart illustrating an example of the lifestyle information processing A flowchart illustrating an example of the score acquisition process A flowchart for explaining an example of the operation of the terminal device 4 Overview of the computer system according to the above embodiment. Block diagram of the computer system

Embodiments of a score prediction device and the like will be described below with reference to the drawings. Note that components with the same reference numerals in the embodiments perform similar operations, and therefore may not be described again.

(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 referred to as an engagement score. The information system may also be referred to as an engagement system. The information processing device may also be referred to as an engagement device.

In this embodiment, an information system including an information processing device that calculates item scores and an overall score for the organization using the expectation level of each item is described. The information system may also be called an engagement system.

In addition, in this embodiment, an information system including an information processing device having a score adjustment function that adjusts the overall score using correlation information regarding the correlation between satisfaction and expectation will be described.

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 impact degree for each attribute value is described.

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

Figure 2 is a block diagram of information system A in this embodiment.

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 organizational 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 impact degree acquisition unit 133. The output unit 14 includes an item score output unit 141, a total score output unit 142, and an impact degree output unit 143.

The terminal device 2 includes a terminal storage unit 21, a terminal reception unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal reception unit 25, and a terminal output unit 26.

Various types of information are stored in the storage unit 11 constituting the information processing device 1. The various types of information include, for example, item information (described later), organizational response information (described later), and an individual score table (described later). The individual score table may also be referred to as an engagement score table, etc.

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 related to comprehensive matters of the organization and individual items that are individual items of 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 for four comprehensive items and item information for 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, etc. The item identifier may be the question information itself. The question information is information indicating a question. Note that questions are usually questions asked in a survey. Note that matters can also be called subjects or items.

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

In addition, examples of comprehensive items include company satisfaction, which indicates the degree of satisfaction with the company, job satisfaction, which indicates the degree of satisfaction with the job, boss satisfaction, which indicates the degree of satisfaction with the boss, and workplace satisfaction, which indicates the degree of satisfaction with the workplace. In addition, examples of individual items include the company's business advantages, the dissemination and communication of strategic goals, an overall sense of solidarity, and the appropriateness of evaluations and wages.

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

The individual score table storage unit 113 stores an individual score table. The individual score table here is information for determining item scores when satisfaction information and expectation information are given. The individual score table here 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 higher the item score obtained, and the smaller the expectation indicated by the expectation information, the higher the item score obtained.

When determining the item score, it may be determined by an arithmetic formula with satisfaction information and expectation information as parameters. It is preferable that such an arithmetic formula is an increasing function with satisfaction information as a parameter, and a decreasing function with expectation information as a parameter.

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 learned information are applied to acquire item scores by machine learning. Note that the machine learning used here may be, for example, SVR, deep learning, decision trees, etc. However, any machine learning algorithm may be used.

The reception unit 12 receives various information and instructions. Examples of the various information and instructions include instructions to output scores, organizational response information, and questionnaire response information (member response information). An instruction to output scores is an instruction to output scores, etc. Here, reception usually refers to reception from the terminal device 2, but it may also be understood as a concept that includes reception of information input from an input device such as a keyboard, mouse, or touch panel, and reception of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

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 impact acquisition unit 133. The various types of processing are, for example, processing to accumulate received organizational response information, questionnaire response information, etc. in the storage unit 11.

The item score acquisition unit 131 statistically processes the response information of members for each of two or more items contained in each of two or more organizational response information, and acquires item scores for each organization and each item.

The item score acquisition unit 131, for example, for each organization, performs statistical processing on the satisfaction information included in the response information for each of two or more items included in the organizational response information of the organization, and acquires an item score for each organization and each item. For example, the item score acquisition unit 131 may 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. In addition, the item score acquisition unit 131 may calculate a weighted average value of the satisfaction information by applying different weights according to the attribute values of the members, for example, 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, gender, etc. 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 it more heavily to calculate a weighted average.

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

It is preferable that the item score acquisition unit 131 acquires the item score so that, for example, the higher the satisfaction information, the higher the score, and the lower the expectation information, the higher the score.

The item score acquisition unit 131, for example, calculates the average value of the satisfaction information contained in the response information and the average value of the expectation information contained in the response 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.

The item score acquisition unit 131 may, for example, apply satisfaction information and expectation information to an individual score table to acquire an item score for each item. The item score acquisition unit 131 may also, for example, apply a statistical processing result of satisfaction information and a statistical processing result of expectation information to an individual score table to acquire an item score for each item. The item score acquisition unit 131 may, for example, apply an average value of satisfaction information and an average value of expectation information to an individual score table to acquire an item score for each item. The item score acquisition unit 131 may, for example, apply a weighted average value of satisfaction information and a weighted average value of expectation information to an 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.

The item score acquisition unit 131 may calculate the item score for each item, for example, using an increasing function with the average satisfaction information as a parameter and a decreasing function with the average expectation information as a parameter.

The overall score acquisition unit 132 acquires an overall score for each of two or more pieces of organizational response information using two or more item scores. The overall score is a comprehensive score for each organization. The overall score acquisition unit 132 typically acquires a higher overall score for two or more items with better scores.

It is preferable that the overall score acquisition unit 132 acquires the overall score using a score adjustment function as follows. The score adjustment function is a function that adjusts the score using correlation information regarding the degree of correlation between satisfaction information and expectation information. Note that it is preferable that the score adjustment function be such that the greater the correlation between satisfaction information and expectation information, the higher the overall score becomes.

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

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 obtain the overall score.

The overall score acquisition unit 132 may calculate the overall score, for example, by "overall 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). In other words, 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.

The overall score acquisition 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 then adjust the scores using the score adjustment function described above to calculate the overall score. Furthermore, the overall score acquisition unit 132 may calculate a standard deviation of the overall score of each organization using the overall scores of multiple organizations, and use this standard deviation as the final overall score.

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

The impact acquisition unit 133 acquires, for each item, the impact 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 impact is, for example, information indicating the impact of the item score on the overall score, and is the correlation between a set of item scores of multiple organizations and a set of overall scores of the multiple organizations. Note that the process of acquiring the correlation, correlation value, and correlation information is a publicly known technique, and therefore a detailed description is omitted.

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

It is preferable that the impact acquisition unit 133 acquires, for each attribute value of the organization, the impact of the item score of each item on the overall score for each item. In other words, when the attribute value of the organization is an industry identifier, it is preferable that the impact acquisition unit 133 acquires the impact of each item for each industry according to the industry identifier.

The output unit 14 outputs various information. The various information is, for example, a collection of information pairs of item identifiers and item scores. The various information is, for example, a total score. The various information is, for example, a collection of information pairs of item identifiers and impact 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 that includes 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.

The item score output unit 141 outputs one or more item scores. The item score output unit 141 typically outputs an 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 that pairs with an organizational identifier included in the score 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 organizational identifier. It is also preferable that the item score output unit 141 outputs the item score in a manner that allows visual distinction between the item score of the overall item and the item score of the individual item.

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.

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.

It is also preferable that the impact output unit 143 outputs the impact level in association with each item so that the impact level classification can be visually distinguished.

The terminal storage unit 21 constituting the terminal device 2 stores various types of information. The various types of information are, for example, an organization identifier that identifies the user's organization. The various types of information are, for example, information received by the terminal receiving unit 25. The organization identifier may be considered as information that identifies the user.

The terminal reception unit 22 receives various instructions and information. Here, reception is a concept that includes the reception of information input from an input device such as a keyboard, mouse, or touch panel, the reception of information transmitted via a wired or wireless communication line, and the reception of information read from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory. Examples of various instructions and information include output instructions for scores, organizational response information, and questionnaire response information.

The input means for various instructions and information may be anything, such as a touch panel, keyboard, mouse, or menu screen. The terminal reception unit 22 may be realized by a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

The various processes performed by the device processing unit 23 include, for example, processing to configure the information received by the device receiving unit 25 into data to be displayed. The various processes include, for example, processing to configure the instructions received by the device accepting unit 22 into instructions to be transmitted.

The terminal transmission unit 24 transmits various instructions, information, etc. to the information processing device 1. Examples of the various instructions, information, etc. include instructions configured by the terminal processing unit 23, instructions and information accepted by the terminal acceptance unit 22, etc.

The terminal receiving unit 25 receives various information from the information processing device 1. The various information includes, for example, item scores, total scores, and impact levels.

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 constructed by the device processing unit 23. The various information is, for example, item scores, total scores, and impact levels.

The storage unit 11 is preferably a non-volatile recording medium, but can also be a volatile recording medium.

The process by which information is stored in the storage unit 11, the item information storage unit 111, the organizational response information storage unit 112, the individual score table storage unit 113, and the terminal storage unit 21 is not important. For example, information may be stored in the storage unit 11 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 11 etc., or information inputted via an input device may be stored in the storage unit 11 etc.

The reception unit 12 and the terminal receiving unit 25 are typically implemented using wireless or wired communication means, but may also be implemented using means for receiving broadcasts.

The processing unit 13, item score acquisition unit 131, total score acquisition unit 132, impact acquisition unit 133, and device processing unit 23 can usually be realized by an MPU, memory, etc. The processing procedures of the processing unit 13, etc. are usually realized by software, and the software is recorded in a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuitry).

The output unit 14, the item score output unit 141, the total score output unit 142, the impact output unit 143, and the terminal transmission unit 24 are typically realized by wireless or wired communication means, but may also be realized by broadcasting means.

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

Next, the operation of information system A will be described. First, an example of the operation of information processing device 1 will be described using the flowchart in FIG. 3. It is assumed that organizational response information of multiple organizations is stored in organizational response information storage unit 112. It is also assumed that individual score tables are stored in individual score table storage unit 113.

(Step S301) The processing unit 13 judges whether it is time to calculate the score, etc. If it is time to calculate the score, etc., the process proceeds to step S302, and if it is not time to calculate the score, etc., the process proceeds to step S312. The timing for calculating the score, etc., refers to, 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 the organizational response information storage unit 112, etc.

(Step S302) The processing unit 13 assigns 1 to counter i.

(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.

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

(Step S305) The processing unit 13 assigns 1 to counter j.

(Step S306) The processing unit 13 determines 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.

(Step S307) The processing unit 13 obtains organization response information that pairs with the jth organization identifier.

(Step S308) The processing unit 13 uses the organization response information acquired in step S307 to calculate various scores corresponding to the jth organization identifier. Note that calculating a score is considered to have the same meaning as obtaining a score. The score calculation process will be described with reference to the flowchart in FIG. 4.

(Step S309) The processing unit 13 increments the counter j by 1. Return to step S306.

(Step S310) The impact acquisition unit 133 calculates the impact of each item. The impact calculation process is described using the flowchart in FIG. 5.

(Step S311) The processing unit 13 increments the counter i by 1. Return to step S303.

(Step S312) The reception unit 12 determines whether or not an instruction to output scores, etc. has been received. If an instruction to output scores, etc. has been received, the process proceeds to step S313; if an instruction to output scores, etc. has not been received, the process returns to step S301.

(Step S313) The processing unit 13 acquires the organization identifier contained in the score etc. output instruction received in step S312.

(Step S314) The processing unit 13 acquires the attribute value of the organization that pairs with the organization identifier acquired in step S313.

(Step S315) The processing unit 13 acquires two or more item scores that are paired with the organization identifier acquired in step S313, and item identifiers that are paired with the item scores, from the storage unit 11 or a buffer (not shown). In other words, the processing unit 13 acquires pairs of item identifiers and item scores for the number of items.

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

(Step S317) The processing unit 13 acquires from the storage unit 11 or a buffer (not shown) two or more impact degrees that are paired with the attribute values acquired in step S314, and item identifiers that are paired with the impact degrees. In other words, the processing unit 13 acquires pairs of item identifiers and impact degrees for the number of items.

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

(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 sent 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.

In the flowchart of FIG. 3, the route by which organizational response information is stored in the organizational response information storage unit 112 does not matter.

In the flowchart in FIG. 3, the impact level is obtained for each attribute value of the organization. However, it goes without saying that the impact level of each item may be obtained using the organizational response information of all organizations, regardless of the attribute value of the organization.

Furthermore, in the flowchart of Figure 3, processing ends when the power is turned off or an interrupt occurs to end processing.

Next, an example of the score calculation process in step S308 will be described using the flowchart in FIG.

(Step S401) The item score acquisition unit 131 assigns 1 to counter i.

(Step S402) The item score acquisition unit 131 determines whether or not the item identifier of the i-th individual item is present in the organizational response information acquired in step S307. If the item identifier of the i-th individual item is present, the process proceeds to step S403; if not, the process proceeds to step S409.

(Step S403) The item score acquisition unit 131 acquires the satisfaction information of all members that is paired with the item identifier of the i-th individual item in the organizational response information acquired in step S307.

(Step S404) The item score acquisition unit 131 performs statistical processing on the satisfaction information acquired in step S403 to calculate statistical satisfaction information. Note that here, the item score acquisition unit 131 calculates statistical satisfaction information that is, for example, the average value of the satisfaction information acquired in step S403. Then, the item score acquisition unit 131 accumulates the calculated statistical satisfaction information in the storage unit 11 or a buffer (not shown) in pairs with the item identifier of the i-th individual item.

(Step S405) The item score acquisition unit 131 acquires the expectation information of all members that is paired with the item identifier of the i-th individual item in the organizational response information acquired in step S307.

(Step S406) The item score acquisition unit 131 performs statistical processing on the expectation information acquired in step S405 to calculate statistical expectation information. Note that here, the item score acquisition 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 acquisition unit 131 accumulates the calculated statistical expectation information in the storage unit 11 or a buffer (not shown) in pairs with the item identifier of the i-th individual item.

(Step S407) The item score acquisition unit 131 uses the statistical satisfaction information and 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 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. The item score acquisition unit 131 then accumulates the acquired item score in the storage unit 11 or a buffer (not shown) in pairs with the item identifier of the i-th individual item.

(Step S408) The item score acquisition unit 131 increments the counter i by 1. Return to step S402.

(Step S409) The item score acquisition unit 131 assigns 1 to counter j.

(Step S410) The item score acquisition unit 131 determines whether or not the item identifier of the jth comprehensive item is present in the organizational response information acquired in step S307. If the item identifier of the jth comprehensive item is present, the process proceeds to step S411; if not, the process proceeds to step S414.

(Step S411) The item score acquisition unit 131 acquires the satisfaction information of all members that is paired with the item identifier of the jth comprehensive item in the organizational response information acquired in step S307.

(Step S412) The item score acquisition unit 131 performs statistical processing on the satisfaction information acquired in step S411 to calculate statistical satisfaction information. Note that here, the item score acquisition unit 131 calculates statistical satisfaction information that is, for example, the average value of the satisfaction information acquired in step S411. Then, the item score acquisition unit 131 accumulates the calculated statistical satisfaction information in the storage unit 11 or a buffer not shown in the figure, paired with the item identifier of the j-th comprehensive item.

(Step S413) The item score acquisition unit 131 increments the counter j by 1. Return to step S410.

(Step S414) The total score acquisition unit 132 acquires all 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.

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

(Step S416) The overall score acquisition unit 132 acquires statistical satisfaction information for all items in the overall category from the storage unit 11 or a buffer not shown.

(Step S417) The overall score acquisition unit 132 statistically processes the statistical satisfaction information of all items acquired in step S416 and calculates statistical satisfaction information of the overall item. For example, the overall score acquisition 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.

(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, using the formula "provisional overall score = α × statistical satisfaction information of the overall items + β × overall score of the individual items".

(Step S419) The overall score acquisition unit 132 acquires correlation information regarding the correlation between the set of satisfaction information and the set of expectation information from the set of satisfaction information for all individual items and the set of expectation information for all individual items.

(Step S420) The overall score acquisition unit 132 adjusts the provisional overall score acquired in step S418 using the correlation information acquired in step S419 to acquire an overall score. The process returns to higher-level processing. Note that the overall score acquisition unit 132 acquires an overall score such that the greater the degree of correlation indicated by the correlation information, the higher the overall score.

Next, an example of the impact calculation process in step S310 will be described using the flowchart in FIG.

(Step S501) The impact acquisition unit 133 assigns 1 to counter i.

(Step S502) The impact acquisition unit 133 determines whether the item identifier of the i-th individual item exists in the two or more pieces of organizational response information 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 level process.

(Step S503) The impact acquisition unit 133 acquires item scores of multiple organizations that are paired with the item identifier of the i-th individual item from the two or more organizational response information pieces acquired in step S304.

(Step S504) The impact acquisition unit 133 acquires a total score for multiple organizations from the two or more organizational response information pieces acquired in step S304.

(Step S505) The impact acquisition unit 133 acquires an impact 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 acquisition unit 133 pairs the acquired impact with the item identifier of the i-th individual item and accumulates it in the storage unit 11 or a buffer (not shown).

(Step S506) The impact acquisition unit 133 increments the counter i by 1. Return to step S502.

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.

The specific operation of information system A in this embodiment will be described below. A conceptual diagram of information system A is shown in Figure 1.

Now, assume 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 that indicate 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: Expectations," and "Question: Satisfaction." "Question No." is an ID that identifies a question, and is an example of an item identifier. "Type" is information that indicates the type of item, and here 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 that indicates the contents of an item. It should be noted that "Item" may be considered an item identifier. "Question: Expectations" is a question for acquiring expectation information. "Question: Satisfaction" is a question for acquiring satisfaction information.

In addition, the organizational response information storage unit 112 stores, for example, the organizational response information shown in FIG. 7. In the organizational response information storage unit 112, two or more pieces of organizational response information are stored. FIG. 7 shows the organizational response information of an organization identified by the organizational identifier "Company A". Also, 701 is the member response information of an employee of the organization identified by the organizational identifier "Company A". The organizational response information of the organization identified by the organizational 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 comprehensive items and do not have expectation information. The expectation information and satisfaction information that make up the member response information of 701 are information obtained 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. In addition, 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. In addition, the attribute value of the industry of the organization identified by the organization identifier "Company A" is "manufacturer."

Furthermore, the individual score table storage unit 113 stores the 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, the average value of expectation information. "Expectation information" may be, for example, information indicating the 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. "Satisfaction information" is, for example, the average value of satisfaction information. "Satisfaction information" may be, for example, information indicating the 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.

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

Next, the processing unit 13 obtains two or more pieces of organizational response information that are paired with the attribute value "manufacturer" from the organizational response information management table (Figure 7).

Next, the processing unit 13 calculates the item scores for each item and the overall score for the organization identifiers that are paired with the attribute value "manufacturer", including "Company A", using the organization response information that is paired with the organization identifier. Below, the calculation of the item scores and the overall score will be explained using "Company A" as an example.

In other words, 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 in FIG. 8 to acquire item scores 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.

Next, the item score acquisition unit 131 acquires satisfaction information of all members corresponding to the item identifier of each comprehensive item. Then, the item score acquisition unit 131 calculates the average value of the acquired satisfaction information for each comprehensive item, and acquires the average value as statistical satisfaction information. Next, the item score acquisition unit 131 accumulates the statistical satisfaction information of each comprehensive item in a buffer, paired with the item identifier of each comprehensive item.

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.

Next, the overall score acquisition unit 132 statistically processes the statistical satisfaction information of all items and calculates 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.

Next, the overall score acquisition unit 132 calculates the provisional overall score using the formula "provisional overall score = 0.7 x statistical satisfaction information for overall items + 0.3 x overall score for individual items."

Next, the overall score acquisition unit 132 acquires correlation information regarding the correlation between the set of satisfaction information and the set of expectation information from the set of satisfaction information for all individual items and the set of expectation information for all individual items.

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 uses the adjusted provisional overall score of other companies to acquire a standard deviation. 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 organizational identifier "Company A" to be "68.0". The overall score acquisition unit 132 then stores the overall score "68.0" in a buffer in association with the organizational 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".

Then, the above process is 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 for each individual item and the overall score are calculated and stored at least temporarily in the buffer.

Next, the impact acquisition unit 133 acquires, for each individual item, an item score paired with the organization identifier paired with the attribute value "manufacturer" from the buffer. In other words, the impact acquisition unit 133 acquires item scores from the buffer equal to the number of organization identifiers. Next, the impact acquisition unit 133 acquires an overall score paired with the organization identifier paired with the attribute value "manufacturer" from the buffer.

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

The above process is also performed on organization response information corresponding to other attribute values (e.g., "trading company" or "bank").

Then, item scores and overall scores are accumulated for each company, and in this case, the impact of each item is accumulated for each organizational attribute value.

In this situation, it is assumed that a user of "Company A" inputs an instruction to output scores, etc., into the terminal device 2. Next, the terminal device 2 accepts the instruction to output scores, etc., and constructs an instruction to output scores, etc., having the organizational identifier "Company A". The terminal device 2 then transmits the instruction to output scores, etc., to the information processing device 1.

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 that pairs with the organization identifier "Company A".

Next, the processing unit 13 obtains two or more item scores that are paired with the organizational identifier "Company A" and item identifiers that are paired with the item scores from the storage unit 11 or a buffer not shown.

Next, the processing unit 13 retrieves the overall score "68.0" that is paired with the organization identifier "Company A" from the storage unit 11 or a buffer not shown.

Next, the processing unit 13 obtains two or more impact degrees that are paired with the obtained attribute value "manufacturer" and item identifiers that are paired with the impact degrees from the storage unit 11 or a buffer (not shown).

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 superior, the work, and the workplace. Also, 903 is the item score and the impact of the individual items. 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 impact of each individual item. Note that, as can be seen from 1004 to 1006 in FIG. 10, the impact output unit 143 outputs the impact in association with each item so that the classification of the impact can be visually distinguished. That is, here, for example, the darkest background color is used when the impact level is 0.8 or more, a normal background color is used when the impact level is 0.6 or more but less than 0.8, and a light background color is used when the impact level is less than 0.6, thereby outputting the impact level classification so that it is visually distinguishable. It goes without saying that the method of outputting the impact level classification so that it is visually distinguishable is not limited to background color, and various attribute values such as font and size can also be changed.

As described above, this embodiment allows organizations to easily understand the items that are important to them.

In addition, according to this embodiment, the degree of impact can be obtained for each attribute value of the organization, allowing important items to be identified with high accuracy.

Furthermore, according to this embodiment, an information processing device 1 can be provided that can acquire an engagement score and be used as a so-called engagement device.

In addition, according to the specific example of this embodiment, the impact degree is obtained for each attribute value of the organization. However, it is also possible to obtain the impact degree for each item regardless of the attribute value of the organization. The technology for obtaining the impact degree for each attribute value of the organization focuses on the fact that the characteristics of a company differ depending on the attribute value of the organization. Such a technology is extremely useful in engagement, for example, in which employees voluntarily try to exert their own capabilities toward achieving the organization's goals, and both the employees and the organization grow.

It goes without saying that the above-mentioned "satisfaction" item in the survey also includes similar terms such as satisfaction and current situation, and that the "expectations" also includes similar terms such as degree of expectation, importance, significance, etc.

The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to 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, that has an item identifier that identifies an item related to the organization and response information including satisfaction level information that is a response regarding the member's degree of satisfaction with the item, function as an item score acquisition unit that statistically processes the response information 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 item scores for each organization and for each item, a total score acquisition unit that uses the two or more item scores to acquire a total score that is a total score for each of the two or more organization response information, an influence acquisition unit that uses the item scores for each of the two or more items of each of the two or more organizations and the total score for each of the two or more organizations to acquire the influence of the item score of each item on the total score for each item, and an influence output unit that outputs the influence acquired by the influence acquisition unit in association with each item. It should be noted that such a program can be said to be a program that realizes an engagement system.

(Embodiment 2)
In this embodiment, an information system including a score prediction device that predicts a score using the correspondence and daily information for the period for which the score is predicted will be described. Note that the correspondence is a correspondence 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.

In addition, in this embodiment, we will explain an information system that includes a score prediction device that predicts scores using a machine learning algorithm.

In addition, in this embodiment, we will explain an information system that includes a score prediction device that predicts scores using a correspondence table obtained by data mining.

Furthermore, in this embodiment, an information system including a score prediction device that predicts scores using an arithmetic formula will be described.

FIG. 11 is a conceptual diagram of information system B in this embodiment. 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 overall score described above, but it may also be a score for one item, or a score that is a representative value (for example, average, median) of two or more item scores.

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, tablet terminal, or mobile phone, a so-called personal computer, etc., and the type is not important.

Figure 12 is a block diagram of information system B. Also, Figure 13 is a block diagram of score prediction device 3 that constitutes information system B.

The score prediction device 3 includes a storage unit 31, a reception unit 32, a processing unit 33, and an output unit 34.

The storage unit 31 includes an organizational information storage unit 311, a daily information storage unit 312, a teacher score storage unit 313, and a correspondence information storage unit 314.

The daily information storage unit 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.

The processing unit 33 includes a score acquisition unit 331. The score acquisition unit 331 includes a correspondence acquisition means 3311 and a score acquisition means 3312.

The output unit 34 includes a score output unit 341.

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.

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 below, daily information described below, teacher scores described below, correspondence relationship information described below, and information on two or more members. 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.

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. In addition, member information such as management, administrative positions, and superiors is also stored in the storage unit 31. Two or more pieces of member information may also be stored in a member information storage unit not shown.

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 defined. 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, ID, etc. The organization attribute value is an attribute value of the organization. The organization attribute value is, for example, 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"), etc.

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 by members in their daily lives. Daily information does not include answers to questions in questionnaires, etc. Daily information is, for example, a file, but may also be a database record. 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.

Note that one piece of information corresponds to another piece of information, which may mean that the one piece of information contains the other piece of information, that the one piece of information and the other piece of information are linked, or that the other piece of information contains the one piece of information. In other words, it is sufficient if the other piece of information can be obtained from the one piece of information, or the one piece of information can be obtained from the other piece of information.

Daily information is, for example, work information and health information. Work information is information about the work performed by members. Health information is information about the health of members.

Examples of business information include input information, attendance status information, and business result information. Input information is information entered by members. Input information is information entered by members into devices used in business (e.g., personal computers, tablet terminals, servers, etc.). Input information is 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.

Health information is information about the health of members. Health information is, for example, biometric information and lifestyle information. Biometric information is information about the body of a member. Biometric information is, for example, stress information and measurement information. Details of stress information and measurement information will be described later. Details of lifestyle information will also be described later.

The everyday information used to obtain the correspondence is called teacher everyday information. The everyday information used in the score prediction process is called predicted everyday information.

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

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

The business input information storage means 3121 constituting the daily information storage unit 312 stores one or more pieces of business input information. The business input information is information regarding input for the execution of a member's own business.

The business input information is, for example, the operation log of the device used by the member for business, the program created by the member, the document created by the member, and the daily report created by the member. The business input information is associated with time information. The business input information is associated with, for example, an organization identifier and a member identifier. The business input information may be, for example, information that can be obtained from the operation log, the program, the document, or the daily report. The information that can be obtained from the operation log is, for example, the operation log attribute value. The operation log attribute value is, for example, the operation amount indicating the amount of the operation log, the number of types obtained using the operation log, the average number of operation logs per day, and the change information of the number of operation logs. The change information of 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 from day to day (for example, variance). The information that can be obtained from the program is, for example, the program attribute value. The program attribute value is, for example, the number of steps of the program, the average number of steps of the program per day, and the change information of the number of steps of the 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 value of the 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 the amount of document data 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 value of the amount of daily report data per day, information on the change in the amount of data, 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 daily report data volume for one day is on an increasing trend, whether the daily report data volume for one day is on a decreasing trend, and information indicating the variation in the daily report data volume from day to day (e.g., variance). Note that the operation log attribute value, program attribute value, document attribute value, and daily report attribute value are included in the business input attribute value, which is the attribute value of business input information.

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.

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 when the communication information was input, sent, or received. The organization identifier here is the identifier of the organization to which the member who input the communication information belongs. The member identifier here is the identifier of the member who input 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.

The communication information is information relating to communication between two or more members. For example, the communication information is chat information entered by members into a chat system (or a chat app), SNS information entered by members into an SNS, emails created by members, and text information obtained by converting speech uttered by members during video conferences into text. Note that the technology for converting speech into text using speech recognition processing is well known, and so a description of this technology will be omitted.

The communication information may include communication attribute values that can be obtained from chat information, SNS information, email, or text information. The communication attribute values are the amount of data in the communication information for a specified period, the number of negative words appearing in the communication information for a specified period, the rate of appearance of negative words in the communication information for a specified period, the number of positive words appearing in the communication information for a specified period, the rate of appearance of positive words in the communication information for a specified period, and the response rate of members to messages from management, supervisors, or superiors. Note that the specified period is a period that has been determined in advance.

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.

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 that clearly indicates annual leave days. Attendance information includes, for example, information that clearly indicates holiday work. Attendance information includes, for example, information of normal working hours. Attendance information includes, for example, information that identifies normal work days.

The attendance status information is, for example, the number of annual holidays a member has, the number of overtime hours a member has worked, and the number of days a member has worked on holidays. The attendance status information is associated with, for example, an organization identifier and a member identifier.

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.

The business result information storage means 3124 stores one or more business result information. Business result information is information related to 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.

The business result information in the business 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).

The stress information storage means 3125 stores one or more pieces of stress information. The stress information is information related to the stress of a member. The stress information is a stress level indicating the degree of stress, and information indicating the presence or absence of stress. The stress information is, for example, the results of a stress check. The stress information is, for example, a response to a questionnaire for the member (for example, "Please select your stress level from 1 to 5"). The 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. The stress information is associated with, for example, an organization identifier and a member identifier.

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.

The measurement information storage means 3126 stores one or more pieces of measurement information. The measurement information is the result of measuring the member's 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.

The measurement information of 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.

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.

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.

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

The audio in the audio storage means 3128 is, for example, audio received from the terminal device 4 or a server not shown, or audio acquired by the score prediction device 3.

The lifestyle information storage means 3129 stores one or more pieces of lifestyle information. The lifestyle information is information about the member's lifestyle. For example, the lifestyle information is the amount of sleep time, whether or not alcohol was consumed, and the number of cigarettes smoked. For example, the lifestyle 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 lifestyle information is associated with, for example, an organization identifier and a member identifier. Furthermore, the lifestyle information is associated with, for example, time information (for example, date).

The teacher score storage unit 313 stores one or more teacher scores. The teacher score is usually an organization score obtained using multiple organizational 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 entered by the user or a predicted score obtained by the score prediction device 3 in the past. The questions are, for example, questionnaire questions or questionnaire questions. The teacher score is, for example, a total score obtained by the information processing device 1 described in embodiment 1. The teacher score may be, for example, any item score obtained by the information processing device 1 described in embodiment 1. In addition, the teacher score may be a representative value (for example, an average value, weighted average, 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 embodiment 1. In other words, the teacher score may be, for example, an organization score obtained using multiple organizational 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 about 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.

The correspondence information storage unit 314 stores one or more pieces of correspondence information. The correspondence information corresponds to, for example, an organization identifier. The correspondence information also corresponds to, for example, time information or period information.

The correspondence information is information regarding 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, or information obtained using one or more pieces of teacher routine information, or information obtained using one or more pieces of teacher routine information and one or more pieces of teacher routine information.

The teacher feature is information that includes one or more types of information, such as data volume information that identifies the data volume of the teacher input information, input frequency information that identifies the input frequency of the teacher input information, analysis information that indicates the analysis results of the teacher input information, and timing information that identifies the timing of input of the teacher input information. Note that the analysis information is, for example, a task input attribute value and a communication attribute value.

The correspondence information is, for example, (1) a learning model, (2) a correspondence table, and (3) an arithmetic formula. The correspondence information is acquired, for example, by the correspondence acquisition means 3311 described later.

A learning model is data acquired by a machine learning learning process. A learning model may also be called a prediction model, a classification model, a learner, or a predictor. In machine learning, for example, algorithms such as deep learning, decision trees, random forests, SVR, and SVM can be used. In machine learning, multiple regression analysis, neural networks, and the like can be used. In other words, any machine learning algorithm can be used. A 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. A teacher vector is a vector whose elements are two or more teacher features. In addition, a vector is a collection of two or more pieces of information, and its structure does not matter.

The correspondence table is a table that contains two or more pieces of correspondence information, which are pairs of teacher vectors and teacher scores. The structure of the table is not important.

The formula is an arithmetic formula (S = f(a, b, c, ...)) that 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).

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 specifies 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 specifies the correspondence information to be used. The prediction instruction has, for example, teacher period information that specifies the period of the daily information that is the source of the teacher data.

In addition, here, acceptance usually means receiving information from the terminal device 4, but it may also be understood as a concept that includes the acceptance of information input from an input device such as a keyboard, mouse, or touch panel, and the acceptance of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

The processing unit 33 performs various types of processing. For example, the various types of processing are processing performed by the score acquisition unit 331, the correspondence acquisition means 3311, and the score acquisition means 3312.

The processing unit 33, for example, stores the daily information received by the reception unit 32 in the daily information storage unit 312. The processing unit 33, for example, stores the daily information received by the reception unit 32 in the daily information storage unit 312 by associating it 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 input or transmitted the daily information, or the member identifier associated with the daily information.

The score acquisition unit 331 acquires a predicted score using the correspondence between the teacher score and one or more pieces of teacher daily routine information, and one or more pieces of predicted daily routine information. The predicted score is a score that corresponds to one or more pieces of predicted daily routine information.

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

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 feature is, for example, daily information, or attribute values of daily information that can be acquired from daily information (for example, operation log attribute values, program attribute values, document attribute values, daily report attribute values, etc.). In addition, a teacher vector is composed of two or more teacher features. A predicted vector is composed of 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 of two or more features to acquire correspondence information and to acquire a predicted score.

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

The score acquisition unit 331, for example, for each member, acquires from the business input information storage means 3121 a program that is paired with each member identifier and corresponds to the time information for each day of 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.

The score acquisition unit 331 acquires, for example, the number of steps in one or more program files for each date information corresponding to the program file (for each day it was created). Then, the score acquisition unit 331 acquires, for example, the average number of steps in the program file for each day, information indicating the variance in the number of steps created for each day in the target period, and change information indicating whether the number of steps in the program created each day is on an increasing or decreasing trend.

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

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

The score acquisition unit 331, for example, for each member, 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.

The score acquisition unit 331, for example, acquires the amount of data for each of the one or more acquired daily reports. The score acquisition unit 331 then acquires, for example, the average value of the amount of data, information indicating the variation in the amount of data, and change information indicating whether the amount of data is increasing or decreasing. The score acquisition unit 331 also performs morphological analysis on the one or more acquired daily reports, for example, and acquires the number of occurrences of negative words by referring to a dictionary of negative words. The score acquisition unit 331 also acquires, for example, the number of independent words in the one or more acquired daily reports, and acquires the occurrence rate of negative words, which is the number of negative words in the number of independent words, and the occurrence rate of positive words, which is the number of positive words in the number of independent words.

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 for 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 of 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 of 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 of the communication information, and acquires the proportion of the number of negative words and the proportion of the number of positive words among the number of acquired independent words.

The score acquisition unit 331, for example, for each member, obtains the sent e-mails that correspond to the time information of each day of the period to be processed, obtains the e-mails that cite the e-mails, and determines whether the identifier (e.g., e-mail address) of the sender of the e-mail that cites the e-mail is stored in the organizational information storage unit 311 in association with the member identifier. If it is stored, the score acquisition unit 331 increments the counter for replies to messages from the reply target (management, manager, superior, etc.). In addition, the score acquisition unit 331 obtains the number of e-mails from the reply target among the received e-mails that correspond to the time information of each day of the period to be processed, paired with each member identifier for each member. 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 e-mail address of each member identifier from the collection of member information, and obtains the number of e-mails from which the e-mail address is the sender (the number of e-mails from the reply target). The score acquisition unit 331 then calculates, for each member, the response rate of the member to messages 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 for each member in association with each member identifier.

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

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

The score acquisition unit 331, for example, reads from the storage unit 31 one or more answers to a questionnaire that is paired with each member identifier for each member and corresponds to the time information of the period to be processed. Next, the score acquisition unit 331, for example, acquires stress information from one or more answers for each member. The content of the questionnaire and the algorithm for acquiring the stress information are not important. The score acquisition unit 331, for example, acquires answers to questions inquiring about the 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 the 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.

The score acquisition unit 331, for example, for each member, acquires from the measurement information storage means 3126 the heart rate and/or blood pressure that are paired with each member identifier and correspond to the time information of the period to be processed. Next, the score acquisition unit 331 acquires stress information for each member, for example, in which the higher the heart rate and/or blood pressure value, the higher the stress. The score acquisition unit 331, for example, acquires the number of times that the heart rate and/or blood pressure value is greater than a threshold value, and acquires a stress level based on the number of times. Note that the stress level based on the number of times increases as the number of times increases. The score acquisition unit 331 also acquires, for example, consecutive times during which the heart rate and/or blood pressure value is greater than a threshold value, and acquires a stress level based on the sum of the consecutive times. Note that the stress level based on the sum of the consecutive times increases as the sum of the consecutive times increases.

The score acquisition 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 the time information of the period to be processed from the measurement information storage means 3126. The score acquisition unit 331, for example, acquires a representative value of the measurement information (e.g., average value, median value) for each type of measurement information.

The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires from the image storage means 3127 one or more images for the period to be processed. 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, or normal, etc.). The score acquisition unit 331 also performs statistical processing on the one or more acquired 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. Also, the processing of performing facial expression analysis and acquiring facial expression identifiers is a publicly known technology.

The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires one or more sounds for the 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 intervals, silent intervals, etc.) from each of the one or more sounds, and acquires the characteristics of the sound using the features. The score acquisition unit 331, for example, uses the features to acquire the proportion of speaking time in the attended conference, and an emotion identifier (laughing, neutral, angry, crying, etc.). Note that the technology for acquiring the features is a well-known technology, for example, LPC analysis, cepstrum analysis, etc.

For example, the score acquisition unit 331 pairs each member identifier with each member identifier for each member and reads out lifestyle information for the period to be processed from the lifestyle information storage means 3129. Then, the score acquisition unit 331 performs statistical processing of the lifestyle information for each member, for example, to acquire the characteristics of the lifestyle information. The characteristics of the lifestyle information are, for example, the average sleep time, the variance of the sleep time, the number of days on which alcohol was consumed, the percentage of days on which alcohol was consumed, and the average number of cigarettes smoked per day.

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

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 predictive features as elements, to a machine learning module, executes the module, performs prediction processing, and acquires a prediction score. Note that the predictive features are predicted everyday information or information that can be acquired from the predicted everyday information. Also, the predictive vector is information acquired by the score acquisition unit 331 through the above-mentioned processing. Also, the machine learning module may be, for example, Tiny_SVM, a TensorFlow function, a MicrosoftML function, or the like.

The score acquisition unit 331 acquires a prediction score, for example, by using a correspondence table, which is correspondence information, and a prediction 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 closest to a prediction vector having two or more predictive features as elements, and acquires the teacher score paired with the teacher vector as the prediction score.

The score acquisition unit 331 also 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 score corresponding to each of the two or more teacher vectors. The representative value is, for example, the average, median, or 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, that the distance is within or less than a threshold value, that the distance is within the top N (N is a natural number equal to or greater than 2), etc.

The score acquisition unit 331 also substitutes, for example, two or more predictive features into an arithmetic formula, executes the arithmetic formula, and calculates a predicted score. Note that the arithmetic formula is stored in the correspondence information storage unit 314.

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

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

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

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

The score acquisition unit 331 acquires, for example, one or more types of information from data amount information that identifies the data amount of teacher input information, input frequency information that identifies the input frequency of teacher input information, analysis information that indicates the analysis result of teacher input information, and timing information that identifies the timing of input of teacher input information, and acquires one or more types of information from 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 that identifies 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.

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

The score acquisition unit 331 acquires a predicted score, for example, using a correspondence 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.

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

The score acquisition unit 331 acquires a predicted score, for example, using a correspondence between one or more pieces of teacher daily information, including teacher biometric information, and a teacher score, and one or more pieces of predicted daily information, including predicted biometric information.

The score acquisition unit 331 acquires a predicted score, for example, 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.

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

The score acquisition unit 331 acquires a predicted score, for example, using a correspondence 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.

The score acquisition unit 331 acquires a predicted score, for example, using a correspondence 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.

The score acquisition unit 331 may, for example, read from the storage unit 31 a past score corresponding to the acquired predicted score, and use the past score and the acquired predicted score to acquire score fluctuation information regarding an increase or decrease in the 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".

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.

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 size identifier such as "large company" or "small and medium-sized enterprise."

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

The correspondence acquisition means 3311 performs a learning process using a machine learning algorithm, for example, using one or more pairs of one or more teacher daily information and teacher scores, and acquires correspondence information, which is a learning model.

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. Each of the two or more sets of teacher daily information is, for example, a set of teacher daily information from different organizations. Each of the two or more sets of teacher daily information is, for example, a set of teacher daily information from different periods.

Next, for each set of teacher daily information, the correspondence acquisition means 3311, for example, uses one or more pieces of acquired teacher daily information to acquire two or more teacher characteristics, and acquires a teacher vector having each of the two or more teacher characteristics as elements. In addition, the correspondence acquisition means 3311, for example, acquires 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.

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 can be said to be an explanatory variable, and the teacher score can be said to be an objective variable. Also, the teacher feature is teacher daily information or information acquired from the teacher daily information.

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 at least temporarily in the correspondence information storage unit 314.

This learning model is a learning model that inputs predicted everyday information and/or a prediction vector, which is information acquired from the predicted everyday information, and outputs a prediction score. In addition, the algorithm for the machine learning learning process can be deep learning, decision tree, random forest, SVR, SVM, or other algorithms. In other words, the algorithm for the machine learning learning process is not important. In addition, the module for performing 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

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 stores the correspondence table in the correspondence relationship information storage unit 314, at least temporarily.
(3) When obtaining an arithmetic expression

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 formula that takes two or more teacher vectors as input and outputs a teacher score corresponding to each teacher vector.

The correspondence acquisition means 3311 reads, for example, an equation element that is the basis of the equation (for example, "score S = _ax1 + _bx2 + _cx3 + ... + (n-1) _xn + n (a, b, c ... n are parameters, _x1 , _x2 , _x3 , ... _xn are variables)") from the storage unit 31. Next, the correspondence acquisition means 3311 substitutes, for example, two or more pieces of teacher data into the variables of the equation element, and acquires parameters from which each teacher score can be obtained by multiple regression analysis, multivariate analysis, or the like. Then, the correspondence acquisition means 3311 stores the acquired equation in the correspondence information storage unit 314, at least temporarily.

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

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 prediction score using the correspondence information and one or more pieces of predicted daily information.

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

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

The score acquisition means 3312 performs prediction processing using a machine learning algorithm, for example, using a learning model and one or more pieces of predicted daily information, and acquires a predicted score.

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, as described above, the module that performs the machine learning prediction processing is not important.
(2) When using the correspondence table

The score acquisition means 3312 acquires a prediction vector, which is a vector having two or more predictive features, 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.

More specifically, the score acquisition means 3312 acquires a predicted vector, for example, using one or more pieces of predicted daily information. Next, the score acquisition means 3312, for example, searches a correspondence table for a vector that is closest to the predicted vector, and acquires the teacher score that pairs with the vector as a predicted score.

Also, the score acquisition means 3312 may determine, for example, 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. Also, the predetermined condition may be, for example, that the distance is within or smaller than a threshold value, and that the shortest distance is in the top N (N is 2 or more).
(3) When using an arithmetic expression

The score acquisition means 3312, for example, acquires a predicted vector using 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.

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

In this case, output usually means transmission to the terminal device 4. However, output here can also be considered as a concept that includes display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

The score output unit 341 may pass the predicted score acquired by the score acquisition unit 331 to the score acquisition unit 331, cause the score acquisition unit 331 to acquire score fluctuation information using the predicted score, and output the score fluctuation information.

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 user's organization. The various types of information are, for example, information received by the terminal receiving unit 45 (for example, a predicted score). The organization identifier may be considered as information that identifies the user.

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

The input means for various instructions and information can be anything, such as a touch panel, keyboard, mouse, or menu screen. The terminal reception unit 42 can be realized by a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

The various processes performed by the device processing unit 43 include, for example, processing to configure the information received by the device receiving unit 45 into data to be displayed. The various processes include, for example, processing to configure the instructions received by the device accepting unit 42 into instructions to be transmitted.

The device transmission unit 44 transmits various instructions, information, etc. to the score prediction device 3. Examples of the various instructions, information, etc. include instructions configured by the device processing unit 43, instructions and information accepted by the device acceptance unit 42, etc.

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

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 constructed by the device processing unit 43. The various information is, for example, a predicted score.

Here, output is a concept that includes displaying on a display, projecting using a projector, printing on a printer, outputting sound, sending to an external device, storing on a recording medium, and passing on the processing results to other processing devices or other programs, etc.

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 relationship information storage unit 314, and terminal storage unit 41 are preferably non-volatile recording media, but can also be realized with volatile recording media.

The process by which information is stored in the storage unit 31, etc. is not important. 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.

The reception unit 32 and the terminal receiving unit 45 are typically implemented using wireless or wired communication means, but may also be implemented using means for receiving broadcasts.

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

The output unit 34, score output unit 341, and terminal transmission unit 44 are typically implemented using wireless or wired communication means, but may also be implemented using broadcasting means.

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

Next, an example of the operation of the score prediction device 3 will be described using the flowchart in FIG. 14.

(Step S1401) The reception unit 32 determines whether or not daily information has been received from the terminal device 4 in association with the member identifier. If daily information has been received, the process proceeds to step S1402; 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.

(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 also 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.

(Step S1403) The reception unit 32 determines 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; if a prediction instruction has not been received, the process returns to step S1401.

(Step S1404) The correspondence acquisition means 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.

(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.

(Step S1406) The score output unit 341 transmits the predicted score obtained in step S1405 to the terminal device 4 that transmitted the prediction instruction. Return to step S1401.

In the flowchart of FIG. 14, the correspondence acquisition process in step S1404 may be performed in advance 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.

In addition, in the flowchart in Figure 14, processing ends when the power is turned off or an interrupt occurs to end processing.

Next, an example of the correspondence acquisition process in step S1404 will be described using the flowchart in FIG.

(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 the period of teacher daily information used to acquire the correspondence relationship information.

(Step S1502) The correspondence acquisition means 3311 acquires the teacher target conditions. The correspondence acquisition means 3311 acquires, for example, the teacher target conditions 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 conditions are conditions for the target organization used when predicting the score. The teacher target conditions are, for example, the organization identifier of the target organization for which a predicted score is to be obtained. The teacher target conditions are, for example, the organization identifier of all organizations. The teacher target conditions are, for example, conditions for the organization attribute values for which a predicted score is to be obtained (for example, industry identifier, size identifier, etc.).

(Step S1503) The correspondence acquisition means 3311 assigns 1 to counter i.

(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 it does not exist, the process proceeds to step S1510. Note that the teacher routine set is a collection of one or more pieces of teacher routine information.

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

(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.

(Step S1507) The correspondence acquisition means 3311 acquires the 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 in correspondence with each other. For example, the teacher routine information corresponding to the time information of the period specified by the period information corresponding to the teacher score is the teacher routine information corresponding to the teacher score.

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

(Step S1509) The correspondence acquisition means 3311 increments the counter i by 1. Return to step S1504.

(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 formula.

(Step S1511) The correspondence acquisition means 3311 stores the correspondence information acquired in step S1510 in the correspondence information storage unit 314, at least temporarily. It then returns to the upper-level processing.

Next, an example of the feature group acquisition process in step S1506 will be described with reference to the flowchart in FIG. 16. Note that 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, for example, in association with correspondence information. In other words, the everyday information used when the correspondence information is acquired and the everyday information used to acquire the predicted score are usually the same.

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

(Step S1602) The score acquisition unit 331 performs business input information processing. An example of business input information processing will be described with reference to the flowchart in FIG. 17. Note that business input information processing is processing that uses business input information to acquire one or more features related to the business input information.

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

(Step S1604) The score acquisition 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 acquiring one or more features related to the communication information using the communication information.

(Step S1605) The score acquisition unit 331 determines whether or not to use attendance status information to acquire the 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.

(Step S1606) The score acquisition unit 331 performs attendance status information processing. An example of attendance status information processing will be described with reference to the flowchart in FIG. 19. Attendance status information processing is processing that uses attendance status information to acquire one or more features related to the attendance status information.

(Step S1607) The score acquisition unit 331 determines whether or not to use the task result information to acquire the predicted score. If the task result information is not 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.

(Step S1608) The score acquisition unit 331 reads out the business result information to be used from the business result information storage means 3124, which is business result information that corresponds to the organization identifier of the target organization and corresponds to the period information of the target period.

(Step S1609) The score acquisition unit 331 determines whether or not to use stress information to acquire the predicted score. If stress information is to be used, the process proceeds to step S1610; if stress information is not to be used, the process proceeds to step S1611.

(Step S1610) The score acquisition unit 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 a process of acquiring one or more features related to the stress information.

(Step S1611) The score acquisition unit 331 determines whether or not to use the measurement information. If the measurement information is to be used, the process proceeds to step S1611. If the measurement information is not to be used, the process proceeds to step S1612.

(Step S1612) The score acquisition 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 a process of using the measurement information to acquire one or more features related to the measurement information.

(Step S1613) The score acquisition unit 331 determines 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; if an image is not to be used, the process proceeds to step S1615.

(Step S1614) The score acquisition unit 331 performs image information processing. An example of the image information processing will be described with reference to the flowchart in FIG. 22. Image information processing is a process of using an image to acquire one or more features related to the image.

(Step S1615) The score acquisition unit 331 determines whether or not to use audio to acquire the predicted score. If audio is to be used, the process proceeds to step S1616; if audio is not to be used, the process proceeds to step S1617.

(Step S1616) The score acquisition 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 a process of using voice to acquire one or more features related to the voice.

(Step S1617) The score acquisition unit 331 determines whether or not to use lifestyle information to acquire the predicted score. If lifestyle information is not to be used, the process proceeds to step S1618; if lifestyle information is not to be used, the process returns to the upper level process.

(Step S1618) The score acquisition unit 331 performs lifestyle information processing. It then returns to the upper level processing. An example of lifestyle information processing is described with reference to the flowchart in FIG. 24. Lifestyle information processing is processing that uses lifestyle information to acquire one or more features related to the lifestyle information.

Next, an example of the business input information processing in step S1602 will be explained using the flowchart in FIG.

(Step S1701) The score acquisition unit 331 assigns 1 to counter i.

(Step S1702) The score acquisition unit 331 determines whether 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.

(Step S1703) The score acquisition unit 331 acquires the daily report for the target period that is paired with the i-th member identifier from the business input information storage means 3121.

(Step S1704) The score acquisition 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 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.

(Step S1705) The score acquisition unit 331 acquires documents for the target period that are paired with the i-th member identifier from the business input information storage means 3121.

(Step S1706) The score acquisition unit 331 acquires one or more document attribute values from the acquired one or more documents. The document attribute values are, for example, the average data volume of the documents for each day they were created, and information on changes in the data volume.

(Step S1707) The score acquisition unit 331 acquires a program for the target period that is paired with the i-th member identifier from the business input information storage means 3121. The program is created by a software developer.

(Step S1708) The score acquisition 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.

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

(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, the average number of operation logs created on each day, and information on changes in the number of operation logs.

(Step S1711) The score acquisition unit 331 increments the counter i by 1. Return to step S1702.

(Step S1712) The score acquisition 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 acquisition 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.

(Step S1713) The score acquisition 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 acquisition unit 331 acquires, for example, a representative value of the document data volume for each day of all members, a representative value of information showing 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 an increasing trend, and the percentage of all members whose document data volume created per day is on a decreasing trend.

(Step S1714) The score acquisition 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 acquisition unit 331 acquires, for example, a representative value of the number of program steps per day for all members, a representative value of information showing the variance in the number of steps per day for all members in the target period, the percentage of all members for whom the amount of data on the number of program steps created per day is on an increasing trend, and the percentage of all members for whom the number of program steps created per day is on a decreasing trend.

(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 per day for all members, a representative value of information indicating the variance in the number of operation logs for each day in the target period for all members, the percentage of all members for whom the number of daily operation logs is on an increasing trend, and the percentage of all members for whom the number of daily operation logs is on a decreasing trend. Return to higher-level processing.

Next, an example of the communication information processing in step S1604 will be described using the flowchart in FIG.

(Step S1801) The score acquisition unit 331 assigns 1 to counter i.

(Step S1802) The score acquisition unit 331 determines whether 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.

(Step S1803) The score acquisition unit 331 acquires chat information for the target period that is paired with the i-th member identifier from the communication information storage means 3122.

(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 volume of the chat information, the number of negative words appearing in the chat information, the appearance rate of negative words in the chat information, the number of positive words appearing in the chat information, and the appearance rate of positive words in the chat information.

(Step S1805) The score acquisition unit 331 acquires SNS information for the target period that is paired with the i-th member identifier from the communication information storage means 3122.

(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 in the SNS information created by the member, the number of negative words that appear in the SNS information created by the member, the appearance rate of negative words in the SNS information created by the member, the number of positive words that appear in the SNS information created by the member, and the appearance rate of positive words in the SNS information created by the member.

(Step S1807) The score acquisition unit 331 acquires emails for the target period that are paired with the i-th member identifier from the communication information storage means 3122.

(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, the response rate, the number of emails sent by the member, the data volume of the emails sent by the member, the number of negative words appearing in the emails sent by the member, the appearance rate of negative words in the emails sent by the member, the number of positive words appearing in the emails sent by the member, and the appearance rate of positive words in the emails sent by the member.

(Step S1809) The score acquisition unit 331 acquires speech text information for the target period that is paired with the i-th member identifier from the communication information storage means 3122.

(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 volume of the speech text information, the number of negative words appearing in the member's speech text information, the appearance rate of negative words in the member's speech text information, the number of positive words appearing in the member's speech text information, and the appearance rate of positive words in the member's speech text information.

(Step S1811) The score acquisition unit 331 increments the counter i by 1. Return to step S1802.

(Step S1812) The score acquisition 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 acquisition unit 331 calculates, for example, a representative value of the data volume of chat information for all members, a representative value of the number of occurrences of negative words for 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.

(Step S1813) The score acquisition 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 acquisition unit 331 acquires, for example, a representative value of the amount of data of SNS information for each day of all members, a representative value of information showing the variation in the amount of data of SNS information for each day in the target period of all members, the percentage of all members whose amount of data of SNS information created per day is on an increasing trend, the percentage of all members whose amount of data of SNS information created per day is on a decreasing trend, a representative value of the number of occurrences of negative words in the SNS information, a representative value of the occurrence rate of negative words in the SNS information, a representative value of the number of occurrences of positive words in the SNS information, and a representative value of the occurrence rate of positive words in the SNS information.

(Step S1814) The score acquisition 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 acquisition 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.

(Step S1815) The score acquisition 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 process returns to the upper level. Note that the score acquisition 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.

Next, an example of the attendance status information processing in step S1606 will be explained using the flowchart in FIG.

(Step S1901) The score acquisition unit 331 assigns 1 to counter i.

(Step S1902) The score acquisition unit 331 determines whether 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.

(Step S1903) The score acquisition unit 331 acquires attendance information for the target period that is paired with the i-th member identifier from the attendance status information storage means 3123.

(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 that the member worked.

(Step S1905) The score acquisition unit 331 increments the counter i by 1. Return to step S1902.

(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 then returns to higher-level processing. The representative value may be, for example, the average, weighted average, median, or total. The score acquisition unit 331 acquires, for example, a representative value for the number of annual paid leave days for all members, a representative value for the number of overtime hours worked by members, and a representative value for the number of days off worked by members.

Next, an example of stress information processing in step S1610 will be described using the flowchart in FIG. 20.

(Step S2001) The score acquisition unit 331 assigns 1 to counter i.

(Step S2002) The score acquisition unit 331 determines whether 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.

(Step S2003) The score acquisition unit 331 acquires one or more answers for the 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 (e.g., stress level).

(Step S2004) The 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, a stress level obtained by substituting one or more answers (e.g., numerical values) into an arithmetic formula and performing an arithmetic operation, or stress information paired with a vector that is closest to a vector having two or more answers as elements. Note that such an arithmetic formula or a table having two or more pairs of such vectors and stress information is stored in the storage unit 31.

(Step S2005) The score acquisition unit 331 acquires one or more pieces of biometric 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 answers are responses to a questionnaire for acquiring stress information (e.g., stress level).

(Step S2006) The score acquisition unit 331 acquires stress information using one or more pieces of biometric information acquired in step S2003.

(Step S2007) The score acquisition unit 331 increments the counter i by 1. Return to step S2002.

(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 may be, for example, the average value, weighted average value, median value, or total value.

(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 target members acquired in step S2006. The process returns to the higher-level process. The representative value may be, for example, the average value, weighted average value, median value, or total value.

Next, an example of the measurement information processing in step S1612 will be described using the flowchart in FIG. 21.

(Step S2101) The score acquisition unit 331 assigns 1 to counter i.

(Step S2102) The score acquisition unit 331 determines whether 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.

(Step S2103) The score acquisition unit 331 acquires one or more heart rates for the target period that are paired with the i-th member identifier from the measurement information storage means 3126.

(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 variance of the heart rate (e.g., variance), and the maximum heart rate.

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

(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 (e.g., 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.

(Step S2107) The score acquisition unit 331 increments the counter i by 1. Return to step S2102.

(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 target members acquired in step S2104. The representative value may be, for example, the average value, weighted average value, median value, or total value.

(Step S2109) The score acquisition unit 331 uses one or more blood pressure attribute values of all target members acquired in step S2106 to acquire a representative value of each blood pressure attribute value for each type of blood pressure attribute value. It returns to the upper level process. The representative value may be, for example, the average value, weighted average value, median value, or total value.

Next, an example of the image information processing in step S1614 will be described using the flowchart in FIG. 22.

(Step S2201) The score acquisition unit 331 assigns 1 to counter i.

(Step S2202) The score acquisition unit 331 determines whether 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.

(Step S2203) The score acquisition unit 331 acquires one or more images from the target period that are paired with the i-th member identifier from the image storage means 3127.

(Step S2204) The score acquisition unit 331 assigns 1 to counter j.

(Step S2205) The score acquisition unit 331 determines whether or not the jth processing unit exists in the acquired image. If the jth field exists, the process proceeds to step S2206, and if the jth field does not exist, the process proceeds to step S2208. Note that the processing unit is, for example, a field or a file containing one or more fields.

(Step S2206) The score acquisition unit 331 acquires one or more image attribute values of the image of the jth processing unit.

(Step S2207) The score acquisition unit 331 increments the counter j by 1. Return to step S2105.

(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 (e.g., an identifier of a negative facial expression) and the proportion of negative facial expressions. Also, the identifier of a negative facial expression is, for example, "anger" or "sadness".

(Step S2209) The score acquisition unit 331 increments the counter i by 1. Return to step S2102.

(Step S2210) The score acquisition unit 331 uses one or more image attribute values of all target members acquired in step S2108 to acquire a representative value for each image attribute value type. The process returns to the higher-level process. The representative value may be, for example, the average value, weighted average value, median value, or total value.

Next, an example of the audio information processing in step S1616 will be described using the flowchart in FIG. 23.

(Step S2301) The score acquisition unit 331 assigns 1 to counter i.

(Step S2302) The score acquisition unit 331 determines whether 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.

(Step S2303) The score acquisition unit 331 acquires one or more pieces of audio for the target period that are paired with the i-th member identifier from the audio storage means 3128.

(Step S2304) The score acquisition unit 331 assigns 1 to counter j.

(Step S2305) The score acquisition unit 331 determines whether or not the jth processing unit exists in the acquired audio. If the jth field exists, the process proceeds to step S2306, and if the jth field does not exist, the process proceeds to step S2308. Note that the processing unit is, for example, audio information of a unit time, or a file.

(Step S2306) The score acquisition unit 331 acquires one or more types of audio attribute values for the audio of the jth processing unit.

(Step S2307) The score acquisition unit 331 increments the counter j by 1. Return to step S2105.

(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 voice 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 proportion of the speaking time in the attended conference, and an emotion identifier.

(Step S2309) The score acquisition unit 331 increments the counter i by 1. Return to step S2102.

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

Next, an example of lifestyle information processing in step S1618 will be explained using the flowchart in FIG. 24.

(Step S2401) The score acquisition unit 331 assigns 1 to counter i.

(Step S2402) The score acquisition unit 331 determines whether 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.

(Step S2403) The score acquisition unit 331 acquires the sleep time paired with the i-th member identifier and the sleep time for each day of the target period from the lifestyle information storage means 3129.

(Step S2404) The score acquisition unit 331 acquires one or more sleep attribute values using one or more sleep times acquired in step S2403. The sleep attribute values are, for example, the average sleep time per day, information indicating the variation in sleep time (e.g., variance), the number of days on which the sleep time is below a threshold, and the percentage of days on which the sleep time is below a threshold.

(Step S2405) The score acquisition unit 331 acquires the drinking dates for the target period that are paired with the i-th member identifier from the lifestyle information storage means 3129.

(Step S2406) The score acquisition unit 331 acquires one or more drinking attribute values using 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.

(Step S2407) The score acquisition unit 331 acquires 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, from the lifestyle information storage means 3129.

(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.

(Step S2409) The score acquisition unit 331 increments the counter i by 1. Return to step S2402.

(Step S2410) The score acquisition 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 indicating the variation in sleep time, the average number of days on which the sleep time is below a threshold, or the average percentage of days on which the sleep time is below a threshold. The score acquisition unit 331 may acquire, for example, the number or percentage of members whose sleep time is so short that it satisfies a predetermined condition.

(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 with a large number of drinking days that meets a predetermined condition.

(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 higher-level processing. 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.

Next, an example of the score acquisition process in step S1405 will be described using the flowchart in FIG. 25.

(Step S2501) The score acquisition means 3312 acquires inter-prediction information corresponding to the prediction instruction.

(Step S2502) The score acquisition means 3312 acquires a predicted organization identifier, which is the organization identifier corresponding to the prediction instruction.

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

(Step S2504) The score acquisition means 3312 acquires a feature group using one or more pieces of predicted everyday life information acquired in step S2503. This feature group acquisition process has been described using the flowchart in FIG. 16.

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

(Step S2506) The score acquisition means 3312 acquires a prediction vector whose elements are each feature in the feature group acquired in step S2504, and acquires a prediction score using the prediction vector and the correspondence relationship information acquired in step S2505. The process returns to the upper level process. An example of the algorithm for acquiring the prediction score is described above.

Next, an example of the operation of the terminal device 4 will be described using the flowchart in FIG. 26.

(Step S2601) The terminal reception unit 42 determines whether or not daily information has been acquired. If daily information has been acquired, the process proceeds to step S2602. If daily information has not been acquired, the process proceeds to step S2605.

(Step S2602) The device processing unit 43 acquires the member identifier from the device storage unit 41.

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

(Step S2604) The terminal transmission unit 44 transmits the information configured in step S2603 to the score prediction device 3. Return to step S2601.

(Step S2605) The terminal reception unit 42 determines whether or not a prediction instruction has been received. If a prediction instruction has been received, the process proceeds to step S2606; if a prediction instruction has not been received, the process returns to step S2601.

(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 daily information to be used for predicting the score. Note that the organization identifier and period information have default values, and may be stored in the device storage unit 41.

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

(Step S2608) The 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. If a predicted score has not been received, the process returns to step S2608.

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

In addition, in the flowchart in Figure 26, processing ends when the power is turned off or an interrupt occurs to end processing.

Specific examples of the operation of the score prediction device 3 in this embodiment are described below. There are eight specific examples. 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 specific examples below, a method of obtaining a predicted score using a learning device is described, but as described above, a predicted score may also be obtained using other correspondence information such as a correspondence table or an arithmetic formula.

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

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 emails that are paired with two or more member identifiers and are for a unit period (here, one year) (e.g., each year from 2000 to 2019) for constructing a learning device, by associating them with the member identifiers, from the communication information storage means 3122 for each unit period.

Then, through the above-described processing, the response 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 (database of two or more member information) not shown for the organizational identifier "A".

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 (e.g., an average value) of the response rates of all members for each unit period.

Next, the correspondence acquisition means 3311 reads out the teacher scores corresponding to each year from the teacher score storage unit 313. Note that the teacher scores correspond to, for example, the identifier of the unit period (here, year) and the organization identifier "A".

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

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 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.

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 predicted daily information, which is an email paired with the above organization identifier and is sent on or after January 1, 2020, from the communication information storage means 3122.

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 (e.g., average value) of the response rates of all members.

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, the variance, etc.

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

The terminal device 4 then receives and outputs the predicted score.

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

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 daily reports that are paired with two or more member identifiers, for each year (e.g., from 2000 to 2019), from the business input information storage means 3121, by associating the member identifiers with the time identifiers.

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.

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 volume 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 specified period (here, one year), and the average of the occurrence rate of positive words in the daily reports for a specified period.

Next, the correspondence acquisition means 3311 acquires a teacher vector for each year, the elements of which are each of the acquired daily report attribute values. 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.

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, which is then paired with the organization identifier "A" and stored in the correspondence information storage unit 314.

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 predicted daily information, which is a daily report paired with the above organization identifier "A" and is a daily report from January 1, 2020 onwards, from the business input information storage means 3121.

Next, the score acquisition means 3312 acquires two or more daily report attribute values for each member identifier using the daily reports acquired 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 for all members, and constructs a predictive vector whose elements are the representative values of each of the two or more daily report attribute values. Note that the predictive vector is a vector whose elements are, for example, the average value of the change information on the amount of data, the occurrence rate of negative words, and the occurrence rate of positive words.

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.

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

The terminal device 4 then receives and outputs the predicted score.

(Specific Example 3)
In the third example, the predicted score is obtained by using the overtime hours in the attendance status information. In the third example, the unit period is one month.

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), by associating it with the member identifier and the time identifier from the attendance status information storage means 3123.

Next, the correspondence acquisition means 3311 uses the attendance information for each member identifier and each year to calculate the average monthly overtime hours 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.

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 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.

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, which is then paired with the organization identifier "A" and stored in the correspondence information storage unit 314.

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.

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.

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.

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

The terminal device 4 then receives and outputs the predicted score.

(Specific 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 acquisition 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 acquisition 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.

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 time identifier. The score acquisition unit 331 also acquires the representative heart rate and blood pressure for each member identifier and each year, and acquires the representative heart rate and blood pressure of all members for each year, and stores them in the measurement information storage means 3126 in pairs with the organization identifier "A" and the identifier for each year.

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 heart rate value, and the representative blood pressure value 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.

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, which is then paired with the organization identifier "A" and stored in the correspondence information storage unit 314.

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-mentioned organization identifier "A" and from January 1, 2020 onwards 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.

The score acquisition means 3312 also 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 for each piece of acquired measurement information.

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

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.

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

The terminal device 4 then receives and outputs the predicted score.

(Specific 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.

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 communication information that is paired with each of the two or more member identifiers from the communication information storage means 3122, in association with time information. The communication information is, for example, one or more types of information among chat information, SNS information, email, and text information.

Next, the correspondence acquisition means 3311 acquires one or more types of communication attribute values using the 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.

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 the 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 the identifier of each month, but, for example, only one teacher score may be stored in the teacher score storage unit 313 for each year. If 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.

Next, the correspondence acquisition means 3311 provides the acquired large amount of teacher data to a module that performs machine learning learning processing, and executes the module to acquire a learning device, which is then paired with the organization identifier "A" and stored in the correspondence information storage unit 314.

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 the communication information for May 2020, which is communication information paired with the above organization identifier "A", from the communication information storage means 3122.

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 (e.g., an average value) of each communication attribute value of all members, and constructs a prediction vector whose elements are the representative values of each of the two or more communication attribute values.

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.

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

The terminal device 4 then receives and outputs the predicted score.

(Specific 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.

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

Next, the correspondence acquisition means 3311 performs a spectrum analysis on each acquired piece of audio information for each year, and acquires two or more audio 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 audio features. Next, the correspondence acquisition means 3311 acquires a teacher vector for each year, the elements of which are the representative values of each type of feature. 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.

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, which is then paired with the organization identifier "A" and stored in the correspondence information storage unit 314.

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, audio information that is paired with the above-mentioned organization identifier "A" and is from January 1, 2020 onward.

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

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.

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

The terminal device 4 then receives and outputs the predicted score.

(Specific Example 7)
In this example, facial expression analysis is performed on a video call image, and a predicted score is obtained from a specific facial expression. Assume 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 this example, the unit period is one year.

In this 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, or neutral, etc.). Next, the correspondence acquisition means 3311 acquires the proportion of negative facial expressions ("anger", "sadness") for each year.

Next, the correspondence acquisition means 3311 acquires teacher data for each year, with the proportion of negative facial expressions as the explanatory variable and the teacher score for each year as the objective variable. The teacher score for each year is stored in the teacher score storage unit 313.

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, which is then paired with the organization identifier "A" and stored in the correspondence information storage unit 314.

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 images paired with the above-mentioned organization identifier "A" and taken on or after January 1, 2020 from the audio storage means 3128.

Next, the correspondence acquisition means 3311 performs facial expression analysis processing on each acquired image from January 1, 2020 onward to acquire a facial expression identifier. Next, the correspondence acquisition means 3311 acquires the proportion of negative facial expressions ('anger', 'sadness') from January 1, 2020 onward.

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 proportion 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.

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

The terminal device 4 then receives and outputs the predicted score.

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

The score prediction device 3 receives a prediction instruction from the terminal device 4, which includes organizational conditions having the industry identifier "trading company" and the size identifier "large company", and the organizational identifier "A" of the organization to be predicted.

Next, the correspondence acquisition means 3311 acquires one or more types of daily information corresponding to the industry identifier "trading company" and the size identifier "large company" from the daily information storage unit 312 for each organization identifier and each year.

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.

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

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, which is then 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.

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 target period (e.g., after January 1, 2020) from the daily information storage unit 312.

Next, the score acquisition means 3312 uses the acquired daily information to construct a prediction vector for 2020.

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.

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

The terminal device 4 then receives and outputs the predicted score.

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

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, business result information, health information, biological information, stress information, measurement information, audio, images, lifestyle information, etc.

In addition, according to this embodiment, it is possible to predict an organization's score without conducting a survey, using scores based on past surveys and daily information.

Furthermore, according to this embodiment, the organization's score can be predicted using machine learning technology, data mining technology, etc.

In this embodiment, the type of daily information used does not matter. In this embodiment, it is preferable to use two or more types of daily information.

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 also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software for realizing the score prediction device 3 in this embodiment is the following program. In other words, this program is a program for making a computer 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's 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 for 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 for each of the two or more members of the organization and is daily information for a period to be predicted for the organization's score, and a score output unit that outputs the predicted score.

FIG. 27 also shows the appearance of a computer that executes the programs 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.

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

In FIG. 28, in addition to a CD-ROM drive 3012, the computer 301 includes an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012 etc., a ROM 3015 for storing programs such as a boot-up program, a RAM 3016 connected to the MPU 3013 for temporarily storing instructions for application programs and providing temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card that provides a connection to a LAN.

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

The program does not necessarily have to 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-described embodiment. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner to obtain the desired results. How the computer system 300 operates is well known, and a detailed description will be omitted.

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

The program may be executed by a single computer or multiple computers. In other words, the program may be executed by centralized processing or distributed processing.

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

In addition, 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 multiple devices.

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

As described above, the score prediction device of the present invention has the effect of being able to predict the score of an organization using the daily information of its members, and is useful as a score prediction device, etc.

3 Score prediction device 4 Terminal device 31 Storage unit 32 Reception unit 33 Processing unit 34 Output unit 41 Terminal storage unit 42 Terminal reception unit 43 Terminal processing unit 44 Terminal transmission unit 45 Terminal reception unit 46 Terminal output unit 311 Organization information storage unit 312 Daily life information storage unit 313 Teacher score storage unit 314 Correspondence relationship information storage unit 331 Score acquisition unit 341 Score output unit 3121 Work input information storage means 3122 Communication information storage means 3123 Attendance status information storage means 3124 Work result information storage means 3125 Stress information storage means 3126 Measurement information storage means 3127 Image storage means 3128 Voice storage means 3129 Lifestyle information storage means 3311 Correspondence acquisition means 3312 Score acquisition means

Claims (14)

a correspondence information storage unit for storing correspondence information that is information regarding 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 . 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 a correspondence relationship between a teacher score, which is a score of the organization acquired 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 for 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 two or more pieces of predicted daily information, which are daily information for each of the two or more members of the organization and are daily information for 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 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. 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 regarding the biological body of the member, and the predicted health information includes predicted biological information regarding the biological body of the member. 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. 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. 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. 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. 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. 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. 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. 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. 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. A score prediction method , comprising: executing all of the processes performed by the score prediction device according to claim 1 . Computer,
A program for causing the score prediction device according to claim 1 to function as the score prediction device .

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