JP2022021444A - Score prediction device, score prediction method, and program - Google Patents

Abstract

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

Description

The present invention relates to a score predictor or the like that predicts the score of an organization.

Conventionally, there has been a management diagnosis support device that can comprehensively judge the soundness of management (see Patent Document 1).

This management diagnosis support device aggregates and analyzes and displays the results of a score-based questionnaire that selects an answer from a plurality of options for question items, and responds to the questionnaire from each layer constituting the organization. And, it is an analysis table to which a plurality of diagnostic elements related to corporate management are assigned to each of the plurality of question items, and points indicating weighting are set for each diagnostic element corresponding to the plurality of question items. The analysis means includes a storage means in which an analysis table is stored, an analysis means for analyzing the answer result of the questionnaire, and an output means for outputting the analysis result by the analysis means, and the analysis means is the answer result of the question item. Multiply the answer score by and the score for each diagnostic element set in the analysis table corresponding to the question item to calculate the individual element evaluation value for each diagnostic element of each question item, and individually for all questions. The element evaluation value is aggregated for each diagnostic element to calculate the individual element aggregated value, and the output means is characterized in that the individual element aggregated value is aggregated for each layer and output as a graph or a table. And.

Japanese Patent No. 6621903

However, in the prior art, as a result of aggregating and analyzing the results of the questionnaire, information that can judge the soundness of management is presented, and the daily information of the members of the organization is used to determine the score of the organization. I couldn't predict it.

The score prediction device of the first invention is daily information that can be acquired in the daily life of each of two or more members of the organization, and one or more predicted daily information that is daily information in the period for which the score of the organization is to be predicted. The daily information storage unit to be stored, the teacher score which is the score of the organization obtained by using the multiple organization answer information showing the answer of the question to each of two or more members of the organization, and the period corresponding to the question or answer. With a score corresponding to 1 or more predicted daily information using 1 or more teacher daily information, which is daily information of 2 or more members, and 1 or more predicted daily information. It is a score prediction device including a score acquisition unit for acquiring a certain predicted score and a score output unit for outputting a predicted score.

With such a configuration, the score of the organization can be predicted by using the daily information of the members of the organization.

Further, in the score prediction device of the second invention, with respect to the first invention, each one or more teacher daily information includes teacher work information regarding the work performed by the member, and one or more prediction daily information is included. , Includes predicted work information about the work performed by the members, and the score acquisition department collects one or more teacher daily information including teacher work information and the correspondence relationship between the teacher score, and one or more predicted daily information including predicted work information. It is a score prediction device that obtains a prediction score by using it.

With such a configuration, the score of the organization can be predicted by using the business information about the business performed by the members of the organization.

Further, in the score prediction device of the third invention, with respect to the second invention, the teacher work information includes the teacher input information input by the member, and the prediction work information is the prediction input information input by the member. The score acquisition unit acquires a predicted score by using the correspondence between one or more teacher daily information including the teacher input information and the teacher score, and one or more predicted daily information including the predicted input information. It is a device.

With such a configuration, the score of the organization can be predicted by using the input information input by the members of the organization.

Further, in the score prediction device of the fourth invention, with respect to the third invention, the teacher input information includes the teacher work input information regarding the input for the performance of the member's own work, and the prediction input information is , Includes predictive work input information regarding input for the performance of the member's own work, and the score acquisition department is responsible for the correspondence between one or more teacher daily information including teacher work input information and the teacher score, and predictive work input. It is a score prediction device that acquires a prediction score by using one or more prediction daily information including information.

With such a configuration, the score of the organization can be predicted by using the teacher work input information regarding the input for the performance of the own work of the members of the organization.

Further, in the score prediction device of the fifth invention, with respect to the second or third invention, the teacher input information includes the teacher communication information regarding the communication between two or more members, and the prediction input information is. Includes predictive communication information regarding communication between two or more members, and the score acquisition department has one or more predictions including the correspondence between one or more teacher daily information including teacher communication information and the teacher score, and predictive communication information. It is a score prediction device that acquires a prediction score using daily information.

With such a configuration, the score of the organization can be predicted by using the communication information regarding the communication between the members of the organization.

Further, in the score prediction device of the sixth invention, for any one of the third to fifth inventions, the score acquisition unit inputs the data amount information and the teacher input information for specifying the data amount of the teacher input information. One or more types of information are acquired: input frequency information that specifies the frequency, analysis information that shows the analysis result of the teacher input information, and timing information that specifies the input timing of the teacher input information, and the amount of data of the predicted input information. One or more types of information, including one or more types of information acquired from information, input frequency information of predictive input information, analysis information of predictive input information, timing information of predictive input information, and one or more types of information acquired from teacher input information. Acquire the correspondence information that identifies the correspondence between the teacher's identity and the teacher score, and use one or more predictive features including the correspondence information and one or more types of information acquired from the prediction input information to obtain the prediction score. It is a score prediction device to be acquired.

With such a configuration, the score of the organization can be predicted by using one or more kinds of information of the data amount information of the input information, the input frequency information, the analysis information of the input information, and the timing information of the input information.

Further, in the score prediction device of the seventh invention, with respect to the second invention, the teacher work information includes the teacher attendance status information regarding the attendance of the members, and the prediction work information is the prediction attendance regarding the attendance of the members. The score acquisition department uses the correspondence between one or more teacher daily information including the teacher attendance status information and the teacher score, and one or more predicted daily information including the predicted attendance status information to obtain the predicted score. It is a score prediction device to be acquired.

With such a configuration, the score of the organization can be predicted by using the attendance status information of the members of the organization.

Further, in the score prediction device of the eighth invention, with respect to the second invention, the teacher work information includes the teacher work result information regarding the result of the work execution of the member, and the prediction work information is the work of the member. The score acquisition department includes one or more predicted daily information including the predicted work result information regarding the execution result, and the correspondence relationship between one or more teacher daily information including the teacher work result information and the teacher score, and one or more predicted daily information including the predicted work result information. It is a score prediction device that obtains a prediction score by using it.

With such a configuration, the score of the organization can be predicted by using the business result information regarding the result of the business performance of the organization.

Further, in the score prediction device of the ninth invention, with respect to the first invention, each one or more teacher daily information includes teacher health information regarding the health of the members, and one or more prediction daily information includes. Includes predicted health information about the health of the members, the score acquisition department uses one or more teacher daily information including teacher health information and the correspondence between the teacher score, and one or more predicted daily information including predicted health information. , A score prediction device that acquires a prediction score.

With such a configuration, the score of the organization can be predicted by using the health information of the members of the organization.

Further, in the score prediction device of the tenth invention, with respect to the ninth invention, the teacher health information includes the teacher's biological information regarding the living body of the member, and the predicted health information is the predicted biological information regarding the living body of the member. The score acquisition unit acquires a predicted score by using the correspondence between one or more teacher daily information including the teacher biometric information and the teacher score, and one or more predicted daily information including the predicted biometric information. It is a device.

With such a configuration, the score of the tissue can be predicted by using the biological information of the members of the tissue.

Further, in the score prediction device of the eleventh invention, with respect to the tenth invention, the teacher biometric information includes the teacher stress information regarding the stress of the members, and the predicted biometric information is the predicted stress regarding the stress of the members. A score that includes information and the score acquisition unit acquires a predicted score using the correspondence between one or more teacher daily information including teacher stress information and the teacher score, and one or more predicted daily information including predicted stress information. It is a predictor.

With such a configuration, the score of the organization can be predicted by using the stress information of the members of the organization.

Further, in the score prediction device of the twelfth invention, with respect to the tenth or eleventh invention, the teacher biometric information includes the teacher measurement information which is the measurement result of the living body of the member, and the predicted biometric information is , The score acquisition unit includes one or more teacher daily information including teacher measurement information and the correspondence relationship between the teacher score, and one or more predictive daily information including predictive measurement information. It is a score prediction device that acquires a prediction score using.

With such a configuration, the score of the tissue can be predicted by using the measurement information which is the measurement result of the living body of the member of the tissue.

Further, in the score prediction device of the thirteenth invention, with respect to any one of the tenth to the twelfth inventions, the teacher biometric information is a feature of an image of a member or an image of a member. The predicted biometric information includes the teacher biometric information, which is the amount, the voice emitted by the member, or the feature amount of the voice emitted by the member, and the predicted biological information is the feature of the image taken by the member or the image taken by the member. The score acquisition unit includes one or more teacher daily information including the teacher biometric information and the teacher score, which includes the predicted biological feature information which is the amount, the voice emitted by the member, or the feature amount of the voice emitted by the member. It is a score prediction device that acquires a prediction score by using one or more prediction daily information including the correspondence relationship of the above and the prediction biological feature information.

With such a configuration, the score of the organization can be predicted by using the image of the member of the organization or the voice of the member.

Further, in the score prediction device of the fourteenth invention, for any one of the ninth to thirteenth inventions, the teacher health information includes the teacher life information regarding the lives of the members, and the predicted health information is. Includes predicted life information about the lives of the members, and the score acquisition department uses one or more teacher daily information including teacher life information and the correspondence relationship between the teacher score, and one or more predicted daily information including predicted life information. , A score prediction device that acquires a prediction score.

With such a configuration, the score of the organization can be predicted by using the living information of the members of the organization.

Further, in the score prediction device of the fifteenth invention, the score acquisition unit specifies the correspondence between one or more teacher daily information and the teacher score for any one of the first to fourteenth inventions. It is a score prediction device including a correspondence acquisition means for acquiring correspondence information and a score acquisition means for acquiring a prediction score by using the correspondence information and one or more prediction daily information.

With such a configuration, the score of the organization can be predicted by using the daily information of the members of the organization.

Further, the score prediction device of the sixteenth invention corresponds to one or more teacher daily information and one or more organizational attribute values with respect to the fifteenth invention, and the correspondence is acquired. The means acquires correspondence information that specifies the correspondence between one or more teacher daily information and the teacher score for each organizational condition configured using one or more organizational attribute values, and the score acquisition means is a prediction target. It is a score prediction device that acquires a prediction score by using correspondence relation information corresponding to an organization condition that matches the organization of the above and one or more prediction daily information.

With such a configuration, it is possible to obtain a predicted score that matches the attribute value of the organization.

Further, the score prediction device of the seventeenth invention is a machine learning algorithm using one or more teacher daily information and a teacher score as a correspondence acquisition means for the fifteenth or sixteenth invention. To acquire correspondence information, which is a learning model with one or more teacher daily information as an explanatory variable and a teacher score as an objective variable, the score acquisition means is a learning model and one or more predicted daily information. It is a score prediction device that performs prediction processing by a machine learning algorithm and acquires a prediction score.

With such a configuration, machine learning techniques can be used to predict the score of the organization using the daily information of the members of the organization.

Further, the score prediction device of the eighteenth invention is a vector having two or more elements using one or more teacher daily information as the corresponding acquisition means for the fifteenth or sixteenth invention. A certain teacher vector is acquired, a correspondence relationship information which is a correspondence table having one or more correspondence information which is a set of a teacher vector and a teacher score is acquired, and the score acquisition means uses one or more predicted daily information. It is a score prediction device that acquires a prediction vector, which is a vector having two or more elements, applies the prediction vector to a correspondence table, and obtains a prediction score.

With such a configuration, data mining techniques can be used to predict the score of the organization using the daily information of the members of the organization.

Further, the score prediction device of the nineteenth invention is a vector having two or more elements using one or more teacher daily information as the corresponding acquisition means for the fifteenth or sixteenth invention. A certain teacher vector is acquired, each element of the teacher vector is input, and the correspondence information which is an arithmetic expression which outputs the teacher score is acquired, and the score acquisition means uses one or more predicted daily information and two or more. It is a score prediction device that acquires a prediction vector, which is a vector having the elements of, gives each element of the prediction vector to a calculation formula, executes the calculation formula, and obtains a prediction score.

With such a configuration, data analysis techniques can be used to predict the score of the organization using the daily information of the members of the organization.

According to the score prediction device according to the present invention, the score of an organization can be predicted by using the daily information of the members of the organization.

Conceptual diagram of the information system A in the first embodiment Block diagram of the information system A A flowchart illustrating an operation example of the information processing apparatus 1. A flowchart illustrating an example of the score calculation process. A flowchart illustrating an example of the same impact calculation process. Figure showing the same item information management table Diagram showing the response information of the organization Figure showing the individual score table Figure showing the same output example Figure showing the same output example Conceptual diagram of the information system B in the second embodiment Block diagram of the information system B Block diagram of the score prediction device 3 A flowchart illustrating an operation example of the score prediction device 3. Flow chart explaining an example of the corresponding acquisition process Flow chart explaining an example of allotropy group acquisition processing Flow chart explaining an example of the same business input information processing A flowchart explaining an example of the communication information processing Flow chart explaining an example of attendance status information processing A flowchart explaining an example of the stress information processing A flowchart illustrating an example of the measurement information processing. A flowchart illustrating an example of the same image information processing A flowchart explaining an example of the voice information processing A flowchart explaining an example of cohabitation information processing A flowchart illustrating an example of the score acquisition process. A flowchart illustrating an operation example of the terminal device 4. Overview of the computer system in the above embodiment Block diagram of the computer system

Hereinafter, embodiments of the score prediction device and the like will be described with reference to the drawings. In addition, since the components with the same reference numerals perform the same operation in the embodiment, the description may be omitted again.

(Embodiment 1)
In the present embodiment, an information system including an information processing device that acquires and outputs the degree of influence of each item on the overall score of the organization calculated from the answers to two or more items will be described. The organization is, for example, a so-called company, a sole proprietorship, a local public body, or the like, and is widely understood. An organization is, for example, an organization that does some work. In addition, the overall score of the organization may be called the engagement score. In addition, the information system may be called an engagement system. Further, the information processing device may be referred to as an engagement device.

Further, in the present embodiment, an information system including an information processing device for calculating an item score and an overall score of an organization will be described using the expectation level of each item. The information system may be called an engagement system.

Further, in the present embodiment, an information system including an information processing device having a score adjusting function for adjusting an overall score by using correlation information regarding a correlation between satisfaction and expectation will be described.

Further, in the present embodiment, an information system including an information processing device that considers the attribute value of the organization and outputs a different degree of influence for each attribute value will be described.

FIG. 1 is a conceptual diagram of the information system A in the present embodiment. The information system A includes an information processing device 1 and one or more terminal devices 2. The information processing device 1 is a so-called server device here. The information processing device 1 is, for example, a cloud server or an ASP server, but its type and installation location do not matter. The terminal device 2 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, a so-called personal computer, and the type thereof does not matter.

FIG. 2 is a block diagram of the information system A according to the present embodiment.

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

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 that constitutes the information processing device 1. The various types of information are, for example, item information described later, organization response information described later, and an individual score table described later. The individual score table may be referred to as an engagement score table or the like.

The item information storage unit 111 stores two or more item information. Item information is information about items of an organization. The item may be a question about the organization. The item may also correspond to a question about the organization. The two or more items include, for example, a comprehensive item relating to the comprehensive items of the organization and an individual item which is an individual item of the organization. The comprehensive item is usually an item for a question with a high degree of abstraction. In addition, individual items are usually items for questions with a lower level of abstraction (more specific questions). Further, the two or more item information of the item information storage unit 111 includes, for example, the item information of four comprehensive items and the item information of 64 individual items. Further, the item corresponds to, for example, any one of two or more objects. The target can be called a factor. In addition, the target may be said to be matters related to the organization. Further, the item information includes, for example, an item identifier for identifying the item and question information. The item identifier is, for example, an ID, an item name, or the like. The item identifier may be the question information itself. Question information is information indicating a question. The question is usually a questionnaire question. In addition, a matter may be said to be an object or an item.

The organization response information storage unit 112 stores the organization response information of two or more organizations. One organization response information is a set of response information of two or more members of one organization. One organization response information has two or more member response information corresponding to each of two or more members. The member answer information is information including the result of the member answering the question for the item. The member response information has two or more item response information. The member response information usually has item response information for the number of items. The item response information has an item identifier and response information. Answer information is information about the answer to the question. The response information has, for example, satisfaction information. Satisfaction information is information indicating an answer regarding the degree of satisfaction of a member with respect to an item. Satisfaction information is, for example, information for specifying satisfaction with an item. Satisfaction information is classified into two or more classes. Satisfaction information can be, for example, a natural number from 1 to 5. However, the satisfaction information may be an evaluation value having a rank or order such as A, B, or C, or may be a natural number from 1 to 100. Further, the response information includes, for example, satisfaction level information and expectation level information. Expectation information is information indicating an answer regarding the degree of expectation of a member for an item. The expectation degree information is, for example, information for specifying the degree of expectation for an item. Expectation information is classified into two or more classes. The expectation information can be, for example, a natural number from 1 to 5. However, the expectation degree information may be an evaluation value having a rank or order such as A, B, and C, or may be a natural number from 1 to 100. The members are, for example, employees of a company, employees of a school, employees of a government office, etc., but may also include officers of the company.

The comprehensive items are, for example, company satisfaction, which indicates the degree of satisfaction with the company, work satisfaction, which indicates the degree of satisfaction with the work, boss satisfaction, which indicates the degree of satisfaction with the boss, and workplace. Workplace satisfaction, etc., which indicates the degree of satisfaction. In addition, individual items include, for example, the company's business superiority, transmission and communication of strategic goals, overall sense of solidarity, and validity of evaluation / salary.

In addition, the organization response information usually corresponds to the organization identifier. The organization identifier is an organization name, an ID that identifies an organization, or the like. Further, it is preferable that the organization response information corresponds to one or more attribute values of the organization. The attribute values include, for example, an industry identifier indicating the type of industry of the organization (for example, bank, apparel, manufacturer, etc.), a scale identifier for classifying the size of the organization (for example, large company, small and medium-sized enterprise, individual business, etc.), and the area of the head office. It is the area identifier shown.

The individual score table is stored in the individual score table storage unit 113. Here, the individual score table is information in which the item score is determined when satisfaction information and expectation information are given. Here, the individual score table is, for example, a table having an axis of satisfaction information and an axis of expectation information, an individual score is described in each cell of the table, and satisfaction information and expectation information are determined. In this table, the item score is determined. It is preferable that the individual score table is a table in which a larger item score is obtained as the satisfaction level indicated by the satisfaction level information is larger, and a larger item score is obtained as the expectation level indicated by the expectation level information is smaller. ..

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

Further, when determining the item score, a plurality of sets of satisfaction information, expectation information, and item score may be learned by machine learning, and the acquired learning information may be used. In such a case, it is applied to satisfaction information, expectation information, and learning information, and an item score is acquired by machine learning. For machine learning here, for example, SVR, deep learning, decision tree, and the like can be used. However, the machine learning algorithm does not matter.

The reception unit 12 receives various information and instructions. The various information and instructions are, for example, output instructions such as scores, organization response information, questionnaire response information (member response information), and the like. The score or the like output instruction is an instruction to output the score or the like. Further, here, the reception is usually reception from the terminal device 2, but reception of information input from an input device such as a keyboard, a mouse, and a touch panel, and reception from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory. It may be regarded as a concept including acceptance of read information.

The processing unit 13 performs various processes. The various processes are, for example, processes performed by the item score acquisition unit 131, the total score acquisition unit 132, and the influence degree acquisition unit 133. The various processes are, for example, a process of accumulating received organization response information, questionnaire response information, and the like in the storage unit 11.

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

For example, the item score acquisition unit 131 statistically processes the satisfaction information included in the response information of two or more items included in the organization response information of the organization for each organization, and sets the item score for each organization and each item. get. The item score acquisition unit 131 calculates, for example, an average value of satisfaction information for each organization and each item, and stores the average value as an item score in a buffer or storage unit 11 (not shown) in pairs with an item identifier. May be. Further, the item score acquisition unit 131 calculates a weighted average value of satisfaction information by weighting differently according to the attribute value of the member for each organization and each item, and sets the weighted average value as an item. As a score, it may be stored in a buffer or a storage unit 11 (not shown) as a pair with the item identifier. The attribute values of the members are, for example, job title, years of service, gender, and the like. For example, the item score acquisition unit 131 may compare the satisfaction information of an employee with a long service period with the satisfaction information of an employee with a short service period, weight it, and calculate a weighted average.

The item score acquisition unit 131 acquires an item score for each organization by using satisfaction information and expectation information for at least some items. The item for which the item score is acquired using the satisfaction level information and the expectation level information is, for example, an individual item.

It is preferable for the item score acquisition unit 131 to acquire the item score so that, for example, if the satisfaction information is high, the score is high, and if the expectation information is low, the score is high.

The item score acquisition unit 131 calculates, for example, the average value of the satisfaction information of the answer information and the average value of the expectation information of the answer information for each of the two or more items for each of the two or more organizations. Obtain the item score for each item using the two average values.

For example, the item score acquisition unit 131 may apply satisfaction information and expectation information to an individual score table and acquire an item score for each item. Further, the item score acquisition unit 131 may, for example, apply the statistical processing result of the satisfaction information and the statistical processing result of the expectation degree information to the individual score table, and acquire the item score for each item. For example, the item score acquisition unit 131 may apply an average value of satisfaction information and an average value of expectation information to an individual score table, and 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, and 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 is, for example, an increasing function having an average value of satisfaction information as a parameter, and a decreasing function having an average value of expectation information as a parameter is used to calculate an item score for each item. Is also good.

The total score acquisition unit 132 acquires a total score by using two or more item scores for each organization response information of two or more. The overall score is the overall score for each organization. The overall score acquisition unit 132 usually acquires a higher overall score as the score of each item of 2 or more is better.

It is preferable for the total score acquisition unit 132 to acquire the total score by using the score adjustment function as follows. The score adjustment function is a function for adjusting the score by using the correlation information regarding the degree of correlation between the satisfaction level information and the expectation level information. Here, it is preferable that the score adjustment function has a higher overall score as the correlation between the satisfaction information and the expectation information is larger.

For example, the total score acquisition unit 132 acquires a provisional total score, which is a provisional total score for each organization, by using two or more item scores for each organization, and obtains satisfaction information of each of the two or more items. Correlation information regarding the degree of correlation with the expectation degree information is acquired, and the total score is acquired from the provisional total score so that the score increases as the degree of correlation increases using the correlation information. The correlation information may be a correlation value between a set of satisfaction information of two or more items and a set of expectation information, or the satisfaction information of each of two or more items and the expectation of each of two or more items. It may be a value calculated by an arithmetic expression whose parameter is the number of items whose difference from the degree information is less than or equal to the threshold value, or the satisfaction information of each item of 2 or more and the expectation information of each item of 2 or more. The difference may be a value calculated by an arithmetic expression using the number of items whose satisfaction information is smaller than the threshold value as a parameter. That is, the algorithm for acquiring the correlation information does not matter.

The total score acquisition unit 132 uses both the item score for the comprehensive item and the item score for the individual item, compares the item score for the comprehensive item with the item score for the individual item, and increases the weight. Obtaining an overall score is preferred.

The total score acquisition unit 132 may calculate the total score by, for example, "total score = α × statistical score of item score for comprehensive item + β × statistical score of item score for individual item". Here, it is preferable that (α> β). That is, it is preferable that the total score acquisition unit 132 obtains the total score by comparing the item score for the comprehensive item with the item score for the individual item and increasing the weight. Further, for example, "α = 0.7, β = 0.3". The statistical score of the item score for the comprehensive item is, for example, an average value of the item scores for the comprehensive item, a weighted average, or the like. The statistical score of the item score for the individual item is, for example, the average value of the item score for the individual item, the weighted average, or the like.

Further, the total score acquisition unit 132 calculates the provisional total score by, for example, "provisional total score = α × statistical score of item score for comprehensive item + β × statistical score of item score for individual item", and further. You may adjust the score by the above score adjustment function and calculate the total score. Further, the total score acquisition unit 132 may calculate a deviation value of the total score of each organization by using, for example, the total score of a plurality of organizations, and use the deviation value as the final total score.

The influence degree acquisition unit 133 acquires the influence degree that the item score has on the total score for each item. The degree of influence of the item score on the total score may be said to be the degree of influence of the item on the total score.

The influence degree acquisition unit 133 uses the item score of 2 or more items of each of 2 or more organizations and the total score of each of 2 or more organizations to determine the degree of influence of the item score of each item on the total score. Get every time. The degree of influence is, for example, information indicating the influence of the item score on the total score, and is the degree of correlation of the set of item scores of a plurality of organizations with respect to the set of the total scores of the plurality of organizations. Since the process of acquiring the degree of correlation, the correlation value, and the correlation information is a known technique, detailed description thereof will be omitted.

The influence degree acquisition unit 133 uses the total score of each organization of 2 or more and the item score of each item of each item of 2 or more, and is information on the correlation between the item score of each item and the total score. Is calculated for each item.

It is preferable that the influence degree acquisition unit 133 acquires the influence degree that the item score of each item has on the total score for each item for each attribute value of the organization. That is, when the attribute value of the organization is an industry identifier, it is preferable that the influence degree acquisition unit 133 acquires the influence degree of each item for each industry according to the industry identifier.

The output unit 14 outputs various information. The various types of information are, for example, a set of paired information of an item identifier and an item score. Further, the various information is, for example, a total score. Further, various types of information are, for example, a set of paired information of an item identifier and an influence degree. Here, the output is usually a transmission to an external device such as the terminal device 2. However, output is a concept that includes display on a display, projection using a projector, printing with a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or programs. You may think that there is.

The item score output unit 141 outputs one or more item scores. The item score output unit 141 usually outputs the item score of the item identified by the item identifier in association with the item identifier. The item score output unit 141 outputs, for example, an item score paired with an organization identifier of an output instruction such as a score in association with the item identifier. It is preferable that the item score output unit 141 outputs one or more item scores in association with the organization identifier. Further, it is preferable that the item score output unit 141 outputs the item score in a manner in which the item score of the comprehensive item and the item score of the individual item can be visually distinguished.

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 influence degree output unit 143 outputs the influence degree acquired by the influence degree acquisition unit 133 in association with each item. It is preferable that the influence degree output unit 143 outputs the influence degree acquired by the influence degree acquisition unit 133 in association with the item identifier of each item.

Further, it is preferable that the influence degree output unit 143 outputs the influence degree in association with each item so that the classification of the influence degree can be visually distinguished.

Various information is stored in the terminal storage unit 21 constituting the terminal device 2. The various information is, 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, information, and the like. Here, acceptance means acceptance of information input from an input device such as a keyboard, mouse, or touch panel, reception of information transmitted via a wired or wireless communication line, recording of an optical disk, magnetic disk, semiconductor memory, or the like. It is a concept including acceptance of information read from a medium. The various instructions and information are, for example, output instructions such as scores, organization response information, questionnaire response information, and the like.

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

The various processes of the terminal processing unit 23 are, for example, processes for configuring the information received by the terminal receiving unit 25 into the displayed data. The various processes are, for example, processes configured as instructions for transmitting an instruction or the like received by the terminal reception unit 22.

The terminal transmission unit 24 transmits various instructions, information, and the like to the information processing device 1. The various instructions and information are, for example, instructions configured by the terminal processing unit 23, instructions and information received by the terminal reception unit 22 and the like.

The terminal receiving unit 25 receives various information from the information processing device 1. The various types of information are, for example, an item score, an overall score, and a degree of influence.

The terminal output unit 26 acquires various types of information. The various types of information are, for example, information received by the terminal receiving unit 22, information received by the terminal receiving unit 25, and information configured by the terminal processing unit 23. The various types of information are, for example, an item score, an overall score, and a degree of influence.

A non-volatile recording medium is suitable for the storage unit 11, but a volatile recording medium can also be realized.

The process of storing information in the storage unit 11, the item information storage unit 111, the organization response information storage unit 112, the individual score table storage unit 113, and the terminal storage unit 21 does not matter. For example, the information may be stored in the storage unit 11 or the like via the recording medium, or the information transmitted via the communication line or the like may be stored in the storage unit 11 or the like. Alternatively, the information input via the input device may be stored in the storage unit 11 or the like.

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

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

The output unit 14, the item score output unit 141, the total score output unit 142, the influence degree output unit 143, and the terminal transmission unit 24 are usually realized by wireless or wired communication means, but even if they are realized by broadcasting means. good.

The terminal output unit 26 may or may not include an output device such as a display or a speaker. The terminal output unit 26 can be realized by the driver software of the output device, the driver software of the output device, the output device, or the like.

Next, the operation of the information system A will be described. First, an operation example of the information processing apparatus 1 will be described with reference to the flowchart of FIG. It is assumed that the organization response information storage unit 112 stores the organization response information of a plurality of organizations. Further, it is assumed that the individual score table is stored in the individual score table storage unit 113.

(Step S301) The processing unit 13 determines whether or not it is the timing for calculating the score or the like. If it is the timing for calculating the score or the like, the process goes to step S302, and if it is not the timing for calculating the score or the like, the process goes to step S312. The timing of calculating the score, etc. is, for example, when an instruction is input from a user, an administrator, or the like, or when a predetermined timing is reached, two or more organization response information is received, and the organization response information is received. This is the case when the information is stored in the storage unit 112.

(Step S302) The processing unit 13 substitutes 1 for the counter i.

(Step S303) The processing unit 13 determines whether or not the i-th attribute value exists. If the i-th attribute value exists, the process goes to step S304, and if the i-th attribute value does not exist, the process returns to step S301. The i-th attribute value is the i-th attribute value of the organization, and is the attribute value of the target for which the degree of influence is calculated.

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

(Step S305) The processing unit 13 substitutes 1 for the counter j.

(Step S306) The processing unit 13 determines whether or not the j-th organization identifier exists in the two or more organization response information acquired in step S304. If the j-th organization identifier exists, the process goes to step S307, and if the j-th organization identifier does not exist, the process goes to step S310.

(Step S307) The processing unit 13 acquires the organization response information paired with the jth organization identifier.

(Step S308) The processing unit 13 calculates various scores corresponding to the j-th organization identifier by using the organization response information acquired in step S307. It should be noted that the calculation of the score has the same meaning as the acquisition of the score. Further, the score calculation process will be described with reference to the flowchart of FIG.

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

(Step S310) The influence degree acquisition unit 133 calculates the influence degree of each item. The influence degree calculation process will be described with reference to the flowchart of FIG.

(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 output instruction such as a score has been received. If the score or the like output instruction is accepted, the process proceeds to step S313, and if the score or the like output instruction is not accepted, the process returns to step S301.

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

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

(Step S315) The processing unit 13 acquires two or more item scores paired with the organization identifier acquired in step S313 and an item identifier paired with the item score from the storage unit 11 or a buffer (not shown). That is, the processing unit 13 acquires as many pairs of item identifiers and item scores as there are items.

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

(Step S317) The processing unit 13 acquires two or more influence degrees paired with the attribute value acquired in step S314 and an item identifier paired with the impact degree from the storage unit 11 or a buffer (not shown). That is, the processing unit 13 acquires as many pairs of item identifiers and influence degrees as there are items.

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

(Step S319) The output unit 14 outputs the information configured in step S318. Return to step S301. The output here is usually transmission to the terminal device 2 that has transmitted an output instruction such as a score. Further, it is assumed that the information to be output includes at least a set of an item identifier and an influence degree. Further, it is preferable that the output information includes a set of an item identifier and an item score, and a total score.

In the flowchart of FIG. 3, the storage route of the organization response information to the organization response information storage unit 112 does not matter.

Further, in the flowchart of FIG. 3, the degree of influence was acquired for each attribute value of the organization. However, it goes without saying that the degree of influence of each item may be acquired using the organization response information of all organizations regardless of the attribute value of the organization.

Further, in the flowchart of FIG. 3, the processing is terminated by the power off or the interrupt of the processing termination.

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

(Step S401) The item score acquisition unit 131 substitutes 1 for the counter i.

(Step S402) The item score acquisition unit 131 determines whether or not the item identifier of the i-th individual item exists in the organization response information acquired in step S307. If the item identifier of the i-th individual item exists, the process goes to step S403, and if it does not exist, the process goes to step S409.

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

(Step S404) The item score acquisition unit 131 statistically processes the satisfaction information acquired in step S403 and calculates the statistical satisfaction information. Here, the item score acquisition unit 131 calculates, for example, statistical satisfaction information which is an average value of satisfaction information acquired in step S403. Then, the item score acquisition unit 131 stores the calculated statistical satisfaction information in the storage unit 11 or a buffer (not shown) in pair with the item identifier of the i-th individual item.

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

(Step S406) The item score acquisition unit 131 statistically processes the expectation degree information acquired in step S405 and calculates the statistical expectation degree information. Here, the item score acquisition unit 131 calculates, for example, statistical expectation information which is an average value of the expectation information acquired in step S403. Then, the item score acquisition unit 131 stores the calculated statistical expectation degree information in the storage unit 11 or a buffer (not shown) in pair with the item identifier of the i-th individual item.

(Step S407) The item score acquisition unit 131 acquires the item score of the i-th individual item of the organization of interest by using the statistical satisfaction information and the statistical expectation information. The item score acquisition unit 131 applies the statistical satisfaction information and the statistical expectation information to the individual score table of the individual score table storage unit 113, and acquires the item score of the i-th individual item. Then, the item score acquisition unit 131 stores the acquired item score in the storage unit 11 or a buffer (not shown) in pair 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 substitutes 1 for the counter j.

(Step S410) The item score acquisition unit 131 determines whether or not the item identifier of the j-th comprehensive item exists in the organization response information acquired in step S307. If the item identifier of the j-th comprehensive item exists, the process goes to step S411, and if it does not exist, the process goes to step S414.

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

(Step S412) The item score acquisition unit 131 statistically processes the satisfaction information acquired in step S411 and calculates the statistical satisfaction information. Here, the item score acquisition unit 131 calculates, for example, statistical satisfaction information which is an average value of satisfaction information acquired in step S411. Then, the item score acquisition unit 131 stores the calculated statistical satisfaction information in the storage unit 11 or a buffer (not shown) in pair with the item identifier of the jth 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 the item scores of the individual items from the storage unit 11 or a buffer (not shown). The item score of the individual item is the score acquired in step S407.

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

(Step S416) The total score acquisition unit 132 acquires statistical satisfaction information of all the items of the comprehensive items from the storage unit 11 or a buffer (not shown).

(Step S417) The total score acquisition unit 132 statistically processes the statistical satisfaction information of all the items acquired in step S416, and calculates the statistical satisfaction information of the comprehensive items. The total score acquisition unit 132 calculates, for example, the average value of the statistical satisfaction information of all the items acquired in step S416, and acquires it as the statistical satisfaction information of the comprehensive items.

(Step S418) The comprehensive score acquisition unit 132 calculates a provisional total score from the comprehensive score of the individual items acquired in step S415 and the statistical satisfaction information of the comprehensive items acquired in step S417. The total score acquisition unit 132 calculates the provisional total score by, for example, the calculation formula “provisional total score = α × statistical satisfaction information of comprehensive items + β × total score of individual items”.

(Step S419) The comprehensive score acquisition unit 132 relates to a set of satisfaction information and a set of expectation information and a correlation from a set of satisfaction information of all individual items and a set of expectation information of all individual items. Get correlation information.

(Step S420) The total score acquisition unit 132 adjusts the provisional total score acquired in step S418 using the correlation information acquired in step S419, and acquires the total score. Return to higher-level processing. The total score acquisition unit 132 acquires the total score so that the higher the degree of correlation indicated by the correlation information, the higher the total score.

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

(Step S501) The influence degree acquisition unit 133 substitutes 1 for the counter i.

(Step S502) The influence degree acquisition unit 133 determines whether or not the item identifier of the i-th individual item exists in the two or more organization 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 higher-level processing.

(Step S503) The influence degree acquisition unit 133 acquires the item scores of a plurality of organizations paired with the item identifiers of the i-th individual items from the two or more organization response information acquired in step S304.

(Step S504) The influence degree acquisition unit 133 acquires the total score of a plurality of organizations from the two or more organization response information acquired in step S304.

(Step S505) The influence degree acquisition unit 133 acquires an influence degree indicating the degree of correlation between the plurality of item scores acquired in step S503 and the plurality of total scores acquired in step S504. Then, the influence degree acquisition unit 133 pairs with the item identifier of the i-th individual item and stores the acquired influence degree in the storage unit 11 or a buffer (not shown).

(Step S506) The influence degree 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, and the like. Next, the terminal processing unit 23 is configured as an instruction or the like for transmitting an instruction or the like received by the terminal reception unit 22. The terminal transmission unit 24 transmits an instruction or the like configured by the terminal processing unit 23 to the information processing device 1. As a result, the terminal receiving unit 25 receives information from the information processing apparatus 1 in response to transmission of an instruction or the like. Next, the terminal processing unit 23 configures the information received by the terminal receiving unit 25 into output data. Next, the terminal output unit 26 outputs the information configured by the terminal processing unit 23.

Hereinafter, the specific operation of the information system A in the present embodiment will be described. The conceptual diagram of the information system A is FIG.

Now, it is assumed 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 showing the items of the questionnaire for the members (here, employees) of the organization (here, the company). Here, the item information has "question No.", "type", "factor", "item", "question: expectation", and "question: satisfaction". The "question No." is an ID that identifies the question and is an example of an item identifier. The "type" is information indicating the type of the item, and here, either a comprehensive item or an individual item can be taken. "Factor" is a medium concept of items and can be said to be an object. The "item" is information indicating the content of the item. It should be noted that the "item" may be considered as an item identifier. "Question: Expectation" is a question for acquiring expectation information. "Question: Satisfaction" is a question for obtaining satisfaction information.

Further, the organization response information storage unit 112 stores, for example, the organization response information shown in FIG. 7. Two or more organization response information is stored in the organization response information storage unit 112. FIG. 7 is the organization response information of the organization identified by the organization identifier “Company A”. Further, 701 is member response information of one employee of the organization identified by the organization identifier "Company A". The organization response information of the organization identified by the organization identifier "Company A" includes the member response information of two or more employees. The member response information has a large number (63 or more) of records having "item identifier", "expectation degree information", and "satisfaction degree information". The records of the items having the item identifiers 1 to 4 are the records of the comprehensive items and do not have the expectation degree information. Then, the expectation degree information and the satisfaction degree information constituting the member answer information of 701 were performed by the employee committee for "question: expectation degree" and "question: satisfaction degree" in the item information management table shown in FIG. Information obtained from the answer. And such an answer is here an answer by any of natural numbers from 1 to 5. Further, here, when the expectation degree information for "question: expectation degree" is 1, the expectation degree is the lowest, and when it is 5, the expectation degree is the highest. Further, when the satisfaction information for "question: satisfaction" is 1, the satisfaction is the lowest, and when it is 5, the satisfaction is the highest. Further, it is assumed that the attribute value of the industry of the organization identified by the organization identifier "Company A" is "manufacturer".

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

In such a situation, it is assumed that the administrator of the information processing apparatus 1 has input an instruction for calculating a score or the like. Then, the reception unit 12 receives an instruction for calculating the score and the like. Next, the processing unit 13 determines that it is the timing for calculating the score and the like.

Next, the processing unit 13 acquires two or more organization response information and the like paired with the attribute value “maker” from the organization response information management table (FIG. 7).

Next, the processing unit 13 uses the organization response information paired with the organization identifier for the organization identifier paired with the attribute value "manufacturer", including "Company A", to obtain the item score of each item and Calculate the total score. Hereinafter, the calculation of the item score and the total score will be described by taking "Company A" as an example.

That is, the item score acquisition unit 131 acquires the member response information paired with the organization identifier “Company A”. Then, the item score acquisition unit 131 acquires satisfaction information of all the members for each individual item from the acquired member answer information. Next, the item score acquisition unit 131 acquires the average value of the acquired satisfaction information as the statistical satisfaction information of each individual item. Further, the item score acquisition unit 131 acquires the average value of the acquired expectation degree information as the statistical expectation degree information of each individual item. Next, the item score acquisition unit 131 applies the statistical satisfaction information and the statistical expectation information to the individual score table of FIG. 8 and acquires the item score of each individual item. Then, the item score acquisition unit 131 stores the acquired item score in the buffer in combination with the item identifier of each individual item.

Next, the item score acquisition unit 131 acquires satisfaction information of all the members corresponding to the item identifiers 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 the statistical satisfaction information. Next, the item score acquisition unit 131 stores the statistical satisfaction information of each comprehensive item in the buffer as a pair with the item identifier of each comprehensive item.

Next, the total score acquisition unit 132 acquires the total score of all the item scores of all the individual items. Here, the total score acquisition unit 132 acquires the average value of all item scores of all individual items.

Next, the comprehensive score acquisition unit 132 statistically processes the statistical satisfaction information of all the items and calculates the statistical satisfaction information of the comprehensive items. Here, the comprehensive score acquisition unit 132 acquires the average value of the statistical satisfaction information of all the items as the statistical satisfaction information of the comprehensive items.

Next, the total score acquisition unit 132 calculates the provisional total score by the calculation formula “provisional total score = 0.7 × statistical satisfaction information of comprehensive items + 0.3 × total score of individual items”.

Next, the comprehensive score acquisition unit 132 correlates with the set of satisfaction information and the set of expectation information from the set of satisfaction information of all individual items and the set of expectation information of all individual items. Get information.

Next, the total score acquisition unit 132 adjusts the acquired provisional total score using the acquired correlation information. Further, the total score acquisition unit 132 acquires the deviation value by using the adjusted value of the provisional total score of another company. The deviation value is the total score. Here, it is assumed that the total score acquisition unit 132 calculates the total score of the organization identified by the organization identifier "Company A" as "68.0". Then, the total score acquisition unit 132 stores the total score "68.0" in the buffer in association with the organization identifier "Company A". The calculation of the value adjusted for the provisional total score of other companies is performed in the same manner as the calculation of the value of "Company A".

Then, the above processing is also performed for other organization response information paired with the attribute value "manufacturer". Then, it is assumed that the item score and the total score of each individual item are calculated for all the organization response information (organization) paired with the attribute value "manufacturer", and at least temporarily stored in the buffer.

Next, the influence degree acquisition unit 133 acquires the item score paired with the organization identifier paired with the attribute value "manufacturer" from the buffer for each individual item. That is, the influence degree acquisition unit 133 acquires the item score from the buffer by the number of the organization identifiers. Next, the influence degree acquisition unit 133 acquires the total score paired with the organization identifier paired with the attribute value “maker” from the buffer.

Next, the influence degree acquisition unit 133 calculates the degree of correlation between the set of the acquired plurality of item scores and the set of the acquired plurality of total scores for each individual item. The degree of correlation may be a correlation coefficient. Then, the influence degree acquisition unit 133 stores the influence degree, which is the degree of correlation, in the buffer for each individual item as a pair with the item identifier. It should be noted that such information has a structure of "item identifier, degree of influence", and here, for example, "influence within the industry, 0.84", "stability of customer base, 0.78", and "topic". And name recognition, 0.65 "" Soundness of financial situation, 0.86 "" Dissemination and transmission of philosophy, 0.50 "" On-site penetration of philosophy, 0.82 "" Dissemination and transmission of strategic goals, 0. It is assumed that it was "78", "a sense of conviction for the strategic goal, 0.50", etc.

In addition, the above processing is also performed for the organization response information corresponding to other attribute values (for example, "trading company", "bank", etc.).

Then, it is assumed that the item score and the total score are accumulated for each company, and here, the degree of influence for each item is accumulated for each attribute value of the organization.

In such a situation, it is assumed that the user of "Company A" inputs an output instruction such as a score to the terminal device 2 to the terminal device 2. Next, the terminal device 2 receives an output instruction such as a score, and constitutes an output instruction such as a score having the organization identifier "Company A". Then, the terminal device 2 transmits the output instruction such as a score to the information processing device 1.

Next, the reception unit 12 of the information processing device 1 receives an output instruction such as a score from the terminal device 2. Next, the processing unit 13 acquires the organization identifier "Company A" possessed by the output instruction such as a score. Next, the processing unit 13 acquires the attribute value "manufacturer" of the organization paired with the organization identifier "Company A".

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

Next, the processing unit 13 acquires the total score “68.0” paired with the organization identifier “Company A” from the storage unit 11 or a buffer (not shown).

Next, the processing unit 13 acquires two or more influence degrees paired with the acquired attribute value “maker” and an item identifier paired with the impact degree from the storage unit 11 or a buffer (not shown).

Next, the processing unit 13 configures the information to be output from the acquired information. Then, the output unit 14 outputs the configured information. An example of such an output is shown in FIG. 901 in FIG. 9 is the total score "68.0" of company A. Further, 902 is an item score of each comprehensive item. Here, the comprehensive items are company, boss, work, and workplace. Further, 903 is an item score and an influence degree of individual items. FIG. 10 is an enlarged view of the area of 903. In FIG. 10, 1001 is an average value of expectation information of individual items. 1002 is an average value of satisfaction information of individual items. The column of 1003 is the degree of influence of each individual item. As can be seen from 1004 to 1006 in FIG. 10, the influence degree output unit 143 outputs the influence degree in association with each item so that the classification of the influence degree can be visually distinguished. That is, here, for example, when the influence degree is 0.8 or more, the darkest background color is adopted, and when the influence degree is 0.6 or more and less than 0.8, the normal dark background color is adopted. By adopting a light background color when the degree of influence is less than 0.6, the classification of the degree of influence is output so as to be visually distinguishable. Needless to say, the method of outputting the classification of the degree of influence so that it can be visually distinguished is not limited to the background color, and it is conceivable to change various attribute values such as font and size.

As described above, according to the present embodiment, it is possible to easily grasp the important items for the organization.

Further, according to the present embodiment, the degree of influence can be acquired for each attribute value of the organization, and important items can be grasped with high accuracy.

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

In addition, according to the specific example of this embodiment, the degree of influence was acquired for each attribute value of the organization. However, the degree of influence for each item may be acquired regardless of the attribute value of the organization. The technology for acquiring the degree of influence for each attribute value of an organization is a technology that focuses on the fact that the characteristics of a company differ for each attribute value of an organization. And, such a technology is extremely useful technology in engagement, for example, in which employees voluntarily try to exert their own power toward achieving the goals of the organization, and both the employees and the organization grow.

In addition, the above-mentioned "satisfaction" of the questionnaire item includes similar terms such as conviction and current situation, and "expectation" includes similar terms such as expectation, importance and importance. Needless to say.

Further, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded on a recording medium such as a CD-ROM and disseminated. It should be noted that this also applies to other embodiments herein. The software that realizes the information processing device 1 in this embodiment is the following program. That is, this program organizes, for example, two or more item response information having an item identifier that identifies an item relating to the organization and response information including response information that is a response relating to the degree of satisfaction of the members with respect to the item. A computer capable of accessing a recording medium provided with an organization response information storage unit in which the organization response information including the organization response information for each of the two or more members belonging to the above two or more organizations is stored is the above-mentioned two or more. The item score acquisition unit that statistically processes the response information for the members of two or more items included in each organization response information and acquires the item score, which is the score of the item, for each organization and each item, and the above two or more. For each organization's response information, the total score acquisition unit that acquires the total score, which is the total score for each organization, using the two or more item scores, and the two or more of each of the two or more organizations. Using the item score of each item and the total score of each of the above two or more organizations, the influence degree acquisition unit that acquires the degree of influence of the item score of each item on the total score for each item, and the influence degree acquisition unit. This is a program for functioning as an influence degree output unit that outputs the influence degree acquired by the unit in association with each item. It can be said that such a program is a program that realizes an engagement system.

(Embodiment 2)
In the present embodiment, an information system including a score prediction device for predicting a score will be described using a correspondence relationship and daily information of a period for which the score is predicted. The correspondence relationship is a correspondence relationship between the past score (for example, engagement score) based on the response of the questionnaire to the members of the organization and the daily information corresponding to the period corresponding to the questionnaire. Further, the daily information is information about the members, and the details will be described later.

Further, in the present embodiment, an information system including a score prediction device for predicting a score by using a machine learning algorithm will be described.

Further, in the present embodiment, an information system including a score prediction device for predicting a score will be described using a correspondence table acquired by data mining.

Further, in the present embodiment, an information system including a score prediction device for predicting a score will be described using an arithmetic expression.

FIG. 11 is a conceptual diagram of the information system B according to the present embodiment. The information system B includes a score prediction device 3 and one or more terminal devices 4. The score prediction device 3 is a device for acquiring a score of an organization. Here, the score is, for example, an engagement score. The score is, for example, a score regarding the motivation of the members of the organization. The score is, for example, a score regarding the evaluation of a member's organization. The score may be a numerical value, information indicating a level (for example, A, B, C) or the like. Further, the score is usually the total score described above, but may be a score having one item score or a score which is a representative value (for example, an average value or a median value) of two or more item scores.

The score prediction device 3 is a so-called server device here. The score prediction device 3 is, for example, a cloud server or an ASP server, but its type and installation location do not matter. The terminal device 4 is a terminal used by a user who wants to acquire a score. The terminal device 4 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, a so-called personal computer, and the type thereof does not matter.

FIG. 12 is a block diagram of the information system B. Further, FIG. 13 is a block diagram of the score prediction device 3 constituting the 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 organization 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 business input information storage means 3121, communication information storage means 3122, attendance status information storage means 3123, business result information storage means 3124, stress information storage means 3125, measurement information storage means 3126, and image storage means 3127. , Voice storage means 3128, and living information storage means 3129.

The processing unit 33 includes a score acquisition unit 331. The score acquisition unit 331 includes a corresponding acquisition unit 3311 and a score acquisition unit 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 that constitutes the score prediction device 3. The various types of information are, for example, organizational information described later, daily information described later, teacher score described later, correspondence-related information described later, and member information of two or more. The various information of the storage unit 31 may be information received from the terminal device 4, information received from a server (not shown), or information acquired and accumulated by the score prediction device 3.

The member information is information about the member. A member has a member identifier that identifies the member. Members have one or more member attribute values. The member attribute value is a member attribute value. Each member attribute value of 1 or more is, for example, a name, an age, a job title, a year of joining the company, an e-mail address, a telephone number, and a member identifier of a boss. Further, it is assumed that the member information such as the management team, the manager, and the boss is also stored in the storage unit 31. Further, the two or more member information may be stored in a member information storage unit (not shown).

The organization information storage unit 311 stores one or more organization information. Organizational information is information about an organization. An organization is, for example, an organization that does some work. Organizations are, for example, so-called companies, sole proprietorships, local public bodies, etc., and are widely understood. The organization may be an organization within a company. That is, the organization may be, for example, a department, a business establishment, a section, a section, or the like in 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 the organization. The organization identifier is, for example, an organization name, an ID, or the like. The organization attribute value is an organization attribute value. The organization attribute value is, for example, an industry identifier indicating the type of industry of the organization (for example, bank, apparel, manufacturer, etc.), a scale identifier for classifying the size of the organization (for example, large company, small and medium-sized enterprise, individual business, etc.), and the area of the head office. Area identifier indicating, number of employees, classification based on the number of employees (for example, 5 stages from 1 to 5), number of business establishments, classification based on the number of business establishments (for example, 3 stages of "many", "medium", and "small") And so on.

The daily information storage unit 312 stores one or more daily information. Daily information is information about members. Daily information is information that can be acquired in the daily life of the members. Daily information does not include answers to questions such as questionnaires. The daily information is, for example, a file, but may be a database record or the like. The data structure of daily information does not matter. A member is a person who belongs to an organization. Members are usually those who work for the organization. The members are, for example, employees, part-time workers, part-time workers, and the like. Daily information corresponds to, for example, an organization identifier. It is preferable that the daily information corresponds to the member identifier. Daily information usually corresponds to time information. Time information is information that identifies the time corresponding to daily information. The time information is, for example, information for specifying the time when the daily information is acquired, and information for specifying the time when the daily information is measured. The time information is usually information that specifies a temporary point, but may be period information that specifies a period. The time information is, for example, information for specifying the date and time, information for specifying the day, and information for specifying the month.

It should be noted that the fact that one piece of information corresponds to another piece of information may mean that one piece of information includes other pieces of information, or that one piece of information and another piece of information are linked to each other. May include one piece of information. That is, it suffices if another information can be obtained from one piece of information, or one piece of information can be obtained from another piece of information.

The daily information is, for example, business information and health information. Business information is information about the business performed by the members. Health information is information about the health of members.

The business information is, for example, input information, attendance status information, and business result information. The input information is the information input by the member. The input information is, for example, information input by a member to a device (for example, a personal computer, a tablet terminal, a server, etc.) used in business. The input information is, for example, business input information and communication information. Details of business input information and communication information will be described later. The details of attendance status information and work result information will also be described later.

Health information is information about the health of members. The health information is, for example, biological information and living information. Biological information is information about the living body of a member. The biological information is, for example, stress information and measurement information. Details of stress information and measurement information will also be described later. The details of living information will also be described later.

The daily information used to acquire the correspondence is called the teacher daily information. The daily information used for the score prediction process is called the predicted daily information.

The teacher daily information is, for example, teacher business information and teacher health information. The teacher work information is, for example, teacher input information, teacher attendance status information, and teacher work result information. The teacher input information is, for example, teacher business input information and teacher communication information. The teacher health information is, for example, teacher biometric information and teacher life information. The teacher biometric information is, for example, teacher stress information and teacher measurement information.

The predicted daily information is, for example, predicted work information and predicted health information. The forecast business information is, for example, forecast input information, forecast attendance status information, and forecast business result information. The predictive input information is, for example, predictive business input information and predictive communication information. The predicted health information is, for example, predicted biological information and predicted living information. The predicted biological information is, for example, predicted stress information and predicted measurement information.

One or two or more business input information is stored in the business input information storage means 3121 constituting the daily information storage unit 312. The business input information is information related to the input for the performance of the member's own business.

The business input information is, for example, an operation log of a device used by a member for business, a program created by the member, a document created by the member, and a daily report created by the member. Business input information corresponds to time information. The business input information corresponds to, for example, an organization identifier and a member identifier. The business input information may be, for example, information that can be acquired from an operation log, a program, a document, a daily report, or the like. The information that can be acquired from the operation log is, for example, an operation log attribute value. The operation log attribute value is, for example, an operation amount indicating the amount of the operation log, the number of types acquired by using the operation log, the average value of the 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, whether the number of operation logs per day is increasing, whether the number of operation logs per day is decreasing, and the number of operation logs per day. Information (for example, variance) indicating the variation of. The information that can be obtained from the program is, for example, a program attribute value. The program attribute value is, for example, the number of steps of the program, the average value of the number of steps of the program in one day, and the change information of the number of steps of the program created in one day. The change information of the number of program steps is, for example, whether or not the number of steps of the daily program is increasing, whether or not the number of steps of the daily program is decreasing, and the number of steps of the daily program. Information (for example, variance) indicating the variation of. The information that can be obtained from a document is, for example, a document attribute value. The document attribute value is, for example, the data amount of the document, the average value of the data amount of the document in one day, and the change information of the data amount of the document created in one day. The change information of the document data amount is, for example, whether the daily document data amount is increasing, whether the daily document data amount is decreasing, and the daily document data amount. Information (for example, dispersion) indicating the variation of. The information that can be obtained from the daily report is, for example, the daily report attribute value. The daily report attribute values are the amount of data in the daily report, the average value of the amount of data in the daily report, the change information in the amount of data, the number of negative words appearing in the daily report for a predetermined period, the appearance rate of negative words in the daily report for a predetermined period, The number of positive words appearing in the daily report for a predetermined period and the appearance rate of positive words in the daily report for a predetermined period. The data amount change information is, for example, whether or not the daily report data amount is increasing, whether or not the daily daily report data amount is decreasing, and the daily daily report data amount. Information indicating variability (eg, dispersion). 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 the business input information.

The business input information of 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).

One or more communication information is stored in the communication information storage means 3122. Communication information corresponds to time information. The communication information corresponds to, for example, an organization identifier and a member identifier. The time information here is information that specifies when the communication information is input, transmitted, or received. Further, the organization identifier here is an identifier of the organization to which the member who input the communication information belongs. Further, the member identifier here is an identifier of the member who input the communication information. The communication information corresponds to, for example, a member identifier of one or more members with whom the communication is made.

Communication information is information about communication between two or more members. Communication information includes, for example, chat information input by a member into a chat system (or a chat application), SNS information input by a member into an SNS, e-mail created by a member, and voice emitted by a member in a video conference. This is textualized information. The technique of converting voice into text by voice recognition processing is a known technique, and the description thereof will be omitted.

The communication information may include communication attribute values that can be obtained from chat information, SNS information, e-mail, or textualized information. The communication attribute values are the amount of communication information data for a predetermined period, the number of negative words appearing in the communication information for a predetermined period, the appearance rate of negative words in the communication information for a predetermined period, and the positive words in the communication information for a predetermined period. The number of appearances, the appearance rate of positive words in communication information for a predetermined period, and the response rate of members to messages from management, managers, and superiors. The predetermined period is a predetermined period.

The communication information of 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 attendance status information. Attendance status information is information related to attendance status. The attendance status information may be attendance information or information that can be obtained from one or more attendance information. The attendance information has a working day, a working start time, and a working end time. Attendance information includes, for example, information indicating the day of annual leave. Attendance information includes, for example, information indicating holiday commuting. Attendance information includes, for example, information on normal working hours. Attendance information includes, for example, information that identifies a normal work day.

The attendance status information is, for example, the number of annual leave of the member, the number of overtime hours of the member, and the number of holiday work of the member. The attendance status information corresponds to, for example, an organization identifier and a member identifier.

The attendance status information of 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 result of business execution. The business result information is, for example, a team goal achievement rate, an individual goal achievement rate, the number of sales, and sales. The business result information usually corresponds to the organization identifier and the period information.

The business result information of 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 stress information. The stress information is information about the stress of the members. The stress information is information indicating the degree of stress and the presence or absence of stress. The stress information is, for example, the result of a stress check. The stress information is, for example, a response to a questionnaire to members (for example, "Please select your stress level from 1 to 5"). The tress information is, for example, information acquired by using other health information of the member (for example, heart rate, blood pressure), and information acquired by using the answers to two or more questions of the questionnaire to the member. The stress information corresponds to, for example, an organization identifier and a member identifier.

The stress information of 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 two or more measurement information. The measurement information is the measurement result of the living body of the member. The measured information is, for example, heart rate and blood pressure. The measurement information is, for example, (a) information acquired by using a wearable terminal worn by a member or a measuring device (not shown) and transmitted to the score prediction device 3, and (b) acquired by the member. Information acquired by a wearable terminal or a measuring device (not shown) and input by a member. The measurement information corresponds to, 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 a terminal device 4, a wearable terminal (not shown), or a server (not shown).

One or two or more images are stored in the image storage means 3127. The image is an image of one or more members. The image is, for example, a recorded image of a video conference in which a member participates. The image corresponds to, for example, an organization identifier and a member identifier.

The image of 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 voice storage means 3128 stores one or more voices. The voice is a voice emitted by a member. The audio is, for example, a recorded image of a video conference in which the members participate. The voice corresponds to, for example, an organization identifier and a member identifier.

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

Living information storage means 3129 stores one or more living information. Living information is information about the lives of members. Living information is, for example, sleep time, whether or not a person has drunk, and the number of cigarettes smoked. The living information is, for example, (a) information input by a member, (b) information automatically acquired by a sleep measuring device (not shown) and transmitted to the score prediction device 3, and (c) automatically acquired by a sleep measuring device (not shown). , Information entered by the members. Living information corresponds to, for example, an organization identifier and a member identifier. Further, the living information corresponds to, for example, time information (for example, date).

The teacher score storage unit 313 stores one or two or more teacher scores. The teacher score is usually the score of an organization obtained using a plurality of organization response information indicating the answers to the questions to each of two or more members of the organization. However, the teacher score may be a score manually input by the user or a predicted score acquired by the score predicting device 3 in the past. The questions are, for example, questionnaire questions and questionnaire questions. The teacher score is, for example, a total score acquired by the information processing apparatus 1 described in the first embodiment. The teacher score may be, for example, any item score acquired by the information processing apparatus 1 described in the first embodiment. In addition, the teacher score is a simple questionnaire (for example, a questionnaire with one question such as "Please select the degree of satisfaction with the company from 1 to 5") from the questionnaire explained in the first embodiment. Representative values of two or more members of the answer (eg, mean, weighted average, median) may be used. That is, the teacher score may be, for example, a score of an organization acquired by using a plurality of organization response information indicating answers to questions to each of two or more members. The teacher score corresponds to, for example, period information and an organization identifier. The period information corresponding to the teacher score is the information of the period subject to the questionnaire (for example, 1 year, 6 months, 3 months). The period information may be information at the time of specifying the time when the questionnaire was conducted. The organization identifier associated with the teacher score is the organization identifier of the score indicated by the teacher score.

One or two or more correspondence informations are stored in the correspondence information storage unit 314. Correspondence information is associated with, for example, an organization identifier. Further, the correspondence-related information corresponds to, for example, time information or period information.

Correspondence information is information about the correspondence between one or more teacher features and the teacher score. The correspondence information is, for example, information that specifies the correspondence between one or more teacher features and the teacher score. Teacher features use one or more teacher daily information, or information obtained using one or more teacher daily information, or one or more teacher daily information and one or more teacher daily information. This is the information that is acquired.

The teacher identity is, for example, data amount information that specifies the data amount of teacher input information, input frequency information that specifies the input frequency of teacher input information, analysis information that shows the analysis result of teacher input information, and timing of input of teacher input information. This is information including one or more types of information among the timing information for specifying the above. The analysis information is, for example, a business 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 expression. The correspondence relationship information is acquired by, for example, the correspondence acquisition means 3311 described later.

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

The correspondence table is a table having two or more correspondence information which is a set of a teacher vector and a teacher score. The structure of the table does not matter.

The arithmetic expression is an arithmetic expression (S = f (a, b, c, ...)) In which each teacher feature of 2 or more is input and the teacher score is output. In addition, a, b, c and the like in the arithmetic expression are teacher features, and S is a 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. The prediction instruction is an instruction to output the prediction score. The prediction instruction is, for example,
Has teacher target conditions. The teacher target condition is information that identifies an organization identifier that is paired with daily information that is the source of teacher data. The teacher target condition is, for example, a condition using an organization identifier and an organization identifier. The prediction instruction has, for example, information that identifies the correspondence information to be used. The predictive instruction has, for example, teacher period information that specifies the period of daily information that is the source of teacher data.

Further, here, reception is usually reception from the terminal device 4, but reception of information input from an input device such as a keyboard, mouse, or touch panel, or reception from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory. It may be regarded as a concept including acceptance of read information.

The processing unit 33 performs various processes. The various processes are, for example, processes performed by the score acquisition unit 331, the corresponding acquisition means 3311, and the score acquisition means 3312.

The processing unit 33 stores, for example, the daily information received by the reception unit 32 in the daily information storage unit 312. For example, the processing unit 33 associates the daily information received by the reception unit 32 with the member identifier of the member corresponding to the daily information, and stores the daily information in the daily information storage unit 312. The configuration identifier is, for example, an identifier of a member who has input or transmitted daily information, or a member identifier corresponding to daily information.

The score acquisition unit 331 acquires a predicted score by using the correspondence between the teacher score and one or more teacher daily information and one or more predicted daily information. The predicted score is a score corresponding to one or more predicted daily information.

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

The score acquisition unit 331 may acquire the features as follows, for example. A feature is a teacher feature used to obtain correspondence information, or a predictive feature used to obtain a predictive score. The process of acquiring the features by the score acquisition unit 331 described below is performed by the corresponding acquisition means 3311 when the features are teacher features, and by the score acquisition means 3312 when the features are predictive features. Further, the identity is, for example, the daily information or the attribute value of the daily information that can be acquired from the daily information (for example, the operation log attribute value, the program attribute value, the document attribute value, the daily report attribute value, etc.). In addition, a teacher vector is composed of two or more teacher features. A prediction vector is composed of two or more prediction features. It does not matter which of the information acquired by the score acquisition unit 331, which will be described below, is used as the feature. The score acquisition unit 331 may use any of two or more features to acquire the predicted score in order to acquire the correspondence information.

For example, the score acquisition unit 331 pairs with each member identifier for each member, and acquires an operation log corresponding to the time information of each day of the period to be processed from the business input information storage means 3121. One or more types of operation log attribute values are acquired from the operation log, and the one or more types of operation log attribute values are stored at least temporarily in the business input information storage means 3121. Since the technique for acquiring the business input attribute value such as the amount of information data and the above-mentioned change information from the business input information such as the operation log is a known technique, detailed description thereof will be omitted. Further, the operation log is, for example, the operation history of the configuration such as login / logout date / time of a PC operating in the organization, viewing / editing of various files, network connection, and the like.

For example, the score acquisition unit 331 acquires a program corresponding to the time information of each day of the period to be processed, which is paired with each member identifier for each member, from the business input information storage means 3121, and the said program is obtained. One or two or more types of program attribute values are acquired from the program, and the one or more types of program attribute values are at least temporarily stored in the business input information storage means 3121.

The score acquisition unit 331 acquires, for example, the number of steps of one or more program files for each date information corresponding to the program file (for each created date). Then, in the score acquisition unit 331, for example, the average value of the number of steps in the program file for each day, the information indicating the variation in the number of steps created in each day in the target period, and the number of steps in the program created in one day tend to increase. The change information indicating whether the value is decreasing or the tendency is decreasing is acquired.

For example, the score acquisition unit 331 obtains a document corresponding to the time information of each day of the period to be processed from the business input information storage means 3121, which is paired with each member identifier for each member. One or two or more types of document attribute values are acquired from a document, and the one or more types of document attribute values are at least temporarily stored in the business input information storage means 3121. A document is a text of a direct product of the performance of a job. Documents are, for example, design documents in the technical department and accounting information in the accounting department.

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

For example, the score acquisition unit 331 obtains a daily report corresponding to the time information of each day of the period to be processed from the business input information storage means 3121, which is paired with each member identifier for each member. One or more types of daily report attribute values are acquired from the daily report, and the one or more types of daily report attribute values are stored at least temporarily in the business input information storage means 3121.

The score acquisition unit 331 acquires, for example, the amount of data of one or more acquired daily reports. Then, the score acquisition unit 331 acquires, for example, the average value of the data amount, the information indicating the variation in the data amount, and the change information indicating whether the data amount is increasing or decreasing. Further, the score acquisition unit 331 morphologically analyzes one or more acquired daily reports, refers to a dictionary of negative words, and acquires the number of occurrences of negative words. Further, the score acquisition unit 331 refers to the dictionary of positive words from the result of morphological analysis of one or more acquired daily reports, and acquires the number of appearances of the positive words. Further, the score acquisition unit 331 acquires, for example, the number of acquired independent words in the daily report, the appearance rate of negative words, which is the number of negative words in the number of the independent words, and the positive in the number of the independent words. Get the appearance rate of positive words, which is the number of words.

For example, the score acquisition unit 331 pairs with each member identifier for each member, and acquires communication information corresponding to the time information of each day of the period to be processed from the communication information storage means 3122, for example. , Acquire one or more types of communication attribute values of the communication information. The score acquisition unit 331 acquires, for example, the amount of acquired communication information data. Further, the score acquisition unit 331, for example, morphologically analyzes the communication information, acquires one or more independent words, and is the number of words existing in the negative word dictionary (not shown) among the one or more independent words. Get the number of negative words. Further, the score acquisition unit 331, for example, morphologically analyzes the communication information, acquires one or more independent words, and is the number of words existing in the positive word dictionary (not shown) among the one or more independent words. Get the number of positive words. Further, the score acquisition unit 331 morphologically analyzes the communication information, and acquires the ratio of the number of negative words and the ratio of the number of positive words in the acquired number of independent words.

For example, the score acquisition unit 331 acquires a transmitted e-mail corresponding to the time information of each day of the period to be processed by pairing with each member identifier for each member, and quoting the e-mail. The original e-mail is acquired, and it is determined whether or not the identifier of the sender of the e-mail of the citation source (for example, an e-mail address) is stored in the organization information storage unit 311 in association with the member identifier. However, if it is stored, the counter of the reply to the message from the reply target person (management team, manager, or boss, etc.) is incremented by 1. In addition, the score acquisition unit 331 is paired with each member identifier for each member, and is received from the reply target person in the received e-mail corresponding to the time information of each day of the period to be processed. Get the number of emails. More specifically, for example, the score acquisition unit 331 acquires, for example, the member identifier of the boss, the member identifier of the management team, and the member identifier of the manager who are paired with the member identifier, and each of them. The e-mail address of the member identifier is acquired from the set of member information, and the number of e-mails to which the e-mail address is the sender (the number of e-mails from the reply target person) is acquired. Then, the score acquisition unit 331 calculates, for example, the response rate of the member to the message from the response target person from the number of e-mails from the response target person and the value of the response counter for each member. It should be noted that, for example, it is assumed that the storage unit 31 stores the identifier (for example, an e-mail address) of the response target person of each member so as to correspond to each member identifier.

The score acquisition unit 331, for example, is paired 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, for example, the attendance status information (for example, the number of annual leave, the number of overtime hours, the number of holiday commuting) for the period to be processed for each member. It should be noted that the technique of acquiring the number of annual leave, the number of overtime hours, the number of holiday work, etc. from the set of attendance information is a publicly known technique.

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

The score acquisition unit 331 is paired with each member identifier for each member, and reads out one or more answers to the questionnaire corresponding to the time information of the period to be processed from the storage unit 31. Next, the score acquisition unit 331 acquires stress information from one or more answers for each member, for example. The content of the questionnaire and the algorithm for acquiring stress information do not matter. The score acquisition unit 331 acquires, for example, an answer to a question inquiring about the degree of stress. The score acquisition unit 331, for example, substitutes the answers of two or more questions regarding the stress degree into the calculation formula for calculating the stress degree, and calculates the stress degree. The score acquisition unit 331, for example, constructs a vector from the answers of two or more questions regarding the stress degree, and acquires the stress degree paired with the vector closest to the vector. In such a case, it is assumed that two or more correspondence informations, which are pairs of the vector and the stress degree, are stored in the storage unit 31.

The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires the heart rate and / or blood pressure corresponding to the time information of the period to be processed from the measurement information storage means 3126. Next, the score acquisition unit 331 acquires stress information indicating higher stress as the heart rate and / or blood pressure value increases for each member, for example. The score acquisition unit 331 acquires, for example, the number of times the heart rate and / and the blood pressure value is larger than the threshold value, and acquires the stress degree based on the number of times. The degree of stress based on the number of times increases as the number of times increases. Further, the score acquisition unit 331 acquires, for example, a continuous time in which the heart rate and / and the blood pressure value is larger than the threshold value, and acquires the stress degree based on the sum of the continuous times. The degree of stress based on the sum of the continuous times increases as the sum of the continuous times increases.

The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires one or more measurement information corresponding to the time information of the period to be processed from the measurement information storage means 3126. The score acquisition unit 331 acquires a representative value (for example, an average value and a median value) of the measurement information for each type of measurement information, for example.

The score acquisition unit 331, for example, for each member, pairs with each member identifier and acquires one or more images of the period to be processed from the image storage means 3127. Next, the score acquisition unit 331 performs facial expression analysis processing on each of the one or more images, and acquires a facial expression identifier (for example, any of emotions and sorrows, normality). Further, the score acquisition unit 331 performs statistical processing on the acquired one or more facial expression identifiers and the like, and acquires the ratio of negative facial expressions (angry, crying, etc.). In addition, such a ratio is an example of a feature. Further, a process of performing a facial expression analysis process and acquiring a facial expression identifier is a known technique.

For example, the score acquisition unit 331 pairs with each member identifier for each member, and acquires one or more voices of the period to be processed from the voice storage means 3128. Next, the score acquisition unit 331 acquires one or more types of feature quantities (for example, spectral information, utterance section, silent section, etc.) from each of the one or more voices, and uses the feature quantities to obtain voice. Get the identity. The score acquisition unit 331 acquires, for example, the ratio of the utterance time in the attended conference and the emotion identifier (laughing, normal, angry, crying, etc.) using the feature amount. The technique for acquiring the feature amount is a known technique such as LPC analysis and cepstrum analysis.

The score acquisition unit 331 is paired with each member identifier for each member, and reads out the living information of the period to be processed from the living information storage means 3129. Then, the score acquisition unit 331 statistically processes the living information for each member, and acquires the features of the living information. The features of living information are, for example, the average value of sleeping hours, the dispersion of sleeping hours, the number of drinking days, the ratio of drinking days, and the average number of cigarettes smoked per day.

The score acquisition unit 331 may acquire correspondence information by using the acquired teacher vector having two or more teacher features and the two or more teacher data having the teacher score. The teacher vector is information acquired by the score acquisition unit 331 by the above-mentioned processing. Further, the correspondence information may be stored in advance in the correspondence information storage unit 314.

The score acquisition unit 331, for example, gives a learning model, which is correspondence information, and a prediction vector, which is a vector whose elements are two or more prediction factors, to a machine learning module, and executes the prediction processing by executing the module. To get the predicted score. The predictive feature is information that can be obtained from the predicted daily information or the predicted daily information. Further, the prediction vector is information acquired by the score acquisition unit 331 by the above-mentioned processing. The machine learning module may be, for example, Tiny_SVM, TensorFlow function, MicrosoftML function, or the like.

The score acquisition unit 331 acquires a prediction score by using, for example, a correspondence table which is correspondence information and a prediction vector whose elements are two or more prediction features. More specifically, the score acquisition unit 331 searches, for example, a teacher vector that most closely resembles a prediction vector having two or more prediction features as elements from the correspondence table, and obtains a teacher score that is paired with the teacher vector. Get as a predicted score.

Further, the score acquisition unit 331 determines from the correspondence table two or more teacher vectors whose distances from the prediction vectors having two or more prediction features as elements are close enough to satisfy a predetermined condition, and the two or more teacher vectors. Get the representative value of the teacher score corresponding to each teacher vector. The representative values are, for example, an average value, a median value, and a weighted average. The weighted average is obtained, for example, by calculating a weight that is inversely proportional to the distance between the vectors and using the weight. The predetermined conditions are, for example, within the threshold value or smaller than the threshold value, and the closeness of the distance is within the upper N (N is a natural number of 2 or more).

Further, the score acquisition unit 331 substitutes, for example, each of two or more predictive features into the calculation formula, executes the calculation formula, and calculates the prediction score. The calculation formula is stored in the correspondence information storage unit 314.

The score acquisition unit 331 acquires a predicted score by using, for example, a correspondence relationship between one or more teacher daily information including teacher business information and a teacher score, and one or more predicted daily information including predicted business information. It should be noted that using the correspondence relationship is usually to use the above-mentioned correspondence relationship information.

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

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

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

The score acquisition unit 331 is, for example, data amount information for specifying the data amount of the teacher input information, input frequency information for specifying the input frequency of the teacher input information, analysis information showing the analysis result of the teacher input information, and input of the teacher input information. One or more types of information of the timing information that specifies the timing of the prediction input information is acquired, and the data amount information of the prediction input information, the input frequency information of the prediction input information, the analysis information of the prediction input information, and the timing information of the prediction input information Acquire one or more types of information, acquire correspondence information that identifies the correspondence between one or more teacher identities and teacher scores, including one or more types of information acquired from teacher input information, and obtain the correspondence information. , And a prediction vector having two or more predictive qualities including one or more types of information obtained from the prediction input information is used to obtain a prediction score.

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

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

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

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

The score acquisition unit 331 acquires a predicted score by using, for example, a correspondence relationship between one or more teacher daily information including teacher stress information and a teacher score, and one or more predicted daily information including predicted stress information.

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

The score acquisition unit 331 acquires a predicted score by using, for example, a correspondence relationship between one or more teacher daily information including teacher biological characteristic information and a teacher score, and one or more predicted daily information including predicted biological characteristic information. ..

The score acquisition unit 331 acquires a predicted score by using, for example, a correspondence relationship between one or more teacher daily information including teacher life information and a teacher score, and one or more predicted daily information including predicted life information.

For example, the score acquisition unit 331 reads the past score corresponding to the acquired predicted score from the storage unit 31, and uses the past score and the acquired predicted score to obtain score fluctuation information regarding an increase or decrease in the score. You may get it. The score fluctuation information is, for example, "predicted score-past score", "past score-predicted score", "information indicating whether the score increases or decreases", "information indicating the degree of increase or decrease in the score". Is.

The correspondence acquisition means 3311 constituting the score acquisition unit 331 acquires the correspondence information that specifies the correspondence between one or more teacher daily information and the teacher score.

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

Hereinafter, a processing example in which the correspondence acquisition means 3311 acquires each of the learning model, the correspondence table, and the arithmetic expression will be described.
(1) When acquiring a learning model

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

More specifically, the correspondence acquisition means 3311 acquires, for example, a set of two or more teacher daily information for acquiring a learning model from the daily information storage unit 312. The set of two or more teacher daily information is, for example, a set of teacher daily information of different organizations. The set of two or more teacher daily information is, for example, a set of teacher daily information having different periods.

Next, for each set of teacher daily information, the correspondence acquisition means 3311 acquires two or more teacher features using the acquired one or two or more teacher daily information, and obtains each of the two or more teacher features. Get the teacher vector as an element. Further, the correspondence acquisition means 3311 acquires, for example, a teacher score corresponding to a set of teacher daily information to be processed from the teacher score storage unit 313. It should be noted that, for example, the teacher score corresponds to the organization identifier or / and the 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 obtains two or more teacher data. In the teacher data, it may be said that each teacher feature of the teacher vector is an explanatory variable and the teacher score is an objective variable. The teacher feature is information obtained from the teacher's daily information or the teacher's daily information.

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

It should be noted that this learning model is a learning model that inputs a prediction vector, which is information acquired from the predicted daily information and / or the predicted daily information, and outputs a predicted score. Further, as the learning processing algorithm of machine learning, algorithms such as deep learning, decision tree, random forest, SVR, and SVM can be used. In other words, the algorithm of the learning process of machine learning does not matter. Further, the module that performs the learning process of machine learning may be, for example, Tiny_SVM, TensorFlow function, MicrosoftML function, or the like.
(2) When acquiring the correspondence table

The correspondence acquisition means 3311 acquires, for example, two or more teacher data having a teacher vector and a teacher score, as in the process described in (1). Next, the correspondence acquisition means 3311 constitutes, for example, a correspondence table having two or more correspondence information having a teacher vector and a teacher score, and the correspondence table is stored at least temporarily in the correspondence information storage unit 314.
(3) When acquiring an arithmetic expression

The correspondence acquisition means 3311 acquires, for example, two or more teacher data having a teacher vector and a teacher score, as in the process described in (1). Next, the correspondence acquisition means 3311 acquires an arithmetic expression in which two or more teacher vectors are input and the teacher score corresponding to each teacher vector is output.

The correspondence acquisition means 3311 is, for example, an arithmetic expression element (for example, “score S = ax ₁ + bx ₂ + cx ₃ + ... + (n-1) x _n + n (a, b, c. ... n is a parameter, x ₁ , x ₂ , x ₃ , ... x _n is a variable) ”) is read from the storage unit 31. Next, the correspondence acquisition means 3311 substitutes, for example, two or more teacher data into the variable of the arithmetic expression source, and acquires the parameter for which each teacher score is obtained by multiple regression analysis, multivariate analysis, or the like. Then, the correspondence acquisition means 3311 at least temporarily stores the acquired arithmetic expression in the correspondence information storage unit 314.

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

The score acquisition means 3312 acquires, for example, correspondence information corresponding to the organizational condition that the organization to be predicted matches from the correspondence information storage unit 314, and uses the correspondence information and one or more prediction daily information. , Get the predicted score.

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

Hereinafter, a processing example in which the score acquisition means 3312 acquires a predicted score by using any of the three types of correspondence information will be described.
(1) When using a learning model

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

More specifically, the score acquisition means 3312 acquires a prediction vector using one or more prediction 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 gives a prediction vector and a learning model to a module that performs prediction processing of machine learning, executes the module, and acquires a prediction score. As the prediction processing algorithm for machine learning, algorithms such as deep learning, decision tree, random forest, and SVR can be used. In other words, the algorithm of the prediction processing of machine learning does not matter. Further, the module that performs the prediction processing of machine learning does not matter as described above.
(2) When using a correspondence table

The score acquisition means 3312 acquires a prediction vector, which is a vector having two or more prediction features, using, for example, one or more prediction daily information. Next, the score acquisition means 3312 applies the prediction vector to the correspondence table and acquires the prediction score.

More specifically, the score acquisition means 3312 acquires a prediction vector by using, for example, one or more prediction daily information. Next, the score acquisition means 3312 searches, for example, a vector that most closely matches the predicted vector from the correspondence table, and acquires a teacher score paired with the vector as a predicted score.

Further, the score acquisition means 3312 determines, for example, a vector whose distance from the acquired prediction vector is close enough to satisfy a predetermined condition from the correspondence table, and is a representative value of the teacher score corresponding to each of the two or more vectors. May be obtained. The representative values are, for example, an average value, a median value, and a weighted average. The weighted average is obtained, for example, by calculating a weight that is inversely proportional to the distance between the vectors and using the weight. Further, the predetermined conditions are, for example, that the distance is within the threshold value or smaller than the threshold value, and the short distance is the upper N (N is 2 or more).
(3) When using an arithmetic expression

The score acquisition means 3312 acquires a prediction vector by using, for example, one or more prediction daily information, gives each feature of the prediction vector to the calculation formula, executes the calculation formula, and obtains the prediction score. The calculation formula 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 output the score fluctuation information acquired by the score acquisition unit 331.

Here, the output is usually a transmission to the terminal device 4. However, the output here includes display on a display, projection using a projector, printing with a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or programs. You may think that it is a concept that includes.

The score output unit 341 passes the predicted score acquired by the score acquisition unit 331 to the score acquisition unit 331, causes the score acquisition unit 331 to acquire score fluctuation information using the predicted score, and outputs the score fluctuation information. May be.

Various information is stored in the terminal storage unit 41 constituting the terminal device 4. The various types of information are, for example, an organization identifier that identifies a user's organization and a member identifier. 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, information, and the like. Various instructions and information are, for example, prediction instructions and daily information. Here, acceptance means acceptance of information input from an input device such as a keyboard, mouse, or touch panel, reception of information transmitted via a wired or wireless communication line, an optical disk, a magnetic disk, a semiconductor memory, or the like. It is a concept including acceptance of information read from a recording medium of.

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

The various processes in the terminal processing unit 43 are, for example, processes in which the information received by the terminal receiving unit 45 is configured as displayed data. The various processes are, for example, processes configured as instructions for transmitting an instruction or the like received by the terminal reception unit 42.

The terminal transmission unit 44 transmits various instructions, information, and the like to the score prediction device 3. The various instructions and information are, for example, instructions configured by the terminal processing unit 43, instructions and information received by the terminal reception unit 42, and the like.

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

The terminal output unit 46 acquires various types of information. The various types of information are, for example, information received by the terminal receiving unit 42, information received by the terminal receiving unit 45, and information configured by the terminal processing unit 43. The various information is, for example, a predicted score.

Here, the output means display on a display, projection using a projector, printing by a printer, sound output, transmission to an external device, storage on a recording medium, storage on another processing device, another program, or the like. It is a concept that includes delivery of processing results.

Storage unit 31, organization information storage unit 311, daily information storage unit 312, business input information storage unit 3121, communication information storage unit 3122, business result information storage unit 3124, stress information storage unit 3125, measurement information storage unit 3126, image storage. As the means 3127, the voice storage means 3128, the living information storage means 3129, the teacher score storage unit 313, the correspondence information storage unit 314, and the terminal storage unit 41, a non-volatile recording medium is suitable, but a volatile recording medium. But it is feasible.

The process of storing information in the storage unit 31 or the like does not matter. For example, the information may be stored in the storage unit 31 or the like via the recording medium, or the information transmitted via the communication line or the like may be stored in the storage unit 31 or the like. Alternatively, the information input via the input device may be stored in the storage unit 31 or the like.

The reception unit 32 and the terminal reception unit 45 are usually realized by wireless or wired communication means, but may be realized by means for receiving broadcasts.

The processing unit 33, the score acquisition unit 331, the corresponding acquisition unit 3311, the score acquisition unit 3312, and the terminal processing unit 43 can usually be realized from a processor, a memory, or the like. The processing procedure of the processing unit 33 and the like is usually realized by software, and the software is recorded in a recording medium such as ROM. However, it may be realized by hardware (dedicated circuit). The processor is a CPU, MPU, GPU, or the like, and the type thereof does not matter.

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

The terminal output unit 46 may or may not include an output device such as a display or a speaker. The terminal output unit 46 can be realized by the driver software of the output device, the driver software of the output device, the output device, or the like.

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

(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 the daily information is received, the process goes to step S1402, and if the daily information is not received, the process goes to step S1403. In addition, daily information usually corresponds to a member identifier or an organization identifier.

(Step S1402) The processing unit 33 stores the daily information received in step S1401 in the daily information storage unit 312. Return to step S1401. The accumulated daily information usually corresponds to a member identifier or an organization identifier. In addition, the accumulated daily information usually corresponds to the time information. That is, it is preferable that the processing unit 33 acquires the date and time information from a clock (not shown) and stores 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 the prediction instruction is received, the process goes to step S1404, and if the prediction instruction is not received, the process returns to step S1401.

(Step S1404) Correspondence acquisition means 3311 performs correspondence acquisition processing. An example of the correspondence acquisition process will be described with reference to the flowchart of FIG. The correspondence acquisition process is a process for acquiring correspondence-related 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 of FIG. The score acquisition process is a process for acquiring a predicted score.

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

In the flowchart of FIG. 14, the correspondence acquisition process of step S1404 may be performed in advance before receiving the prediction instruction. That is, the correspondence acquisition process and the score acquisition process do not have to be performed continuously.

Further, in the flowchart of FIG. 14, the processing is terminated by the power off or the interrupt of the processing termination.

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

(Step S1501) Correspondence acquisition means 3311 acquires teacher period information. The correspondence acquisition means 3311 acquires, for example, the teacher period information included in the received prediction instruction. The correspondence acquisition means 3311 acquires, for example, predetermined teacher period information from the storage unit 31. The teacher period information is information that specifies the period of the teacher's daily information used to acquire the correspondence information.

(Step S1502) Correspondence acquisition means 3311 acquires the teacher target condition. The correspondence acquisition means 3311 acquires, for example, the teacher target condition included in the received prediction instruction. The correspondence acquisition means 3311 acquires, for example, a predetermined teacher target condition from the storage unit 31. The teacher target condition is a condition of the target organization used when predicting the score. The teacher target condition is, for example, the organization identifier of the target organization for which the predicted score is obtained. The teacher target condition is, for example, the organization identifier of the entire organization. The teacher target condition is, for example, a condition (for example, an industry identifier, a scale identifier, etc.) of an organization attribute value of a target for which a predicted score is acquired.

(Step S1503) Correspondence acquisition means 3311 substitutes 1 for counter i.

(Step S1504) The correspondence acquisition means 3311 determines whether or not the i-th teacher daily set that meets the conditions including the teacher period information and the teacher target article exists in the daily information storage unit 312. If the i-th teacher daily set exists, the process goes to step S1505, and if it does not exist, the process goes to step S1510. The teacher daily set is a set of one or more teacher daily information.

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

(Step S1506) Correspondence acquisition means 3311 performs a process of acquiring feature groups using the i-th teacher daily set acquired in step S1505. An example of such a feature group acquisition process will be described with reference to the flowchart of FIG. The feature group is usually a set of two or more features. Also, the feature here is the teacher feature.

(Step S1507) The correspondence acquisition means 3311 acquires the teacher score corresponding to the i-th teacher daily set from the teacher score storage unit 313. The teacher daily set and the teacher score correspond to each other. For example, the teacher daily information corresponding to the time information of the period specified by the period information corresponding to the teacher score is the teacher daily information corresponding to the teacher score.

(Step S1508) The corresponding acquisition means 3311 constitutes 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). do.

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

(Step S1510) The correspondence acquisition means 3311 acquires correspondence relation information using two or more teacher data temporarily accumulated in step S1508. The method for acquiring correspondence information using two or more teacher data has been described above. Further, as described above, the correspondence acquisition means 3311 acquires, for example, a learning device, a correspondence table, or an arithmetic expression.

(Step S1511) The correspondence acquisition means 3311 temporarily stores the correspondence information acquired in step S1510 in the correspondence information storage unit 314. Return to higher-level processing.

Next, an example of the feature group acquisition process in step S1506 will be described with reference to the flowchart of FIG. In the flowchart of FIG. 16, for example, what is the daily information to be used is determined in advance. Further, the information that identifies the daily information to be used is stored in association with, for example, the correspondence information. That is, the daily information used when the correspondence information is acquired and the daily information used for acquiring the predicted score are usually the same.

(Step S1601) The score acquisition unit 331 determines whether or not to use the business input information for acquiring the predicted score. If the business input information is used, the process goes to step S1602, and if the business input information is not used, the process goes 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 of FIG. The business input information processing is a process of acquiring one or two or more features related to the business input information by using the business input information.

(Step S1603) The score acquisition unit 331 determines whether or not to use the communication information for acquiring the predicted score. If the communication information is used, the process goes to step S1604, and if the communication information is not used, the process goes to step S1605.

(Step S1604) The score acquisition unit 331 performs communication information processing. An example of communication information processing will be described with reference to the flowchart of FIG. Communication information processing is a process of acquiring one or more features related to communication information by using communication information.

(Step S1605) The score acquisition unit 331 determines whether or not to use the attendance status information for acquiring the predicted score. If the attendance status information is used, the process goes to step S1606, and if the attendance status information is not used, the process goes to step S1607.

(Step S1606) The score acquisition unit 331 performs information processing on the attendance status. An example of attendance status information processing will be described with reference to the flowchart of FIG. Attendance status information processing is a process of acquiring one or more features related to attendance status information using attendance status information.

(Step S1607) The score acquisition unit 331 determines whether or not to use the business result information for acquiring the predicted score. If the business result information is used, the process goes to step S1608, and if the business result information is not used, the process goes to step S1609.

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

(Step S1609) The score acquisition unit 331 determines whether or not to use the stress information for acquiring the predicted score. If the stress information is used, the process goes to step S1610, and if the stress information is not used, the process goes to step S1611.

(Step S1610) The score acquisition unit 331 performs stress information processing. An example of stress information processing will be described with reference to the flowchart of FIG. Stress information processing is a process of acquiring one or more features related to stress information.

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

(Step S1612) The score acquisition unit 331 performs measurement information processing. An example of measurement information processing will be described with reference to the flowchart of FIG. The measurement information processing is a process of acquiring one or more features related to the measurement information by using the measurement information.

(Step S1613) The score acquisition unit 331 determines whether or not to use the image for acquiring the predicted score. If an image is used, the process goes to step S1614, and if no image is used, the process goes to step S1615.

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

(Step S1615) The score acquisition unit 331 determines whether or not to use voice for acquiring the predicted score. If voice is used, the process goes to step S1616, and if voice is not used, the process goes to step S1617.

(Step S1616) The score acquisition unit 331 performs voice information processing. An example of voice information processing will be described with reference to the flowchart of FIG. Speech information processing is a process of acquiring one or more features related to speech using speech.

(Step S1617) The score acquisition unit 331 determines whether or not to use the living information for acquiring the predicted score. If the living information is used, the process goes to step S1618, and if the living information is not used, the process returns to the higher-level processing.

(Step S1618) The score acquisition unit 331 performs life information processing. Return to higher-level processing. An example of life information processing will be described with reference to the flowchart of FIG. 24. Life information processing is a process of acquiring one or more features related to life information using life information.

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

(Step S1701) The score acquisition unit 331 substitutes 1 for the counter i.

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

(Step S1703) The score acquisition unit 331 is a daily report paired with the i-th member identifier, and acquires the daily report of the target period 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 amount of each daily report, the average value of the data amount of one daily report, the change information of the data amount, the number of appearances of negative words, the appearance rate of negative words, the number of appearances of positive words, and the positive words. The appearance rate.

(Step S1705) The score acquisition unit 331 is a document paired with the i-th member identifier, and acquires the document of the target period from the business input information storage means 3121.

(Step S1706) The score acquisition unit 331 acquires one or more document attribute values from one or more acquired documents. The document attribute value is, for example, an average value of the data amount of the document for each created day and change information of the data amount.

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

(Step S1708) The score acquisition unit 331 acquires one or more program attribute values from one or more acquired programs. The program attribute value is, for example, the average value of the number of steps of the program for each created day, and the change information of the number of steps.

(Step S1709) The score acquisition unit 331 is an operation log paired with the i-th member identifier, and acquires the operation log of the target period from the business input information storage means 3121. The operation log is an operation log for a device (for example, a personal computer) used by a 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 value is, for example, an average value of the number of operation logs for each created day and change information of 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 by using each one or more daily report attribute values of all the target members acquired in step S1704. Representative values are, for example, average, weighted average, median, and total. The score acquisition unit 331 is, for example, a representative value of the daily report data amount of all members, a representative value of change information of the data amount of all members, a representative value of the number of appearances of negative words of all members, and the appearance of negative words. The representative value of the rate, the representative value of the number of appearances of positive words, and the representative value of the appearance rate of positive words are calculated. All members are, for example, members identified by a member identifier paired with a designated organization identifier.

(Step S1713) The score acquisition unit 331 acquires a representative value for each type of document attribute value by using each document attribute value of 1 or more of all the target members acquired in step S1706. The score acquisition unit 331 is, for example, a representative value of the data amount of the document for each day of all the members, a representative value of information indicating the variation of the data amount of each day in the target period of all the members, and among all the members. Obtain the ratio that the amount of data of the document created in one day is increasing, and the ratio that the amount of data of the document created in one day is decreasing among all the members.

(Step S1714) The score acquisition unit 331 acquires a representative value for each type of program attribute value by using each one or more program attribute values of all the target members acquired in step S1708. The score acquisition unit 331 is, for example, a representative value of the number of steps of the program for each day of all the members, a representative value of information indicating the variation in the number of steps of each day in the target period of all the members, and among all the members. Obtain the ratio in which the amount of data of the number of steps of the program created in one day is increasing, and the ratio in which the number of steps of the program created in one day is decreasing among all the members.

(Step S1715) The score acquisition unit 331 acquires a representative value for each type of operation log attribute value by using each operation log attribute value of one or more of all the target members acquired in step S1710. The score acquisition unit 331 is, for example, a representative value of the number of operation logs for each day of all members, a representative value of information indicating a variation in the number of operation logs for each day in the target period of all members, and a representative value of all members. Acquire the ratio in which the number of daily operation logs is increasing and the ratio in which the number of daily operation logs is decreasing among all the members. Return to higher-level processing.

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

(Step S1801) The score acquisition unit 331 substitutes 1 for the counter i.

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

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

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

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

(Step S1806) The score acquisition unit 331 acquires one or more SNS attribute values from one or more SNS information acquired in step S1805. The SNS attribute value is, for example, the amount of data of SNS information created by a member, the number of appearances of negative words in the SNS information created by a member, the appearance rate of negative words in the SNS information created by a member, and the member. The number of appearances of positive words in the SNS information created by the member, and the appearance rate of positive words in the SNS information created by the members.

(Step S1807) The score acquisition unit 331 is an e-mail paired with the i-th member identifier, and acquires the e-mail of the target period from the communication information storage means 3122.

(Step S1808) The score acquisition unit 331 acquires one or more e-mail attribute values from one or more e-mails acquired in step S1807. The e-mail attribute values are, for example, the response rate, the number of e-mails sent by the members, the amount of e-mail data sent by the members, the number of negative words appearing in the e-mails sent by the members, and the members. The appearance rate of negative words in the sent e-mail, the number of appearances of positive words in the e-mail sent by the members, and the appearance rate of the positive words in the e-mails sent by the members.

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

(Step S1810) The score acquisition unit 331 acquires one or more voice text attribute values from one or more voice text information acquired in step S1809. The voice text attribute value is, for example, the amount of voice text information data, the number of appearances of negative words in the member's voice text information, the appearance rate of negative words in the member's voice text information, and the member's voice text information. The number of positive words appearing in, and the appearance rate of positive words in the voice text information of the members.

(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 by using each chat attribute value of 1 or more of all the target members acquired in step S1804. Representative values are, for example, average, weighted average, median, and total. The score acquisition unit 331 is, for example, a representative value of the data amount of chat information of all members, a representative value of the number of appearances of negative words of all members, a representative value of the appearance rate of negative words, and a representative of the number of appearances of positive words. Calculate the representative value of the value and the appearance rate of positive words. All members are, for example, members identified by a member identifier paired with a designated organization identifier.

(Step S1813) The score acquisition unit 331 acquires a representative value for each type of SNS attribute value by using each SNS attribute value of 1 or more of all the target members acquired in step S1806. The score acquisition unit 331 is, for example, a representative value of the data amount of the SNS information for each day of all the members, a representative value of the information indicating the variation of the data amount of the SNS information for each day in the target period of all the members, and the total configuration. Percentage of members who have a tendency to increase the amount of SNS information data created per day, percentage of all members who have a tendency to decrease the amount of SNS information data created per day, and negative words in SNS information. The representative value of the number of appearances of the negative word, the representative value of the appearance rate of the negative word in the SNS information, the representative value of the number of appearances of the positive word in the SNS information, and the representative value of the appearance rate of the positive word in the SNS information are acquired.

(Step S1814) The score acquisition unit 331 acquires a representative value for each type of e-mail attribute value by using each one or more e-mail attribute values of all the target members acquired in step S1808. The score acquisition unit 331 is, for example, a representative value of the response rate of all members, a representative value of the number of e-mails, a representative value of the amount of data in e-mails, a representative value of the number of appearances of negative words in e-mails, and e-mails. The representative value of the appearance rate of the negative word in the email, the representative value of the number of appearances of the positive word in the e-mail, and the representative value of the appearance rate of the positive word in the e-mail are acquired.

(Step S1815) The score acquisition unit 331 acquires a representative value for each type of voice text attribute value by using each voice text attribute value of one or more of all the target members acquired in step S1810. Return to higher-level processing. The score acquisition unit 331 is, for example, a representative value of the data amount of the voice text information of all the members, a representative value of the number of appearances of negative words in the voice text information of all the members, and the voice text information of all the members. The representative value of the appearance rate of negative words, the representative value of the number of appearances of positive words in the voice text information of all members, and the representative value of the appearance rate of positive words in the voice text information of all members are acquired.

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

(Step S1901) The score acquisition unit 331 substitutes 1 for the counter i.

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

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

(Step S1904) The score acquisition unit 331 acquires one or more attendance attribute values using the acquired attendance information. The attendance attribute value is, for example, the number of annual leave of the member, the number of overtime hours of the member, and the number of holiday work of the member.

(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 by using each attendance attribute value of 1 or more of all the target members acquired in step S1704. Return to higher-level processing. The representative values are, for example, an average value, a weighted average value, a median value, and a total. The score acquisition unit 331 acquires, for example, a representative value of the number of annual leave of all members, a representative value of the number of overtime hours of the members, and a representative value of the number of holiday work of the members.

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

(Step S2001) The score acquisition unit 331 substitutes 1 for the counter i.

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

(Step S2003) The score acquisition unit 331 is one or two or more answers paired with the i-th member identifier, and acquires one or more answers in the target period from the stress information storage means 3125. The answer is an answer to a questionnaire for acquiring stress information (for example, the degree of stress).

(Step S2004) The score acquisition unit 331 acquires stress information using one or more answers acquired in step S2003. The stress information may be one answer itself, or may be a stress level obtained by substituting one or two or more answers (for example, a numerical value) into an arithmetic expression and executing the arithmetic, or two or more answers as elements. It may be stress information paired with a vector that most closely resembles the vector it has. It is assumed that the arithmetic expression or the table having two or more pairs of the vector and the stress information is stored in the storage unit 31.

(Step S2005) The score acquisition unit 331 is one or two or more biological information (for example, heart rate, blood pressure) paired with the i-th member identifier, and stores one or more biological information in the target period as measurement information. Obtained from means 3126. The answer is an answer to a questionnaire for acquiring stress information (for example, the degree of stress).

(Step S2006) The score acquisition unit 331 acquires stress information using one or more biological 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 the representative value of the first stress information by using the stress information based on the questionnaire of all the target members acquired in step S2004. The representative values are, for example, an average value, a weighted average value, a median value, and a total.

(Step S2009) The score acquisition unit 331 acquires the representative value of the second stress information by using the stress information based on the biological information of all the target members acquired in step S2006. Return to higher-level processing. The representative values are, for example, an average value, a weighted average value, a median value, and a total.

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

(Step S2101) The score acquisition unit 331 substitutes 1 for the counter i.

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

(Step S2103) The score acquisition unit 331 is a heart rate of 1 or 2 or more paired with the i-th member identifier, and acquires a heart rate of 1 or more in the target period from the measurement information storage means 3126.

(Step S2104) The score acquisition unit 331 acquires a heart rate attribute value of 1 or 2 or more by using the heart rate of 1 or more acquired in step S2103. The heart rate attribute value is an average value of heart rate, information indicating variation in heart rate (for example, variance), and a maximum value of heart rate.

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

(Step S2106) The score acquisition unit 331 acquires a blood pressure attribute value using the blood pressure of 1 or more acquired in step S2103. The blood pressure attribute value is, for example, an average value of one or more blood pressures, information indicating variation in blood pressure (for example, variance), and a maximum value of blood pressure. The blood pressure is systolic blood pressure, diastolic blood pressure, or systolic blood pressure and diastolic blood pressure.

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

(Step S2108) The score acquisition unit 331 uses each heart rate attribute value of one or more of all the target members acquired in step S2104, and has each heart rate attribute value for each type of heart rate attribute value. Get the representative value. The representative values are, for example, an average value, a weighted average value, a median value, and a total.

(Step S2109) The score acquisition unit 331 uses each blood pressure attribute value of 1 or more of all the target members acquired in step S2106 to obtain a representative value of each blood pressure attribute value for each type of blood pressure attribute value. get. Return to higher-level processing. The representative values are, for example, an average value, a weighted average value, a median value, and a total.

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

(Step S2201) The score acquisition unit 331 substitutes 1 for the counter i.

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

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

(Step S2204) The score acquisition unit 331 substitutes 1 for the counter j.

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

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

(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. .. The image attribute value is, for example, a specific facial expression identifier (for example, a negative facial expression identifier) or a ratio of negative facial expressions. Further, the identifiers of negative facial expressions are, for example, "anger" and "sorrow".

(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 each image attribute value of 1 or more of all the target members acquired in step S2108, and sets a representative value of each image attribute value for each type of image attribute value. get. Return to higher-level processing. The representative values are, for example, an average value, a weighted average value, a median value, and a total.

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

(Step S2301) The score acquisition unit 331 substitutes 1 for the counter i.

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

(Step S2303) The score acquisition unit 331 is one or two or more voices paired with the i-th member identifier, and acquires one or more voices of the target period from the voice storage means 3128.

(Step S2304) The score acquisition unit 331 substitutes 1 for the counter j.

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

(Step S2306) The score acquisition unit 331 acquires one or more types of voice attribute values of the voice of the j-th 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 feature amount of voice, a ratio of utterance time in a conference attended, 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 value by using each voice attribute value of 1 or more of all the target members acquired in step S2108 for each type of voice attribute value. do. Return to higher-level processing. The representative values are, for example, an average value, a weighted average value, a median value, and a total.

Next, an example of life information processing in step S1618 will be described with reference to the flowchart of FIG. 24.

(Step S2401) The score acquisition unit 331 substitutes 1 for the counter i.

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

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

(Step S2404) The score acquisition unit 331 acquires one or more sleep attribute values by using one or two or more sleep times acquired in step S2403. The sleep attribute values include, for example, the average value of sleep time per day, information indicating variation in sleep time (for example, variance), the number of days when sleep time is below the threshold, and the ratio of days when sleep time is below the threshold. Is.

(Step S2405) The score acquisition unit 331 is a drinking day paired with the i-th member identifier, and acquires the drinking day of the target period from the living information storage means 3129.

(Step S2406) The score acquisition unit 331 acquires one or more drinking attribute values by using one or two or more drinking days acquired in step S2405. The drinking attribute value is, for example, the number of drinking days, the ratio of drinking days, and the maximum value of consecutive drinking days.

(Step S2407) The score acquisition unit 331 is the number of cigarettes paired with the i-th member identifier, and acquires the number of cigarettes on each day of the target period from the living information storage means 3129.

(Step S2408) The score acquisition unit 331 acquires one or more cigarette attribute values by using one or two or more cigarettes acquired in step S2407. The cigarette attribute values are, for example, the total number of cigarettes smoked during the period, the average value of the number of cigarettes smoked per day, the number of days when the number of cigarettes smoked exceeds the threshold value, and the number of cigarettes smoked. The percentage of days above the 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 by using each sleep attribute value of 1 or more of all the target members acquired in step S2404. The representative values are, for example, the average value of the sleep time, the average of the information indicating the variation of the sleep time, the average of the number of days when the sleep time is below the threshold value, and the average of the ratio of the days when the sleep time is below the threshold value. The score acquisition unit 331 may acquire, for example, the number or proportion of members so small that the sleep time satisfies a predetermined condition.

(Step S2411) The score acquisition unit 331 acquires a representative value for each type of drinking attribute value by using each drinking attribute value of 1 or more of all the target members acquired in step S2406. The representative value is, for example, an average value of the number of drinking days, an average value of the ratio of drinking days, and an average value of the maximum values of consecutive drinking days. The score acquisition unit 331 may acquire, for example, the number or proportion of members so large that the drinking date satisfies a predetermined condition.

(Step S2412) The score acquisition unit 331 acquires a representative value for each type of tobacco attribute value by using each of one or more tobacco attribute values of all the target members acquired in step S2408. Return to higher-level processing. The representative values are, for example, the average value of the total number of smoked cigarettes, the average value of the number of smoked cigarettes, the average value of the number of days when the number of smoked cigarettes is equal to or higher than the threshold value, and the number of smoked cigarettes is the threshold value. It is the average value of the ratio of the above days. The score acquisition unit 331 may acquire, for example, the number or proportion of members so that the number of smoked cigarettes satisfies a predetermined condition.

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

(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 prediction organization identifier, which is an organization identifier corresponding to the prediction instruction.

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

(Step S2504) The score acquisition means 3312 acquires a feature group by using one or more predicted daily information acquired in step S2503. The feature group acquisition process has been described with reference to the flowchart of FIG.

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

(Step S2506) The score acquisition means 3312 acquires a prediction vector having each feature of the feature group acquired in step S2504 as an element, and uses the prediction vector and the correspondence information acquired in step S2505 to obtain a prediction score. To get. Return to higher-level processing. An example of the algorithm for acquiring the predicted score is described above.

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

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

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

(Step S2603) The terminal processing unit 43 is information for associating the daily information acquired in step S2601 with the member identifier acquired in step S2602, and constitutes information to be transmitted.

(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 the prediction instruction has been received. If the prediction instruction is received, the process goes to step S2606, and if the prediction instruction is not received, the process returns to step S2601.

(Step S2606) The terminal processing unit 43 constitutes a prediction instruction to be transmitted. The forecast instruction to be transmitted has, for example, an organization identifier and period information. The organization identifier is the identifier of the organization for which the score is predicted. The period information is information that specifies the period of daily information used for predicting the score. The organization identifier and the period information have default values and may be stored in the terminal storage unit 41.

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

(Step S2608) The terminal receiving unit 45 determines whether or not the predicted score has been received. If the predicted score is received, the process goes to step S2609, and if the predicted score is not received, the process returns to step S2608.

(Step S2609) The terminal processing unit 43 configures the predicted score to be output by using the predicted score received in step S2608. The terminal output unit 46 outputs the predicted score. Return to step S2601.

Further, in the flowchart of FIG. 26, the processing is terminated by the power off or the interrupt of the processing termination.

Hereinafter, a specific operation example of the score prediction device 3 in the present embodiment will be described. The following eight are 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. Further, in the following specific example, a method of acquiring a predicted score using a learning device will be described, but as described above, the predicted score is acquired using other correspondence information such as a correspondence table and a calculation formula. Is also good.

(Specific example 1)
A specific example 1 is a case where a predicted score is obtained by using the response rate of the member's e-mail to the e-mail from the management team and the boss.

The correspondence acquisition means 3311 of the score prediction device 3 acquires each of two or more member identifiers paired with the organization identifier “A” of the target organization. Next, the correspondence acquisition means 3311 is an e-mail paired with two or more member identifiers, and is a unit period (here, one year) for constructing the learning device (for example, 2000 or later, 2019). E-mails (for each year up to) are acquired from the communication information storage means 3122 for each unit period in association with the member identifier.

Then, the response acquisition means 3311 performs the response target person (management) paired with each member identifier from the member information database (database of two or more member information) of the organization identifier "A" (not shown) by the above-mentioned processing. Acquire one or more member identifiers of the response target of the team, boss, etc.).

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

Next, the correspondence acquisition means 3311 reads the teacher score corresponding to each year from the teacher score storage unit 313. The teacher score corresponds to, for example, an identifier of a unit period (here, a year) and an organization identifier "A".

Next, the correspondence acquisition means 3311 constitutes two or more teacher data in which the representative value of the response rate is the explanatory variable and the teacher score is the objective variable.

Then, the correspondence acquisition means 3311 gives the two or more teacher data to the module that performs the learning process of machine learning, executes the module, acquires the learning device, and sets the learning device as the organization identifier "A". As a pair, it is stored in the correspondence information storage unit 314. The correspondence acquisition means 3311 uses two or more pieces of information such as the average value of the response rates of all members, the median value of the response rates of all members, and the variance as explanatory variables for each unit period. , You may get a learner.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 acquires predicted daily information, which is an e-mail paired with the above-mentioned organization identifier and is an e-mail 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 by using the acquired e-mail for each member identifier. Next, the score acquisition means 3312 acquires a representative value (for example, an average value) of the response rate of all the members.

Next, the score acquisition means 3312 acquires a learning device paired with the organization identifier "A" of the correspondence information storage unit 314. Next, the score acquisition means 3312 gives the acquired response rate in 2020 and the learning device to the module that performs the prediction processing of machine learning, and acquires the prediction score by executing the module. The score acquisition means 3312 may perform prediction processing using, for example, two or more pieces of information such as the average value of the response rates of all the members, the median value of the response rates of all the members, and the variance. ..

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

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

(Specific example 2)
A specific example 2 is a case where a predicted score is acquired using a daily report. In Specific Example 2, the unit period is one year.

The correspondence acquisition means 3311 of the score prediction device 3 acquires each of two or more member identifiers paired with the organization identifier “A” of the target organization. Next, the correspondence acquisition means 3311 is a daily report paired with two or more member identifiers, and the daily report for one year of each year (for example, from 2000 to 2019) is a member identifier and a time identifier. In association with and, it is acquired from the business input information storage means 3121.

Next, the correspondence acquisition means 3311 acquires one or more daily report attribute values by using each daily report for each member identifier and each year. The daily report attribute value to be acquired is, for example, change information of the amount of data, the appearance rate of negative words in the daily report for a predetermined period, and the appearance rate of positive words in the daily report for a predetermined period.

Next, the correspondence acquisition means 3311 acquires a representative value (for example, an average value) of the daily report attribute value in the one-year daily report of each member identifier for each year and for each type of the above daily report attribute value. Next, the correspondence acquisition means 3311 acquires a representative value (for example, an average value) of the daily report attribute values of all the members for each type of the daily report attribute value described above for each year. That is, the response acquisition means 3311 is a negative word in the daily report for each year, for example, the average of the change information of the data amount of all the members of the organization of the organization identifier "A", and the predetermined period (here, one year). Obtain the average value of the appearance rate of and the average value of the appearance rate of positive words in the daily report for a predetermined period.

Next, the correspondence acquisition means 3311 acquires a teacher vector having each acquired daily report attribute value as an element for each year. Next, the correspondence acquisition means 3311 reads 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 gives the acquired plurality of teacher data to the module that performs the learning process of machine learning, acquires the learning device by executing the module, and uses the learning device as the organization identifier ". Paired with "A", it is stored in the correspondence information storage unit 314.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 is a daily report paired with the above-mentioned organization identifier "A", and is a business input information storage means for predicting daily information which is a daily report after January 1, 2020. Obtained from 3121.

Next, the score acquisition means 3312 acquires two or more daily report attribute values for each member identifier by using the daily report acquired for each member identifier. Next, the score acquisition means 3312 acquires a representative value (for example, an average value) of each of two or more daily report attribute values of all the members, and a prediction vector having the representative value of each of the two or more daily report attribute values as an element. To configure. The prediction vector is, for example, a vector having an average value of change information of the amount of data, an appearance rate of negative words, and an appearance rate of positive words as elements.

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

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

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

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

The correspondence acquisition means 3311 of the score prediction device 3 acquires each of two or more member identifiers paired with the organization identifier “A” of the target organization. Next, the correspondence acquisition means 3311 is attendance information paired with two or more member identifiers, and the attendance information for one year of each year (for example, from 2000 to 2019) is used as a member identifier. It is acquired from the attendance status information storage means 3123 in association with the time identifier.

Next, the response acquisition means 3311 calculates the average overtime hours per month for each member identifier and each year by using the attendance information for each member identifier and each year. Next, the response acquisition means 3311 acquires the average value and the variance of the overtime hours of all the members of the organization "A" for each year.

Next, the correspondence acquisition means 3311 acquires a teacher vector having the average value and the variance of the acquired overtime hours of all the members as elements for each year. Next, the correspondence acquisition means 3311 reads 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 gives the acquired plurality of teacher data to the module that performs the learning process of machine learning, acquires the learning device by executing the module, and uses the learning device as the organization identifier ". Paired with "A", it is stored in the correspondence information storage unit 314.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 is attendance information paired with the above-mentioned organization identifier "A", and predicts daily information which is attendance information after January 1, 2020, and attendance status information. Obtained from the storage means 3123.

Next, the score acquisition means 3312 calculates the average overtime hours per month for each member identifier by using the attendance information acquired for each member identifier. Next, the score acquisition means 3312 acquires the average value and the variance of the overtime hours of all the members. Next, the score acquisition means 3312 constitutes a prediction vector having the average value and the variance of the overtime hours of all the members as elements.

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

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

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

(Specific example 4)
This is a case of acquiring a predicted score using stress information and measurement information. In Specific Example 4, the unit period is one year. Further, in this specific example, the stress information (for example, the degree of stress) is acquired by the score acquisition unit 331 by the method described above, and is stored in the stress information storage means 3125 in association with the member identifier and the time identifier. Yes. In addition, the score acquisition unit 331 acquires the attribute values (for example, average value, variance, etc.) of the stress information of all the members in each year, associates them with the organization identifier "A" and the identifier of each year, and stresses. It is assumed that the information is stored in the information storage means 3125.

Further, the measurement information (heart rate, blood pressure) is received by the score prediction device 3 from a wearable terminal (not shown) worn by the member, and is associated with the member identifier and the time identifier to store the measurement information. It is assumed that it has accumulated in 3126. Further, the score acquisition unit 331 acquires the representative value of the heart rate and the representative value of the blood pressure for each member identifier and each year, and the representative value of the heart rate and the representative value of the blood pressure of all the members for each year. Is acquired, paired with the organization identifier "A" and the identifier of each year, and stored in the measurement information storage means 3126.

In such a situation, the correspondence acquisition means 3311 of the score prediction device 3 has elements of the attribute value of the stress information, the representative value of the heart rate, and the representative value of the blood pressure of each year paired with the tissue identifier "A" of the target tissue. It is assumed that the teacher vector is acquired every year. Next, the correspondence acquisition means 3311 reads 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 gives the acquired plurality of teacher data to the module that performs the learning process of machine learning, acquires the learning device by executing the module, and uses the learning device as the organization identifier ". Paired with "A", it is stored in the correspondence information storage unit 314.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 is stress information paired with the above-mentioned tissue identifier “A”, and stress information after January 1, 2020 is acquired from the stress information storage means 3125. Further, the score acquisition means 3312 acquires the attribute value (for example, average value, variance, etc.) of the stress information from the acquired stress information.

Further, the score acquisition means 3312 acquires measurement information (heart rate, blood pressure) paired with the tissue identifier "A" after January 1, 2020 from the measurement information storage means 3126. Next, the score acquisition means 3312 acquires a representative value of each acquired measurement information.

Next, the score acquisition means 3312 constitutes a prediction vector having an attribute value of stress information and a representative value of each measurement information as elements.

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

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

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

(Specific example 5)
The specific example 5 is a case where the predicted score is acquired by using the communication information. In Specific Example 5, the unit period is one month.

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

Next, the correspondence acquisition means 3311 acquires one or more types of communication attribute values by using the communication information for each member identifier and each month. The communication attribute values to be acquired are, for example, the number of appearances of negative words, the appearance rate of negative words, the number of appearances of positive words, and the appearance rate of positive words. Next, the correspondence acquisition means 3311 acquires the representative value (for example, the average value) of the communication attribute values of all the members on a monthly basis.

Next, the correspondence acquisition means 3311 acquires a teacher vector having each acquired communication attribute value as an element for each month. Next, the correspondence acquisition means 3311 reads 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 such a 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 in each year is stored in the teacher score storage unit 313. It may be stored. If only one teacher score is stored in the teacher score storage unit 313 in each year, the teacher scores in each month of a certain year will be the same teacher score.

Next, the correspondence acquisition means 3311 gives a large number of acquired teacher data to a module that performs learning processing of machine learning, acquires a learning device by executing the module, and uses the learning device as an organization identifier ". Paired with "A", it is stored in the correspondence information storage unit 314.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 is communication information paired with the above-mentioned organization identifier “A”, and the communication information of May 2020 is acquired from the communication information storage means 3122.

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

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

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

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

(Specific example 6)
A specific example 6 is a case where a predicted score is acquired by using the voice information of a videophone. It is assumed that the voice storage means 3128 stores a large amount of voice information (voice file) corresponding to the organization identifier "A" and the time information. In Specific Example 6, the unit period is one year.

In such a situation, the corresponding acquisition means 3311 of the score prediction device 3 acquires voice information paired with the organization identifier "A" of the target organization every year.

Next, the corresponding acquisition means 3311 performs spectral analysis on each acquired voice information every year, and acquires two or more feature quantities of the voice. Further, the corresponding acquisition means 3311 acquires a representative value of the feature amount for each type of the feature amount for each year from two or more feature amounts of the voice. Next, the correspondence acquisition means 3311 acquires a teacher vector having a representative value of each type of the feature amount as an element for each year. Next, the correspondence acquisition means 3311 reads 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 gives the acquired plurality of teacher data to the module that performs the learning process of machine learning, acquires the learning device by executing the module, and uses the learning device as the organization identifier ". Paired with "A", it is stored in the correspondence information storage unit 314.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 is voice information paired with the above-mentioned organization identifier "A", and voice information after January 1, 2020 is acquired from the voice storage means 3128.

Next, the corresponding acquisition means 3311 performs spectral analysis on each acquired voice information after January 1, 2020, and acquires two or more feature quantities of the voice. Further, the corresponding acquisition means 3311 acquires a representative value of the feature amount for each type of the feature amount from two or more feature amounts of the voice. Next, the correspondence acquisition means 3311 acquires a prediction vector having a representative value of each type of the feature amount as an element.

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

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

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

(Specific example 7)
This is a case where facial expression analysis is performed from a videophone image and a predicted score is obtained from a specific facial expression. It is assumed that the image storage means 3127 stores a large number of images (video files) corresponding to the organization identifier "A" and the time information. In Specific Example 7, the unit period is one year.

In such a situation, the corresponding acquisition means 3311 of the score prediction device 3 acquires an image paired with the organization identifier “A” of the target organization every year. Next, the correspondence acquisition means 3311 performs a facial expression analysis process on each of the images, and acquires a facial expression identifier (for example, any of emotions and sorrows, normality). Next, the response acquisition means 3311 acquires the ratio of negative facial expressions (“angry” and “sorrow”) every year.

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

Next, the correspondence acquisition means 3311 gives the acquired plurality of teacher data to the module that performs the learning process of machine learning, acquires the learning device by executing the module, and uses the learning device as the organization identifier ". Paired with "A", it is stored in the correspondence information storage unit 314.

Next, it is assumed that the score prediction device 3 receives the prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 is an image paired with the above-mentioned organization identifier “A”, and the image after January 1, 2020 is acquired from the voice storage means 3128.

Next, the correspondence acquisition means 3311 performs facial expression analysis processing on each acquired image after January 1, 2020, and acquires a facial expression identifier. Next, the response acquisition means 3311 acquires the percentage of negative facial expressions (“angry” and “sorrow”) after January 1, 2020.

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

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

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

(Specific Example 8)
The specific example 8 is a case where the prediction score is acquired by using the daily information of another organization that matches the condition of the organization identifier of the corresponding organization. In Specific Example 8, the unit period is one year.

It is assumed that the score prediction device 3 receives from the terminal device 4 a prediction instruction including an organization condition having an industry identifier "trading company" and a scale identifier "large company" and an organization 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 scale 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 by using the acquired daily information for each organization identifier and each year. Further, the correspondence acquisition means 3311 reads out each organization identifier and 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 organization identifier and each year.

Next, the correspondence acquisition means 3311 gives the acquired plurality of teacher data to the module that performs the learning process of machine learning, acquires the learner by executing the module, and sets the learner as an organizational condition ( Here, the industry identifier “trading company” and the scale identifier “large company”) are paired 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 obtains one or more types of daily information of the prediction target period (for example, after January 1, 2020). Obtained from the daily information storage unit 312.

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

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

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

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

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

Further, according to this embodiment, one of business information, input information, communication information, attendance status information, business result information, health information, biological information, stress information, measurement information, voice, image, living information, etc. Alternatively, two or more types of information can be used to predict the score of an organization.

Further, according to the present embodiment, the score of the organization can be predicted by using the score based on the past questionnaire and the daily information without conducting the questionnaire.

Further, according to the present embodiment, the score of the organization can be predicted by using the machine learning technique, the data mining technique and the like.

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

Further, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded on a recording medium such as a CD-ROM and disseminated. It should be noted that this also applies to other embodiments herein. The software that realizes the score prediction device 3 in this embodiment is the following program. That is, the program corresponds to the teacher score, which is the score of the organization obtained by using the computer with a plurality of organization answer information indicating the answer of the question to each of two or more members of the organization, and the question or the answer. It is the daily information during the period, the correspondence relationship with one or more teacher daily information which is the daily information that can be acquired in the daily life of each of the two or more members, and the daily information of each of the two or more members of the organization. , A score acquisition unit for acquiring a predicted score, which is a score corresponding to the one or more predicted daily information, using one or more predicted daily information which is daily information in the period of the prediction target of the score of the organization, and the prediction. It is a program to function as a score output unit that outputs a score.

Further, FIG. 27 shows the appearance of a computer that executes the program described in the present specification to realize the score prediction device 3 and the like in the various embodiments described above. The above-described embodiment can be realized by computer hardware and a computer program executed on the computer hardware. 27 is an overview view of the computer system 300, and FIG. 28 is a block diagram of the system 300.

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

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

The program for causing the computer system 300 to execute the functions of the score prediction device 3 and the like according to the above-described embodiment may be stored in the CD-ROM 3101, inserted into the CD-ROM drive 3012, and further transferred to the hard disk 3017. .. Alternatively, the program may be transmitted to the computer 301 via a network (not shown) and stored in the hard disk 3017. The program is loaded into RAM 3016 at run time. The program may be loaded directly from the CD-ROM3101 or the network.

The program does not necessarily have to include an operating system (OS), a third-party program, or the like that causes the computer 301 to execute the functions of the score prediction device 3 or the like according to the above-described embodiment. The program only needs to include a part of instructions that call the appropriate function (module) in a controlled manner and obtain the desired result. It is well known how the computer system 300 works, and detailed description thereof will be omitted.

In the above program, in the step of transmitting information and the step of receiving information, the processing performed by the hardware, for example, the processing performed by the modem or the interface card in the transmission step (only performed by the hardware). Processing) is not included.

Further, the number of computers that execute the above program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

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

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

It goes without saying that the present invention is not limited to the above embodiments, and various modifications can be made, and these are also included in the scope of the present invention.

As described above, the score prediction device according to the present invention has an effect that the score of the organization can be predicted by using the daily information of the members of the organization, and is useful as a score prediction device or the like.

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 Information storage unit 313 Teacher score storage unit 314 Correspondence information storage unit 331 Score acquisition unit 341 Score output unit 3121 Business input information storage unit 3122 Communication information storage unit 3123 Attendance status information storage unit 3124 Business result information storage unit 3125 Stress information storage unit 3126 Measurement information storage means 3127 Image storage means 3128 Voice storage means 3129 Living information storage means 3311 Correspondence acquisition means 3312 Score acquisition means

Claims (21)

The teacher score, which is the score of the organization obtained by using a plurality of organization answer information indicating the answers to the questions to each of two or more members of the organization, and the daily information in the period corresponding to the question or the answer. Correspondence relationship with one or more teacher daily information which is daily information that can be acquired in the daily life of two or more members, and daily information of two or more members of the organization, which is a prediction target of the score of the organization. A score acquisition unit that acquires a predicted score, which is a score corresponding to the one or more predicted daily information, using one or more predicted daily information that is daily information in a period.
A score prediction device including a score output unit that outputs the predicted score. Each of the above 1 or more teacher daily information includes teacher work information regarding the work performed by the member.
Each of the above 1 or more forecast daily information includes forecast business information regarding the business performed by the member.
The score acquisition unit
The first aspect of claim 1, wherein the predicted score is acquired by using the correspondence relationship between one or more teacher daily information including the teacher business information and the teacher score, and one or more predicted daily information including the predicted business information. Score predictor. The teacher business information includes teacher input information input by the member.
The forecast business information includes forecast input information input by the member.
The score acquisition unit
The second aspect of claim 2, wherein the predicted score is acquired by using the correspondence relationship between one or more teacher daily information including the teacher input information and the teacher score, and one or more predicted daily information including the predicted input information. Score predictor. The teacher input information includes the teacher work input information regarding the input for the performance of the member's own work.
The predictive input information includes predictive business input information regarding input for the performance of the member's own business.
The score acquisition unit
3. Claim 3 to acquire the predicted score by using the correspondence relationship between one or more teacher daily information including the teacher business input information and the teacher score, and one or more predicted daily information including the predicted business input information. The score predictor described. The teacher input information includes teacher communication information regarding communication between the two or more members.
The predictive input information includes predictive communication information regarding communication between the two or more members.
The score acquisition unit
Claim 2 or claim to acquire the predicted score by using the correspondence relationship between the teacher daily information including the teacher communication information and the teacher score, and the predicted daily information including the predicted communication information. Item 3. The score prediction device according to item 3. The score acquisition unit
Specify the data amount information that specifies the data amount of the teacher input information, the input frequency information that specifies the input frequency of the teacher input information, the analysis information that shows the analysis result of the teacher input information, and the input timing of the teacher input information. One or more kinds of information of the timing information to be performed is acquired, and the data amount information of the prediction input information, the input frequency information of the prediction input information, the analysis information of the prediction input information, and the timing information of the prediction input information. One or more types of information are acquired, and correspondence information that specifies the correspondence between one or more teacher identities including one or more types of information acquired from the teacher input information and the teacher score is acquired, and the correspondence is obtained. The score prediction device according to any one of claims 3 to 5, which obtains the prediction score by using one or more prediction factors including one or more types of information acquired from the relational information and the prediction input information. .. The teacher business information includes teacher attendance status information regarding the attendance of the member.
The forecasted business information includes forecasted attendance status information regarding the attendance of the member.
The score acquisition unit
Claim 2 to acquire the predicted score by using the correspondence relationship between one or more teacher daily information including the teacher attendance status information and the teacher score, and one or more predicted daily information including the predicted attendance status information. The score predictor described. The teacher work information includes teacher work result information regarding the result of the work execution of the member.
The forecasted business information includes forecasted business result information regarding the result of business execution of the member.
The score acquisition unit
Claim 2 to acquire the predicted score by using the correspondence relationship between one or more teacher daily information including the teacher work result information and the teacher score, and one or more predicted daily information including the predicted work result information. The score predictor described. Each of the above 1 or more teacher daily information includes teacher health information regarding the health of the member.
Each of the above 1 or more predicted daily information includes predicted health information regarding the health of the member.
The score acquisition unit
The first aspect of claim 1, wherein the predicted score is acquired by using the correspondence relationship between one or more teacher daily information including the teacher health information and the teacher score, and one or more predicted daily information including the predicted health information. Score predictor. The teacher health information includes teacher biometric information regarding the living body of the member.
The predicted health information includes predicted biological information regarding the living body of the member.
The score acquisition unit
The ninth aspect of claim 9, wherein the predicted score is acquired by using the correspondence relationship between one or more teacher daily information including the teacher biological information and the teacher score, and one or more predicted daily information including the predicted biological information. Score predictor. The teacher biometric information includes teacher stress information regarding the stress of the member.
The predicted biological information includes predicted stress information regarding the stress of the member.
The score acquisition unit
The tenth aspect of claim 10, wherein the predicted score is acquired by using the correspondence relationship between one or more teacher daily information including the teacher stress information and the teacher score, and one or more predicted daily information including the predicted stress information. Score predictor. The teacher biological information includes teacher measurement information which is a measurement result of the biological body of the member.
The predicted biological information includes predicted measurement information acquired from the living body of the member.
The score acquisition unit
Claim 10 or claim to acquire the predicted score by using the correspondence relationship between the teacher daily information including the teacher measurement information and the teacher score, and the predicted daily information including the predicted measurement information. Item 11. The score prediction device according to Item 11. The teacher biometric information is a feature amount of an image obtained by photographing the member, an image obtained by photographing the member, a voice emitted by the member, or a feature amount of a voice emitted by the member. Including feature information
The predicted living body information is a predicted living body which is a feature amount of an image obtained by photographing the member, an image obtained by photographing the member, a voice emitted by the member, or a voice emitted by the member. Including feature information
The score acquisition unit
10. Claim 10 to acquire the predicted score by using the correspondence relationship between one or more teacher daily information including the teacher biological characteristic information and the teacher score, and one or more predicted daily information including the predicted biological characteristic information. 12. The score prediction device according to any one of claims 12. The teacher health information includes teacher life information regarding the lives of the members.
The predicted health information includes predicted life information regarding the lives of the members.
The score acquisition unit
Claim 9 for acquiring the predicted score using the correspondence relationship between one or more teacher daily information including the teacher life information and the teacher score, and one or more predicted daily information including the predicted life information. Item 13 The score prediction device according to any one of the items. The score acquisition unit
Correspondence acquisition means for acquiring correspondence relation information for specifying correspondence relation between one or more teacher daily information and said teacher score,
The score prediction device according to any one of claims 1 to 14, further comprising a score acquisition means for acquiring the prediction score by using the correspondence information and the prediction daily information of one or more. The teacher daily information of 1 or more and the teacher score correspond to 1 or more organizational attribute values.
The corresponding acquisition means
For each organizational condition configured using the one or more organizational attribute values, the correspondence information that specifies the correspondence between the one or more teacher daily information and the teacher score is acquired.
The score acquisition means is
The score prediction device according to claim 15, wherein the prediction score is acquired by using the correspondence relationship information corresponding to the organizational condition that the organization to be predicted matches and the prediction daily information of one or more. The corresponding acquisition means
In a learning model in which the learning process is performed by a machine learning algorithm using the teacher daily information of 1 or more and the teacher score, the teacher daily information of 1 or more is used as an explanatory variable, and the teacher score is used as an objective variable. Get some correspondence information and
The score acquisition means is
The score prediction device according to claim 15 or 16, wherein prediction processing is performed by a machine learning algorithm using the learning model and the prediction daily information of one or more, and the prediction score is acquired. The corresponding acquisition means
Using the teacher daily information of 1 or more, a teacher vector which is a vector having 2 or more elements is acquired, and a correspondence table which is a pair of the teacher vector and a teacher score and has 1 or more correspondence information. Get information,
The score acquisition means is
Claim 15 or claim 16 for acquiring a prediction vector which is a vector having two or more elements by using the one or more prediction daily information, applying the prediction vector to the correspondence table, and acquiring the prediction score. Described score predictor. The corresponding acquisition means
Correspondence information that is an arithmetic expression that acquires a teacher vector that is a vector having two or more elements using the teacher daily information of one or more, inputs each element of the teacher vector, and outputs the teacher score. To get,
The score acquisition means is
Using the one or more prediction daily information, a prediction vector which is a vector having two or more elements is acquired, each element of the prediction vector is given to the calculation formula, the calculation formula is executed, and the prediction score is obtained. The score predictor according to claim 15 or claim 16 to be acquired. It is a score prediction method realized by the score acquisition unit and the score output unit.
The teacher score, which is the score of the organization acquired by the score acquisition unit using a plurality of organization response information indicating the answers to the questions to each of the two or more members of the organization, and the period corresponding to the questions or the answers. It is the daily information, the correspondence relationship with one or more teacher daily information which is the daily information that can be acquired in the daily life of each of the two or more members, and the daily information of each of the two or more members of the organization. A score acquisition step of acquiring a predicted score, which is a score corresponding to the above-mentioned one or more predicted daily information, by using one or more predicted daily information which is the daily information in the period of the prediction target of the score.
A score prediction method in which the score output unit includes a score output step for outputting the predicted score. Computer,
The teacher score, which is the score of the organization obtained by using a plurality of organization answer information indicating the answers to the questions to each of two or more members of the organization, and the daily information in the period corresponding to the question or the answer. Correspondence relationship with one or more teacher daily information which is daily information that can be acquired in the daily life of two or more members, and daily information of two or more members of the organization, which is a prediction target of the score of the organization. A score acquisition unit that acquires a predicted score, which is a score corresponding to the one or more predicted daily information, using one or more predicted daily information that is daily information in a period.
A program for functioning as a score output unit that outputs the predicted score.

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