Score prediction device, score prediction method, and program

Abstract

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

Description

Technical Field

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

Background Art

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

[0003] This management diagnosis support device totals and analyzes and displays the results of a score-based questionnaire in which answers are selected from a plurality of options for question items, and the questionnaire answer results obtained from each level constituting the organization, and for each of the plurality of question items, an analysis table to which a plurality of diagnostic elements related to corporate management are assigned, and a score indicating weighting is set for each diagnostic element corresponding to the plurality of question items. A storage means storing an analysis table, an analysis means for analyzing the answer results of the questionnaire, and an output means for outputting the analysis results by the analysis means are provided. The analysis means multiplies the answer score based on the answer results of the question items by the score for each diagnostic element set in the analysis table corresponding to the question items to calculate an individual element evaluation value for each diagnostic element of each question item, totals the individual element evaluation values of all questions for each diagnostic element to calculate an individual element total value, and the output means totals the individual element total values for each level and outputs them in a graph or table.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the prior art, as a result of aggregating and analyzing the questionnaire results, information that can be used to judge the soundness of management was presented, and it was not possible to predict the score of an organization using the daily information of the organization's members.

Means for Solving the Problem

[0006] The score prediction device of the first invention includes a daily information storage unit that stores one or more prediction daily information, which is daily information that can be obtained in the daily life of two or more members of an organization and is daily information during the period for which the score of the organization is to be predicted; a teacher score, which is the score of the organization obtained using a plurality of organization response information indicating the answers to questions for two or more members of the organization; a score acquisition unit that obtains a prediction score, which is the score corresponding to one or more prediction daily information, using the correspondence relationship between one or more teacher daily information, which is daily information during the period corresponding to the question or answer and is the daily information of two or more members of the organization, and one or more prediction daily information; and a score output unit that outputs the prediction score.

[0007] With such a configuration, it is possible to predict the score of an organization using the daily information of the organization's members.

[0008] Further, the score prediction device of the second invention, compared with the first invention, is such that one or more pieces of teacher daily information include teacher business information related to the business performed by the members, one or more pieces of prediction daily information include prediction business information related to the business performed by the members, and the score acquisition unit obtains a prediction score using the correspondence relationship between one or more pieces of teacher daily information including teacher business information and the teacher score, and one or more pieces of prediction daily information including prediction business information.

[0009] With such a configuration, it is possible to predict the score of an organization using the business information related to the business performed by the members of the organization.

[0010] In addition, for the score prediction device of the third invention, for the second invention, the teacher operation information includes teacher input information input by the organizational members, the prediction operation information includes prediction input information input by the organizational members, and the score acquisition unit is a score prediction device that acquires a prediction score using the correspondence relationship between one or more teacher daily information including the teacher input information and the teacher score, and one or more prediction daily information including the prediction input information.

[0011] With such a configuration, the score of the organization can be predicted using the input information input by the organizational members.

[0012] In addition, for the score prediction device of the fourth invention, for the third invention, the teacher input information includes teacher operation input information related to the input for the performance of the members' own operations, the prediction input information includes prediction operation input information related to the input for the performance of the members' own operations, and the score acquisition unit is a score prediction device that acquires a prediction score using the correspondence relationship between one or more teacher daily information including the teacher operation input information and the teacher score, and one or more prediction daily information including the prediction operation input information.

[0013] With such a configuration, the score of the organization can be predicted using the teacher operation input information related to the input for the performance of the members' own operations in the organization.

[0014] In addition, for the score prediction device of the fifth invention, for the second or third invention, the teacher input information includes teacher communication information related to the communication between two or more organizational members, the prediction input information includes prediction communication information related to the communication between two or more organizational members, and the score acquisition unit is a score prediction device that acquires a prediction score using the correspondence relationship between one or more teacher daily information including the teacher communication information and the teacher score, and one or more prediction daily information including the prediction communication information.

[0015] With such a configuration, the score of the organization can be predicted using the communication information related to the communication between the organizational members.

[0016] In addition, for the score prediction device of the sixth invention, for any one of the third to fifth inventions, the score acquisition unit acquires one or more types of information from among data amount information for specifying the data amount of teacher input information, input frequency information for specifying the input frequency of teacher input information, analysis information indicating the analysis result of teacher input information, and timing information for specifying the input timing of teacher input information, and also acquires one or more types of information from among data amount information of prediction input information, input frequency information of prediction input information, analysis information of prediction input information, and timing information of prediction input information, and acquires correspondence relationship information for specifying the correspondence relationship between one or more teacher features including one or more types of information acquired from teacher input information and the teacher score, and uses the correspondence relationship information and one or more prediction features including one or more types of information acquired from the prediction input information to acquire a prediction score.

[0017] With such a configuration, the score of the organization can be predicted using one or more types of information from among 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.

[0018] In addition, for the score prediction device of the seventh invention, for the second invention, the teacher work information includes teacher attendance situation information regarding the attendance of the constituent members, the prediction work information includes prediction attendance situation information regarding the attendance of the constituent members, and the score acquisition unit uses the correspondence relationship between one or more teacher daily information including the teacher attendance situation information and the teacher score, and one or more prediction daily information including the prediction attendance situation information to acquire a prediction score.

[0019] With such a configuration, the score of the organization can be predicted using the attendance situation information of the constituent members of the organization.

[0020] In addition, for the score prediction device of the eighth invention of the present application, with respect to the second invention, the teacher work information includes teacher work result information regarding the work performance results of the organizational members, the predicted work information includes predicted work result information regarding the work performance results of the organizational members, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence relationship between one or more pieces of teacher daily information including the teacher work result information and the teacher score, and one or more pieces of predicted daily information including the predicted work result information.

[0021] With such a configuration, it is possible to predict the score of the organization using the work result information regarding the work performance results of the organization.

[0022] In addition, for the score prediction device of the ninth invention of the present application, with respect to the first invention, each of the one or more pieces of teacher daily information includes teacher health information regarding the health of the organizational members, each of the one or more pieces of predicted daily information includes predicted health information regarding the health of the organizational members, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence relationship between one or more pieces of teacher daily information including the teacher health information and the teacher score, and one or more pieces of predicted daily information including the predicted health information.

[0023] With such a configuration, it is possible to predict the score of the organization using the health information of the organizational members.

[0024] In addition, for the score prediction device of the tenth invention of the present application, with respect to the ninth invention, the teacher health information includes teacher biological information regarding the living body of the organizational members, the predicted health information includes predicted biological information regarding the living body of the organizational members, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence relationship between one or more pieces of teacher daily information including the teacher biological information and the teacher score, and one or more pieces of predicted daily information including the predicted biological information.

[0025] With such a configuration, it is possible to predict the score of the organization using the biological information of the organizational members.

[0026] Further, in the score prediction device of the eleventh invention, for the tenth invention, the teacher biological information includes teacher stress information regarding the stress of the organizational members, the predicted biological information includes predicted stress information regarding the stress of the organizational members, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence between one or more pieces of teacher daily information including the teacher stress information and the teacher score, and one or more pieces of predicted daily information including the predicted stress information.

[0027] With such a configuration, the score of the organization can be predicted using the stress information of the organizational members.

[0028] Further, in the score prediction device of the twelfth invention, for the tenth or eleventh invention, the teacher biological information includes teacher measurement information which is the measurement result of the biological bodies of the organizational members, the predicted biological information includes predicted measurement information acquired from the biological bodies of the organizational members, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence between one or more pieces of teacher daily information including the teacher measurement information and the teacher score, and one or more pieces of predicted daily information including the predicted measurement information.

[0029] With such a configuration, the score of the organization can be predicted using the measurement information which is the measurement result of the biological bodies of the organizational members.

[0030] Further, in the score prediction device of the thirteenth invention, for any one of the tenth to twelfth inventions, the teacher biological information includes teacher biological feature information which is an image of the organizational member, or a feature amount of the image of the organizational member, or the voice emitted by the organizational member, or a feature amount of the voice emitted by the organizational member, the predicted biological information includes predicted biological feature information which is an image of the organizational member, or a feature amount of the image of the organizational member, or the voice emitted by the organizational member, or a feature amount of the voice emitted by the organizational member, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence between one or more pieces of teacher daily information including the teacher biological feature information and the teacher score, and one or more pieces of predicted daily information including the predicted biological feature information.

[0031] With such a configuration, the score of the organization can be predicted using the image of the organizational member or the voice of the organizational member.

[0032] In addition, for the score prediction device of the fourteenth invention, for any one of the ninth to thirteenth inventions, the teacher health information includes teacher life information regarding the life of the organization members, the predicted health information includes predicted life information regarding the life of the organization members, and the score acquisition unit is a score prediction device that acquires a predicted score using the correspondence relationship between one or more teacher daily information including teacher life information and the teacher score, and one or more predicted daily information including predicted life information.

[0033] With such a configuration, the score of the organization can be predicted using the life information of the organization members.

[0034] In addition, for the score prediction device of the fifteenth invention, for any one of the first to fourteenth inventions, the score acquisition unit includes a correspondence acquisition means for acquiring correspondence relationship information for specifying the correspondence relationship between one or more teacher daily information and the teacher score, and a score acquisition means for acquiring a predicted score using the correspondence relationship information and one or more predicted daily information.

[0035] With such a configuration, the score of the organization can be predicted using the daily information of the organization members.

[0036] In addition, for the score prediction device of the sixteenth invention, for the fifteenth invention, one or more teacher daily information and the teacher score are associated with one or more organization attribute values. The correspondence acquisition means acquires correspondence relationship information for specifying the correspondence relationship between one or more teacher daily information and the teacher score for each organization condition constituted by using one or more organization attribute values. The score acquisition means acquires a predicted score using the correspondence relationship information corresponding to the organization condition that the organization to be predicted matches and one or more predicted daily information.

[0037] With such a configuration, a predicted score that matches the attribute value of the organization can be acquired.

[0038] In addition, for the score prediction device of the seventeenth invention, for the fifteenth or sixteenth invention, the correspondence acquisition means performs learning processing by a machine learning algorithm using one or more teacher daily information and teacher scores, and uses one or more teacher daily information as explanatory variables and the teacher score as the target variable to obtain correspondence relationship information which is a learning model, and the score acquisition means uses the learning model and one or more prediction daily information to perform prediction processing by a machine learning algorithm and obtain a predicted score, which is a score prediction device.

[0039] With such a configuration, using machine learning technology, the score of an organization can be predicted using the daily information of the organization's members.

[0040] In addition, for the score prediction device of the eighteenth invention, for the fifteenth or sixteenth invention, the correspondence acquisition means uses one or more teacher daily information to obtain a teacher vector which is a vector having two or more elements, and obtains correspondence relationship information which is a correspondence table having one or more correspondence information which is a pair of the teacher vector and the teacher score, and the score acquisition means uses one or more prediction daily information to obtain a prediction vector which is a vector having two or more elements, applies the prediction vector to the correspondence table, and obtains a predicted score, which is a score prediction device.

[0041] With such a configuration, using data mining technology, the score of an organization can be predicted using the daily information of the organization's members.

[0042] In addition, for the score prediction device of the nineteenth invention, for the fifteenth or sixteenth invention, the correspondence acquisition means uses one or more teacher daily information to obtain a teacher vector which is a vector having two or more elements, and obtains correspondence relationship information which is an arithmetic expression taking each element of the teacher vector as an input and the teacher score as an output, and the score acquisition means uses one or more prediction daily information to obtain a prediction vector which is a vector having two or more elements, gives each element of the prediction vector to the arithmetic expression, executes the arithmetic expression, and obtains a predicted score, which is a score prediction device.

[0043] With such a configuration, the score of an organization can be predicted using data analysis technology and the daily information of the organization's members.

Advantages of the Invention

[0044] According to the score prediction device of the present invention, the score of an organization can be predicted using the daily information of the organization's members.

Brief Description of the Drawings

[0045]

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Modes for Carrying Out the Invention

[0046] Hereinafter, embodiments of a score prediction device and the like will be described with reference to the drawings. In the embodiments, components denoted by the same reference numerals perform the same operations, so the description may be omitted again.

[0047] (Embodiment 1) In this embodiment, an information system including an information processing apparatus that acquires and outputs the influence degree of each item with respect to the overall score of an organization calculated from responses to two or more items will be described. Note that the organization is, for example, a so-called company, individual business, local public entity, etc., and is broadly interpreted. The organization is, for example, an organization that performs some kind of work. Also, the overall score of the organization may be referred to as an engagement score. Also, the information system may be referred to as an engagement system. Also, the information processing apparatus may be referred to as an engagement apparatus.

[0048] Also, in this embodiment, an information system including an information processing apparatus that calculates an item score and the overall score of an organization using the degree of expectation of each item will be described. Note that the information system may be referred to as an engagement system.

[0049] Also, in this embodiment, an information system including an information processing apparatus having a score adjustment function for adjusting an overall score using correlation information regarding the correlation between satisfaction and expectation will be described.

[0050] Furthermore, in this embodiment, an information system including an information processing apparatus that considers the attribute value of an organization and outputs different influence degrees for each attribute value will be described.

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

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

[0053] The information processing apparatus 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 comprehensive score acquisition unit 132, and an influence degree acquisition unit 133. The output unit 14 includes an item score output unit 141, a comprehensive score output unit 142, and an influence degree output unit 143.

[0054] The terminal device 2 includes a terminal storage unit 21, a terminal reception unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal reception unit 25, and a terminal output unit 26.

[0055] Various types of information are stored in the storage unit 11 that constitutes the information processing apparatus 1. The various types of information are, for example, item information to be described later, organization response information to be described later, and an individual score table to be described later. Note that the individual score table may also be referred to as an engagement score table or the like.

[0056] Two or more pieces of item information are stored in the item information storage unit 111. The item information is information regarding the items of the organization. The item may be a question regarding the organization. Also, the item may be associated with a question regarding the organization. The two or more items include, for example, comprehensive items regarding comprehensive matters of the organization and individual items that are individual items of the organization. Note that the comprehensive items are usually items for questions with a high degree of abstraction. Also, the individual items are usually items for questions with a lower degree of abstraction (more specific questions). Also, the two or more pieces of item information in the item information storage unit 111 have, for example, item information for four comprehensive items and item information for 64 individual items. Also, the item corresponds to, for example, any one of two or more targets. The target may also be referred to as a factor. Also, the target may be referred to as a matter related to the organization. Also, the item information has, for example, an item identifier for identifying the item and question information. Also, the item identifier is, for example, an ID, an item name, or the like. The item identifier may also be the question information itself. The question information is information indicating the question. Note that the question is usually a questionnaire question. Note that the matter may also be referred to as a target or an item.

[0057] In the organizational response information storage unit 112, the organizational response information of two or more organizations is stored. One piece of organizational response information is a set of the response information of two or more members of one organization. One piece of organizational response information has two or more member response information corresponding to two or more members. Member response information is information including the result of a member's answer to a question about an item. Member response information has two or more item response information. Member response information usually has item response information corresponding to the number of items. Item response information has an item identifier and response information. Response information is information regarding the answer to a question. Response information has, for example, satisfaction information. Satisfaction information is information indicating the degree of satisfaction of a member with respect to an item. Satisfaction information is, for example, information specifying the satisfaction degree with respect to an item. Satisfaction information is classified into two or more classes. Satisfaction information can take, for example, any natural number from 1 to 5. However, satisfaction information can also be an evaluation value having a rank or order, such as A, B, C, for example, or any natural number from 1 to 100. Further, response information has, for example, satisfaction information and expectation information. Expectation information is information indicating the degree of expectation of a member with respect to an item. Expectation information is, for example, information specifying the expectation degree with respect to an item. Expectation information is classified into two or more classes. Expectation information can take, for example, any natural number from 1 to 5. However, expectation information can also be an evaluation value having a rank or order, such as A, B, C, for example, or any natural number from 1 to 100. Note that the members are, for example, employees of a company, school staff, office staff, etc., but may also include officers of a company.

[0058] Note that comprehensive items are, for example, company satisfaction indicating the degree of satisfaction with the company, job satisfaction indicating the degree of satisfaction with the job, supervisor satisfaction indicating the degree of satisfaction with the supervisor, workplace satisfaction indicating the degree of satisfaction with the workplace, etc. Further, individual items are, for example, the business superiority of the company itself, the dissemination and transmission of strategic goals, the overall sense of unity, the validity of evaluation and salary, etc.

[0059] In addition, the organizational response information is usually associated with an organizational identifier. The organizational identifier is the organization name, an ID for identifying the organization, and the like. Also, it is preferable that the organizational response information is associated with one or more attribute values of the organization. The attribute values are, for example, industry identifiers indicating the industry of the organization (e.g., bank, apparel, manufacturer, etc.), scale identifiers classifying the scale of the organization (e.g., large enterprise, small and medium-sized enterprise, individual business, etc.), regional identifiers indicating the region of the head office, and the like.

[0060] The individual score table storage unit 113 stores an individual score table. Here, the individual score table is information for determining an item score 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, with individual scores described in each cell of the table, and when the satisfaction information and the expectation information are determined, the item score is determined. It is preferable that such an individual score table is a table in which a larger item score is obtained as the satisfaction indicated by the satisfaction information is higher, and a larger item score is obtained as the expectation indicated by the expectation information is lower.

[0061] In addition, when determining the item score, it may be determined by an arithmetic expression using the satisfaction information and the expectation information as parameters. It is preferable that such an arithmetic expression is an increasing function using the satisfaction information as a parameter and a decreasing function using the expectation information as a parameter.

[0062] Also, when determining the item score, a plurality of sets of satisfaction information, expectation information, and item scores may be learned by machine learning, and the obtained learning information may be used. In such a case, it is applied to the satisfaction information, the expectation information, and the learning information, and the item score is obtained by machine learning. Here, for example, SVR, deep learning, decision trees, etc. can be used for the machine learning here. However, the algorithm of the machine learning is not limited.

[0063] The reception unit 12 receives various types of information and instructions. The various types of information and instructions include, for example, score output instructions, organizational response information, questionnaire response information (member response information), etc. The score output instruction is an instruction to output scores and the like. Here, reception generally refers to reception from the terminal device 2, but it can also be understood as a concept that includes reception of information input from input devices such as keyboards, mice, touch panels, and reception of information read from recording media such as optical disks, magnetic disks, and semiconductor memories.

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

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

[0066] The item score acquisition unit 131, for example, statistically processes the satisfaction information included in the response information of each of the two or more items included in the organizational response information of each organization, and acquires item scores for each organization and each item. The item score acquisition unit 131, for example, calculates the average value of the satisfaction information for each organization and each item, and accumulates such an average value as an item score in a buffer (not shown) or the storage unit 11 in a pair with an item identifier. Also, the item score acquisition unit 131, for example, performs different weightings according to the attribute values of the members for each organization and each item, calculates the weighted average value of the satisfaction information, and accumulates such a weighted average value as an item score in a buffer (not shown) or the storage unit 11 in a pair with an item identifier. Note that the attribute values of the members are, for example, job positions, years of service, gender, etc. For example, the item score acquisition unit 131 may compare the satisfaction information of employees with a long years of service with the satisfaction information of employees with a short years of service, increase the weight, and calculate the weighted average.

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

[0068] For example, it is preferable for the item score acquisition unit 131 to acquire item scores such that the higher the satisfaction information, the higher the score, and the lower the expectation information, the higher the score.

[0069] For example, for each of two or more organizations and for each of two or more items, the item score acquisition unit 131 calculates the average value of the satisfaction information and the average value of the expectation information included in the response information, and uses the two average values to acquire item scores for each item.

[0070] For example, the item score acquisition unit 131 may apply the satisfaction information and the expectation information to an individual score table to acquire item scores for each item. Also, for example, the item score acquisition unit 131 may apply the statistical processing results of the satisfaction information and the statistical processing results of the expectation information to an individual score table to acquire item scores for each item. The item score acquisition unit 131 may, for example, apply the average value of the satisfaction information and the average value of the expectation information to an individual score table to acquire item scores for each item. The item score acquisition unit 131 may, for example, apply the weighted average value of the satisfaction information and the weighted average value of the expectation information to an individual score table to acquire item scores for each item. Note that the weighted average value is a weighted average value based on the attribute values of the constituent members.

[0071] For example, the item score acquisition unit 131 may calculate item scores for each item using an increasing function with the average value of the satisfaction information as a parameter and a decreasing function with the average value of the expectation information as a parameter.

[0072] The overall score acquisition unit 132 acquires an overall score for each of two or more pieces of organizational response information by using two or more item scores. The overall score is an overall score for each organization. Usually, the overall score acquisition unit 132 acquires a higher overall score as the two or more item scores are better scores.

[0073] It is preferable that the overall score acquisition unit 132 acquires the overall score by using a score adjustment function as follows. The score adjustment function is a function that adjusts scores by using correlation information regarding the degree of correlation between satisfaction information and expectation information. Here, it is preferable that the score adjustment function results in a higher overall score as the correlation between the satisfaction information and the expectation information is greater.

[0074] For example, the overall score acquisition unit 132 acquires a provisional overall score, which is a provisional overall score for each organization, by using two or more item scores for each organization, acquires correlation information regarding the degree of correlation between the satisfaction information and the expectation information for each of the two or more items, and uses the correlation information to acquire the overall score from the provisional overall score such that the score increases as the degree of correlation increases. Note that the correlation information may be a correlation value between the set of satisfaction information for two or more items and the set of expectation information, or may be a value calculated by an arithmetic expression that is an increasing function with the number of items where the difference between the satisfaction information for each of the two or more items and the expectation information for each of the two or more items is equal to or less than a threshold value as a parameter, or may be a value calculated by an arithmetic expression that is a decreasing function with the number of items where the difference between the satisfaction information for each of the two or more items and the expectation information for each of the two or more items is equal to or less than a threshold value and the satisfaction information is smaller as a parameter. That is, the algorithm for acquiring the correlation information is not limited.

[0075] It is preferable that the overall score acquisition unit 132 acquires the overall score by using both the item score for the comprehensive item and the item score for the individual item, and by comparing the item score for the comprehensive item with the item score for the individual item and increasing the weight.

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

[0077] Also, the comprehensive score acquisition unit 132 may calculate a provisional comprehensive score, for example, by "Provisional comprehensive score = α × Statistical score of item scores for comprehensive items + β × Statistical score of item scores for individual items", and further perform score adjustment by the above score adjustment function to calculate the comprehensive score. Furthermore, the comprehensive score acquisition unit 132 may calculate the deviation value of the comprehensive score of each organization using the comprehensive scores of a plurality of organizations, and use such deviation value as the final comprehensive score.

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

[0079] The influence degree acquisition unit 133 acquires, for each item, the influence degree that the item score of each item of two or more organizations has on the comprehensive score using the item scores of two or more items of two or more organizations and the comprehensive scores of two or more organizations. The influence degree is, for example, information indicating the influence that the item score has on the comprehensive score, and is the correlation degree of the set of item scores of a plurality of organizations with respect to the set of comprehensive scores of the plurality of organizations. Since the process of acquiring the correlation degree, correlation value, and correlation information is a known technique, detailed description is omitted.

[0080] The influence degree acquisition unit 133 calculates, for each item, an influence degree, which is information regarding the correlation between the item score and the overall score of each item, using the overall scores of two or more organizations and the item scores of each item of two or more organizations.

[0081] The influence degree acquisition unit 133 preferably acquires, for each item, the influence degree that the item score of each item has on the overall score 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.

[0082] The output unit 14 outputs various types of information. The various types of information are, for example, a set of information pairs of item identifiers and item scores. Also, the various types of information are, for example, the overall score. Also, the various types of information are, for example, a set of information pairs of item identifiers and influence degrees. Here, output usually means transmission to an external device such as the terminal device 2. However, output may be considered a concept that includes display on a display, projection using a projector, printing by a printer, sound output, storage on a recording medium, delivery of a processing result to another processing device or another program, etc.

[0083] The item score output unit 141 outputs one or more item scores. The item score output unit 141 usually outputs the item score of the item identified by the item identifier in association with the item identifier. The item score output unit 141 outputs, for example, the item score paired with the organization identifier included in the score output instruction, etc., 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. Also, it is preferable that the item score output unit 141 outputs the item scores in a manner that can visually distinguish the item scores of general items and the item scores of individual items.

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

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

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

[0087] The terminal storage unit 21 that constitutes the terminal device 2 stores various types of information. The various types of information are, for example, an organization identifier that identifies the organization of the user. The various types of information are, for example, the information received by the terminal reception unit 25. Note that the organization identifier may be considered as information for identifying the user.

[0088] The terminal reception unit 22 receives various instructions, information, etc. Here, "receiving" includes receiving information input from an input device such as a keyboard, mouse, or touch panel, receiving information transmitted via a wired or wireless communication line, and receiving information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory. The various instructions, information, etc. are, for example, score output instructions, organization response information, questionnaire response information, etc.

[0089] The input means for various instructions, information, etc. can be anything, such as a touch panel, keyboard, mouse, or menu screen. The terminal reception unit 22 can be realized by a device driver for an input means device such as a touch panel or keyboard, or control software for a menu screen.

[0090] The terminal processing unit 23 performs various processes, which are, for example, processes for configuring the information received by the terminal reception unit 25 into data to be displayed. The various processes are, for example, processes for configuring instructions received by the terminal reception unit 22 into instructions to be transmitted.

[0091] The terminal transmission unit 24 transmits various instructions, information, etc. to the information processing apparatus 1. The various instructions, information, etc. are, for example, instructions configured by the terminal processing unit 23, instructions and information, etc. received by the terminal reception unit 22.

[0092] The terminal reception unit 25 receives various information from the information processing apparatus 1. The various information is, for example, item scores, overall scores, and degrees of influence.

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

[0094] The storage unit 11 preferably uses a non-volatile recording medium, but can also be realized with a volatile recording medium.

[0095] The process by which information is stored in the storage unit 11, item information storage unit 111, organization response information storage unit 112, individual score table storage unit 113, and terminal storage unit 21 is not limited. For example, information may be stored in the storage unit 11 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 11 etc., or information input via an input device may be stored in the storage unit 11 etc.

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

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

[0098] The output unit 14, item score output unit 141, overall score output unit 142, impact degree output unit 143, and terminal transmission unit 24 are usually realized by wireless or wired communication means, but may also be realized by broadcast means.

[0099] The terminal output unit 26 may or may not be considered to include output devices such as displays and speakers. The terminal output unit 26 can be realized by the driver software of the output device or the driver software of the output device and the output device, etc.

[0100] Next, the operation of the information system A will be described. First, the operation example of the information processing apparatus 1 will be described using the flowchart of FIG. 3. It is assumed that the organization response information storage unit 112 stores the organization response information of a plurality of organizations. Also, it is assumed that the individual score table storage unit 113 stores an individual score table.

[0101] (Step S301) The processing unit 13 determines whether it is the timing for calculating scores and the like. If it is the timing for calculating scores and the like, it proceeds to step S302, and if it is not the timing for calculating scores and the like, it proceeds to step S312. Note that the timing for calculating scores and the like is, for example, when an instruction is input from a user, administrator, etc., when a predetermined timing is reached, when two or more pieces of organization response information are received and accumulated in the organization response information storage unit 112, etc.

[0102] (Step S302) The processing unit 13 substitutes 1 into the counter i.

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

[0104] (Step S304) The processing unit 13 acquires two or more pieces of organization response information or the like that correspond to the i-th attribute value from the organization response information storage unit 112. The organization response information or the like is, for example, an organization identifier and organization response information. The organization response information or the like is, for example, an organization identifier, the i-th attribute value, and organization response information.

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

[0106] (Step S306) The processing unit 13 determines whether the j-th organization identifier exists among the two or more pieces of organization response information or the like acquired in Step S304. If the j-th organization identifier exists, it proceeds to Step S307, and if the j-th organization identifier does not exist, it proceeds to Step S310.

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

[0108] (Step S308) The processing unit 13 calculates various scores corresponding to the j-th organization identifier using the organization response information acquired in Step S307. Note that the calculation of the score is considered to have the same meaning as the acquisition of the score. Also, the score calculation process will be described using the flowchart of FIG. 4.

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

[0110] (Step S310) The influence degree acquisition unit 133 calculates the influence degree of each item. Such influence degree calculation processing will be described using the flowchart of FIG. 5.

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

[0112] (Step S312) The reception unit 12 determines whether it has received a score or other output instruction. If it has received a score or other output instruction, it proceeds to step S313; if it has not received a score or other output instruction, it returns to step S301.

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

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

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

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

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

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

[0119] (Step S319) The output unit 14 outputs the information configured in step S318. Return to step S301. Note that the output here usually means transmission to the terminal device 2 that has sent an output instruction such as a score. Also, assume that the information to be output includes at least a pair of an item identifier and an influence degree. Further, it is preferable that the information to be output includes a pair of an item identifier and an item score, and a comprehensive score.

[0120] Note that in the flowchart of FIG. 3, the storage route of the organization response information to the organization response information storage unit 112 is not limited.

[0121] Also, in the flowchart of FIG. 3, the influence degree was obtained for each attribute value of the organization. However, it goes without saying that the influence degree of each item may be obtained using the organization response information of all organizations regardless of the attribute value of the organization.

[0122] Furthermore, in the flowchart of FIG. 3, the process ends due to a power-off or a processing end interrupt.

[0123] Next, an example of the score calculation process in step S308 will be described using the flowchart of FIG. 4.

[0124] (Step S401) The item score acquisition unit 131 substitutes 1 for the counter i.

[0125] (Step S402) The item score acquisition unit 131 determines whether 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, go to step S403; if it does not exist, go to step S409.

[0126] (Step S403) The item score acquisition unit 131 acquires the satisfaction information of all members paired with the item identifier of the i-th individual item in the organization response information acquired in step S307.

[0127] (Step S404) The item score acquisition unit 131 statistically processes the satisfaction information acquired in step S403 and calculates statistical satisfaction information. Here, the item score acquisition unit 131 calculates, for example, the statistical satisfaction information that is the average value of the 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 association with the item identifier of the i-th individual item.

[0128] (Step S405) The item score acquisition unit 131 acquires the expectation information of all members corresponding to the item identifier of the i-th individual item among the organizational response information acquired in step S307.

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

[0130] (Step S407) The item score acquisition unit 131 acquires the item score of the i-th individual item of the organization of interest 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 in the individual score table storage unit 113 to acquire the item score of the i-th individual item. Then, the item score acquisition unit 131 stores the acquired item score in the storage unit 11 or a buffer (not shown) in association with the item identifier of the i-th individual item.

[0131] (Step S408) The item score acquisition unit 131 increments the counter i by 1. Return to step S402.

[0132] (Step S409) The item score acquisition unit 131 substitutes 1 for the counter j.

[0133] (Step S410) The item score acquisition unit 131 determines whether 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, it proceeds to step S411; if not, it proceeds to step S414.

[0134] (Step S411) The item score acquisition unit 131 acquires the satisfaction information of all the members that pairs with the item identifier of the j-th comprehensive item in the organization response information acquired in step S307.

[0135] (Step S412) The item score acquisition unit 131 statistically processes the satisfaction information acquired in step S411 and calculates the statistical satisfaction information. Here, for example, the item score acquisition unit 131 calculates the statistical satisfaction information as the average value of the 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 j-th comprehensive item.

[0136] (Step S413) The item score acquisition unit 131 increments the counter j by 1. It returns to step S410.

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

[0138] (Step S415) The comprehensive score acquisition unit 132 acquires the comprehensive score of the individual items from all the item scores acquired in step S414. For example, the comprehensive 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 the individual items.

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

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

[0141] (Step S418) The comprehensive score acquisition unit 132 calculates a provisional comprehensive 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. Note that the comprehensive score acquisition unit 132 calculates a provisional comprehensive score, for example, according to the arithmetic formula "provisional comprehensive score = α × statistical satisfaction information of comprehensive items + β × comprehensive score of individual items".

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

[0143] (Step S420) The comprehensive score acquisition unit 132 adjusts the provisional comprehensive score acquired in Step S418 using the correlation information acquired in Step S419, and acquires the comprehensive score. Return to the upper-level process. Note that the comprehensive score acquisition unit 132 acquires the comprehensive score such that the higher the degree of correlation indicated by the correlation information, the higher the comprehensive score.

[0144] Next, an example of the influence degree calculation process in Step S310 will be described with reference to the flowchart of FIG. 5.

[0145] (Step S501) The influence degree acquisition unit 133 substitutes 1 into the counter i.

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

[0147] (Step S503) The influence degree acquisition unit 133 acquires the item scores of a plurality of organizations that are paired with the item identifier of the i-th individual item from among two or more pieces of organizational response information and the like acquired in step S304.

[0148] (Step S504) The influence degree acquisition unit 133 acquires the comprehensive scores of a plurality of organizations from among two or more pieces of organizational response information and the like acquired in step S304.

[0149] (Step S505) The influence degree acquisition unit 133 acquires the influence degree indicating the degree of correlation between the plurality of item scores acquired in step S503 and the plurality of comprehensive scores acquired in step S504. Then, the influence degree acquisition unit 133 stores the acquired influence degree in the storage unit 11 or in a buffer (not shown) in pair with the item identifier of the i-th individual item.

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

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

[0152] 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 shown in FIG. 1.

[0153] Now, assume that the item information storage unit 111 stores the item information management table shown in FIG. 6. The item information management table is a table that manages a large number of item information indicating the items of a questionnaire for the members (here, employees) of an organization (here, a company). The item information here has "Question No", "Type", "Factor", "Item", "Question: Expectation Degree", and "Question: Satisfaction Degree". "Question No" is an ID for identifying a question and is an example of an item identifier. "Type" is information indicating the type of an item, and here it can be either a comprehensive item or an individual item. "Factor" is the middle concept of an item and can also be referred to as the object. "Item" is information indicating the content of an item. Note that "Item" may be considered as an item identifier. "Question: Expectation Degree" is a question for obtaining expectation degree information. "Question: Satisfaction Degree" is a question for obtaining satisfaction degree information.

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

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

[0156] In such a situation, assume that the administrator of the information processing apparatus 1 inputs an instruction to calculate a score or the like. Then, the reception unit 12 receives the instruction to calculate a score or the like. Next, the processing unit 13 determines that it is the timing to calculate a score or the like.

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

[0158] Next, starting from "Company A", the processing unit 13 calculates the item score and the total score for each item using the organization response information that pairs with the organization identifier that pairs with the attribute value "manufacturer". Hereinafter, taking "Company A" as an example, the calculation of the item score and the total score will be described.

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

[0160] Next, the item score acquisition unit 131 acquires the satisfaction information of all constituent 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 accumulates the statistical satisfaction information of each comprehensive item in a buffer in pairs with the item identifiers of each comprehensive item.

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

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

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

[0164] Next, the comprehensive score acquisition unit 132 obtains correlation information regarding the correlation between the set of satisfaction information for all individual items and the set of expectation information for all individual items.

[0165] Next, the comprehensive score acquisition unit 132 adjusts the obtained provisional comprehensive score using the obtained correlation information. Also, the comprehensive score acquisition unit 132 obtains a deviation value using the adjusted value of the provisional comprehensive score of other companies. Such a deviation value is the comprehensive score. Here, assume that the comprehensive score acquisition unit 132 calculates the comprehensive score of the organization identified by the organization identifier "Company A" as "68.0". Then, the comprehensive score acquisition unit 132 accumulates the comprehensive score "68.0" in the buffer in association with the organization identifier "Company A". Note that the calculation of the adjusted value of the provisional comprehensive score of other companies is performed in the same manner as the calculation of the value of "Company A".

[0166] Then, the above processing is also performed on other organization response information paired with the attribute value "manufacturer". And assume that for all organization response information (organizations) paired with the attribute value "manufacturer", the item scores of each individual item and the comprehensive score are calculated and at least temporarily accumulated in the buffer.

[0167] Next, the influence degree acquisition unit 133 obtains the item scores from the buffer for each individual item, paired with the organization identifier paired with the attribute value "manufacturer". That is, the influence degree acquisition unit 133 obtains the item scores from the buffer for the number of organization identifiers. Next, the influence degree acquisition unit 133 obtains the comprehensive score paired with the organization identifier paired with the attribute value "manufacturer" from the buffer.

[0168] Next, the influence degree acquisition unit 133 calculates the correlation degree between the set of the acquired multiple item scores and the set of the acquired multiple overall scores for each individual item. Note that the correlation degree may be a correlation coefficient. Then, the influence degree acquisition unit 133 accumulates, in a buffer, the influence degree, which is the correlation degree, in pairs with the item identifier for each individual item. Note that such information has a structure of "item identifier, influence degree", and here, for example, it is assumed to be "influence within the industry, 0.84", "stability of customer base, 0.78", "topicability and popularity, 0.65", "soundness of financial situation, 0.86", "dissemination and transmission of concept, 0.50", "penetration degree of concept at the site, 0.82", "dissemination and transmission of strategic goal, 0.78", "sense of acceptance of strategic goal, 0.50", etc.

[0169] In addition, the above processing is also performed on the organizational response information corresponding to other attribute values (for example, "trading company" or "bank", etc.).

[0170] And it is assumed that for each company, the item scores and the overall scores are accumulated, and here, for each attribute value of the organization, the influence degree for each item is accumulated.

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

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

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

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

[0175] Next, the processing unit 13 acquires from the storage unit 11 or a buffer (not shown) two or more degrees of influence that pair with the acquired attribute value "manufacturer", and item identifiers that pair with the degrees of influence.

[0176] Next, the processing unit 13 constructs the information to be output from the acquired information. Then, the output unit 14 outputs the constructed information. Such an output example is shown in FIG. 9. In FIG. 9, 901 is the comprehensive score "68.0" of Company A. Also, 902 is the item score of each comprehensive item. Here, the comprehensive items are company, supervisor, work, and workplace. Also, 903 is the item score and degree of influence of individual items. Note that FIG. 10 is an enlarged view of the area of 903. In FIG. 10, 1001 is the average value of the expected degree information of individual items. 1002 is the average value of the satisfaction information of individual items. The columns of 1003 are the degrees of influence of each individual item. Note that in FIG. 10, as can be seen from 1004 to 1006, the degree of influence output unit 143 outputs the degrees of influence in association with each item so that the classification of the degrees of influence can be visually distinguished. That is, here, for example, when the degree of influence is 0.8 or more, the darkest background color is adopted, when the degree of influence is 0.6 or more and less than 0.8, an ordinary darkness background color is adopted, and when the degree of influence is less than 0.6, a light background color is adopted, thereby outputting the classification of the degrees of influence so that it can be visually distinguished. Note that the method of outputting the classification of the degrees of influence so that it can be visually distinguished is not limited to the background color, and it goes without saying that various attribute values such as font and size can be changed.

[0177] As described above, according to the present embodiment, for an organization, important items can be easily grasped.

[0178] Also, 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.

[0179] Furthermore, according to the present embodiment, an engagement score can be obtained, and an information processing apparatus 1 that can be used as a so-called engagement apparatus can be provided.

[0180] In addition, according to a specific example of the present embodiment, the degree of influence was obtained for each attribute value of the organization. However, the degree of influence for each item may be obtained regardless of the attribute value of the organization. The technique of obtaining the degree of influence for each attribute value of the organization focuses on the fact that the characteristics of the company differ for each attribute value of the organization. And such a technique is, for example, an extremely useful technique in engagement where employees try to spontaneously exert their own power towards achieving the organization's goals, and both the employees and the organization grow.

[0181] Also, regarding the above-mentioned "satisfaction" of the questionnaire items, similar terms such as acceptance and current situation are also included, and needless to say, regarding "expectation", similar terms such as the degree of expectation, importance, and significance are also included.

[0182] In addition, the processing in this embodiment may be realized by software. And this software may be distributed by software download or the like. Also, this software may be recorded on a recording medium such as a CD-ROM and distributed. Note that this also applies to other embodiments in this specification. The software that realizes the information processing apparatus 1 in this embodiment is a program as follows. That is, this program includes, for example, item response information having item identifiers that identify items related to an organization and response information including satisfaction information that is a response regarding the degree of satisfaction of members of the organization with respect to the items, for two or more item response information for each of two or more members belonging to the organization. The program causes a computer capable of accessing a recording medium having an organization response information storage unit that stores, for each of two or more organizations, the organization response information including the response information for each of the members of the two or more items included in the two or more organization response information, to statistically process the response information for each of the members of the two or more items included in the two or more organization response information, and acquire item scores that are scores of the items for each organization and for each item, an item score acquisition unit; for each of the two or more organization response information, using the two or more item scores, a comprehensive score acquisition unit that acquires a comprehensive score that is a comprehensive score for each organization; using the item scores of the two or more items of each of the two or more organizations and the comprehensive scores of each of the two or more organizations, an influence degree acquisition unit that acquires, for each item, the influence degree of the item score of each item on the comprehensive score; and a program for causing the influence degree output unit to function to output the influence degree acquired by the influence degree acquisition unit in association with each item. Note that such a program can be said to be a program that realizes an engagement system.

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

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

[0185] Also, in the present embodiment, an information system including a score prediction device that predicts a score using a correspondence table obtained by data mining will be described.

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

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

[0188] 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 are not limited. The terminal device 4 is a terminal used by a user who wants to acquire the score. The terminal device 4 is a mobile terminal such as a smartphone, a tablet terminal, or a mobile phone, or a so-called personal computer, etc., and its type is not limited.

[0189] FIG. 12 is a block diagram of the information system B. Also, FIG. 13 is a block diagram of the score prediction device 3 that constitutes the information system B.

[0190] The score prediction device 3 includes a storage unit 31, a reception unit 32, a processing unit 33, and an output unit 34.

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

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

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

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

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

[0196] Various types of information are stored in the storage unit 31 that constitutes the score prediction device 3. The various types of information are, for example, the organization information described later, the daily information described later, the teacher score described later, the correspondence relationship information described later, and the information of two or more members. The various types of information in 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.

[0197] The constituent member information is information about constituent members. A constituent member has a constituent member identifier that identifies the constituent member. A constituent member has one or more constituent member attribute values. A constituent member attribute value is an attribute value of a constituent member. Each of one or more constituent member attribute values is, for example, a name, age, position, year of joining the company, email address, phone number, and the constituent member identifier of a supervisor. Also, assume that constituent member information such as top management, management positions, and supervisors is stored in the storage unit 31. Also, two or more pieces of constituent member information may be stored in a constituent member information storage unit (not shown).

[0198] One or more pieces of organization information are stored in the organization information storage unit 311. Organization information is information about an organization. An organization is, for example, an organization that performs some work. An organization is, for example, a so-called company, sole proprietorship, local public entity, etc., and is interpreted broadly. An organization may be an organization within a company. That is, an organization may be, for example, a department, workplace, section, division, etc. within a company. Organization information has, for example, an organization identifier and one or more organization attribute values. An organization identifier is information that identifies an organization. An organization identifier is, for example, an organization name, ID, etc. An organization attribute value is an attribute value of an organization. An organization attribute value is, for example, an industry identifier (e.g., bank, apparel, manufacturer, etc.) indicating the industry of the organization, a scale identifier (e.g., large enterprise, small and medium-sized enterprise, sole proprietorship, etc.) classifying the scale of the organization, a regional identifier indicating the region of the head office, the number of employees, a classification based on the number of employees (e.g., five levels from 1 to 5), the number of workplaces, a classification based on the number of workplaces (e.g., three levels of "many", "medium", "few"), etc.

[0199] The daily information storage unit 312 stores one or more pieces of daily information. The daily information is information about the constituent members. The daily information is information that can be obtained in the daily life of the constituent members. The daily information does not include answers to questions such as questionnaires. The daily information is, for example, a file, but it may also be a record in a database or the like. The data structure and the like of the daily information are not limited. Note that the constituent members are those belonging to the organization. The constituent members are usually those who work for the organization. The constituent members are, for example, employees, part-timers, temporary workers, etc. The daily information is, for example, associated with an organization identifier. It is preferable that the daily information is associated with a constituent member identifier. The daily information is usually associated with time information. The time information is information that specifies the time corresponding to the daily information. The time information is, for example, information that specifies the time when the daily information was obtained, information that specifies the time when the daily information was measured. The time information is usually information that specifies a single point in time, but it may also be period information that specifies a period. The time information is, for example, information that specifies a date and time, information that specifies a day, information that specifies a month.

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

[0201] The daily information is, for example, business information and health information. The business information is information about the business performed by the constituent members. The health information is information about the health of the constituent members.

[0202] The business information is, for example, input information, work attendance status information, and business result information. The input information is information input by the constituent members. The input information is, for example, information input by the constituent members to a device used in the business (for example, a personal computer, a tablet terminal, a server, etc.). The input information is, for example, business input information and communication information. Details of the business input information and the communication information will be described later. Also, details of the work attendance status information and the business result information will be described later.

[0203] Health information is information related to the health of the members. The health information is, for example, biological information and life information. The biological information is information related to the members' organisms. The biological information is, for example, stress information and measurement information. Details of the stress information and the measurement information will be described later. Also, details of the life information will be described later.

[0204] The daily information used for obtaining the correspondence relationship is called teacher daily information. The daily information used for the prediction process of the score is called prediction daily information.

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

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

[0207] One or more pieces of work input information are stored in the work input information storage means 3121 that constitutes the daily information storage unit 312. The work input information is information related to the input for the performance of the members' own work.

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

[0209] 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, program attribute value, document attribute value, and daily report attribute value are, for example, information acquired by the processing unit 33 or information received from the terminal device 4 or a server (not shown).

[0210] One or more pieces of communication information are stored in the communication information storage means 3122. The communication information is associated with time information. The communication information is, for example, associated with an organization identifier and a constituent member identifier. Here, the time information is information that specifies the time when the communication information is input, transmitted, or received. Also, the organization identifier here is an identifier of the organization to which the constituent member who input the communication information belongs. Also, the constituent member identifier here is an identifier of the constituent member who input the communication information. The communication information is, for example, associated with the constituent member identifiers of one or more constituent members who are the communication partners.

[0211] The communication information is information regarding communication between two or more constituent members. The communication information is, for example, chat information input by a constituent member into a chat system (which may be a chat application), SNS information input by a constituent member into an SNS, an email created by a constituent member, or textified information obtained by textifying the voice uttered by a constituent member during a video conference. Note that the technology for textifying voice through voice recognition processing is a known technology and will not be described.

[0212] The communication information may include communication attribute values that can be obtained from chat information, SNS information, emails, or textified information. The communication attribute values are the data volume of communication information within a predetermined period, the number of occurrences of negative words within the communication information for a predetermined period, the occurrence rate of negative words within the communication information for a predetermined period, the number of occurrences of positive words within the communication information for a predetermined period, the occurrence rate of positive words within the communication information for a predetermined period, and the response rate of members to messages from management, management positions, or superiors. Note that the predetermined period is a period determined in advance.

[0213] 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 values are, for example, information obtained by the processing unit 33 or information received from the terminal device 4 or a server (not shown).

[0214] One or more work attendance status information is stored in the work attendance status information storage means 3123. The work attendance status information is information regarding the work attendance status. The work attendance status information may be work attendance information or information that can be obtained from one or more work attendance information. The work attendance information has a workday, a work start time, and a work end time. The work attendance information includes, for example, information indicating annual leave days. The work attendance information includes, for example, information indicating overtime work on holidays. The work attendance information includes, for example, information on normal working hours. The work attendance information includes, for example, information identifying normal working days.

[0215] The work attendance status information is, for example, the number of annual leave days of members, the number of overtime hours of members, and the number of holiday work days of members. The work attendance status information is associated with, for example, an organization identifier and a member identifier.

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

[0217] One or more pieces of business result information are stored in the business result information storage means 3124. The business result information is information regarding the result of business execution. The business result information is, for example, the team target achievement rate, the individual target achievement rate, the number of sales, and the sales amount. The business result information is usually associated with the organization identifier and the period information.

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

[0219] One or more pieces of stress information are stored in the stress information storage means 3125. The stress information is information regarding the stress of the team members. The stress information is the stress level indicating the degree of stress and the information indicating the presence or absence of stress. The stress information is, for example, the result of a stress check. The stress information is, for example, the answer to a questionnaire for team members (e.g., "Please select your stress level from 1 to 5"). The stress information is, for example, the information acquired using other health information of team members (e.g., heart rate, blood pressure), or the information acquired using the answers to two or more questions of a questionnaire for team members. The stress information is, for example, associated with the organization identifier and the team member identifier.

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

[0221] One or more pieces of measurement information are stored in the measurement information storage means 3126. The measurement information is the measurement result of the biometrics of team members. The measurement information is, for example, the heart rate and the blood pressure. The measurement information is, for example, (a) the information acquired using a wearable terminal worn by a team member or a measurement device (not shown) and transmitted to the score prediction device 3, or (b) the information acquired using a wearable terminal worn by a team member or a measurement device (not shown) and input by the team member. The measurement information is, for example, associated with the organization identifier and the team member identifier.

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

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

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

[0225] One or more voices are stored in the voice storage means 3128. The voices are voices uttered by the members. The voices are, for example, voices of a video conference in which the members participate. The voices are associated with, for example, an organization identifier and a member identifier.

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

[0227] One or more pieces of life information are stored in the life information storage means 3129. The life information is information related to the life of the members. The life information is, for example, the sleep time, whether alcohol has been consumed, and the number of cigarettes smoked. The life information is, for example, (a) information input by the members, (b) information automatically acquired by a sleep measurement device (not shown) and transmitted to the score prediction device 3, or (c) information automatically acquired by a sleep measurement device (not shown) and input by the members. The life information is associated with, for example, an organization identifier and a member identifier. Also, the life information is associated with, for example, time information (e.g., date).

[0228] The teacher score storage unit 313 stores one or more teacher scores. The teacher score is usually the score of the organization obtained using a plurality of organization response information indicating the responses to questions for two or more members of the organization. However, the teacher score may be a score manually input by the user or a predicted score obtained by the score prediction device 3 in the past. The questions are, for example, questionnaire questions or survey questions. The teacher score is, for example, the comprehensive score obtained by the information processing device 1 described in Embodiment 1. The teacher score may be, for example, any item score obtained by the information processing device 1 described in Embodiment 1. Also, the teacher score may be the representative value (for example, average value, weighted average, median) of the responses of two or more members to a simple questionnaire (for example, a questionnaire with one question such as "Please select from 1 to 5 the degree of satisfaction with the company"). That is, the teacher score may be, for example, the score of the organization obtained using a plurality of organization response information indicating the responses to questions for two or more members. The teacher score is associated with, for example, period information and an organization identifier. The period information associated with the teacher score is information on the period of the questionnaire (for example, one year, six months, three months). The period information may also be time information for specifying the time when the questionnaire was conducted. The organization identifier associated with the teacher score is the identifier of the organization whose score the teacher score indicates.

[0229] The correspondence relationship information storage unit 314 stores one or more pieces of correspondence relationship information. The correspondence relationship information is associated with, for example, an organization identifier. Also, the correspondence relationship information is associated with, for example, time information or period information.

[0230] The correspondence relationship information is information regarding the correspondence relationship between one or more teacher attributes and the teacher score. The correspondence relationship information is, for example, information for specifying the correspondence relationship between one or more teacher attributes and the teacher score. The teacher attribute is one or more pieces of teacher daily information, or information obtained using one or more pieces of teacher daily information, or information obtained using one or more pieces of teacher daily information and one or more pieces of teacher daily information.

[0231] The teacher attribute is information including at least one type of information among, for example, data amount information for specifying the data amount of teacher input information, input frequency information for specifying the input frequency of teacher input information, analysis information indicating the analysis result of teacher input information, and timing information for specifying the input timing of teacher input information. Note that the analysis information is, for example, a business input attribute value or a communication attribute value.

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

[0233] The learning model is data obtained by a learning process of machine learning. The learning model may also be referred to as a prediction model, a classification model, a learner, or a predictor. In machine learning, for example, algorithms such as deep learning, decision trees, random forests, SVR, and SVM can be used. In machine learning, multiple regression analysis, neural networks, etc. can be used. That is, the algorithms of machine learning, etc. are not limited. The learning model is information obtained by an algorithm of machine learning 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 attributes as elements. Note that the vector is a set of two or more pieces of information, and its structure is not limited.

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

[0235] The arithmetic expression is an arithmetic expression (S = f(a, b, c, ···)) that takes two or more teacher attributes as inputs and outputs a teacher score. Note that a, b, c, etc. in the arithmetic expression are teacher attributes, and S is a score (teacher score or prediction score).

[0236] The reception unit 32 receives various types of information and instructions. The various types of information and instructions are, for example, a prediction instruction and daily information. The prediction instruction is an instruction to output a prediction score. The prediction instruction is, for example, It has conditions for teachers. The conditions for teachers are information that identifies the organizational identifier paired with the daily information that is the source of the teacher data. The conditions for teachers are, for example, the organizational identifier, conditions using the organizational identifier. The prediction instruction has, for example, information that identifies the correspondence information to be used. The prediction instruction has, for example, teacher period information that identifies the period of the daily information that is the source of the teacher data.

[0237] Also, here, reception usually refers to reception from the terminal device 4, but it may be construed as a concept including reception of information input from input devices such as a keyboard, mouse, touch panel, etc., and reception of information read from recording media such as optical disks, magnetic disks, semiconductor memories, etc.

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

[0239] The processing unit 33 accumulates, for example, the daily information received by the reception unit 32 in the daily information storage unit 312. The processing unit 33 accumulates, for example, the daily information received by the reception unit 32 in the daily information storage unit 312 in association with the member identifier of the constituent member corresponding to the daily information. Note that such a configuration identifier is, for example, the identifier of the constituent member who input or transmitted the daily information, or the member identifier associated with the daily information.

[0240] The score acquisition unit 331 acquires a prediction score using the correspondence between the teacher score and one or more teacher daily information, and one or more prediction daily information. The prediction score is the score corresponding to one or more prediction daily information.

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

[0242] The score acquisition unit 331 may obtain features, for example, as follows. The features are either teacher features used to obtain correspondence information or prediction features used to obtain prediction scores. Note that, regarding the process of the score acquisition unit 331 obtaining features described below, when the features are teacher features, the correspondence acquisition means 3311 performs the process, and when the features are prediction features, the score acquisition means 3312 performs the process. Also, the features are, for example, daily information or attribute values of daily information that can be obtained from daily information (for example, operation log attribute values, program attribute values, document attribute values, daily report attribute values, etc.). Further, a teacher vector is composed of two or more teacher features. A prediction vector is composed of two or more prediction features. Note that, among the information obtained by the score acquisition unit 331 described below, it does not matter which information is used as features. The score acquisition unit 331 may use any two or more features for obtaining correspondence information or for obtaining prediction scores.

[0243] For example, for each member of the organization, the score acquisition unit 331 obtains, in association with each member identifier, the operation logs corresponding to the time information of each day during the period to be processed from the business input information storage means 3121, obtains one or two or more operation log attribute values from the operation logs, and at least temporarily accumulates the one or more operation log attribute values in the business input information storage means 3121. Note that since the technology of obtaining business input attribute values such as the data volume of information and the above-described change information from business input information such as operation logs is a well-known technology, detailed description thereof is omitted. Also, the operation log is, for example, the operation history of the configurator such as the login and logout dates and times of the PCs operating within the organization, the viewing and editing of various files, and network connections.

[0244] For example, for each member of the organization, the score acquisition unit 331 obtains, in association with each member identifier, the programs corresponding to the time information of each day during the period to be processed from the business input information storage means 3121, obtains one or two or more program attribute values from the programs, and at least temporarily accumulates the one or more program attribute values in the business input information storage means 3121.

[0245] 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 creation date). Then, the score acquisition unit 331 acquires, for example, the average value of the number of steps of the program files per day, information indicating the variation in the number of steps created each day during the target period, and variation information indicating whether the number of steps of the programs created in one day is on an increasing trend or a decreasing trend.

[0246] The score acquisition unit 331 acquires, for example, for each member of the organization, a document corresponding to the time information of each day during the period to be processed, paired with each member identifier, from the business input information storage means 3121, acquires one or two or more types of document attribute values from the document, and stores at least temporarily the one or more types of document attribute values in the business input information storage means 3121. Note that a document is a text of a direct result of work performance. Documents are, for example, design documents in the technical department and accounting information in the accounting department.

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

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

[0249] The score acquisition unit 331 acquires, for example, the data volume of each of the one or more daily reports acquired. Then, the score acquisition unit 331 acquires, for example, the average value of the data volume, information indicating the variation of the data volume, and variation information indicating whether the data volume is on an increasing trend or a decreasing trend. Also, the score acquisition unit 331 morphologically analyzes, for example, the one or more acquired daily reports, refers to a negative word dictionary, and acquires the number of occurrences of negative words. Also, the score acquisition unit 331 morphologically analyzes, for example, the one or more acquired daily reports, refers to a positive word dictionary, and acquires the number of occurrences of positive words. Also, the score acquisition unit 331 acquires, for example, the number of independent words in the one or more acquired daily reports, and acquires the occurrence rate of negative words, which is the number of negative words among the number of independent words, and the occurrence rate of positive words, which is the number of positive words among the number of independent words.

[0250] The score acquisition unit 331 acquires, for example, for each constituent member, communication information corresponding to the time information of each day during the period to be processed, which is paired with each constituent member identifier, from the communication information storage means 3122, and acquires, for example, one or two or more communication attribute values of the communication information. The score acquisition unit 331 acquires, for example, the data volume of the acquired communication information. Also, the score acquisition unit 331 morphologically analyzes the communication information, for example, acquires one or more independent words, and acquires the number of negative words, which is the number of words existing in a negative word dictionary (not shown) among the one or more independent words. Also, the score acquisition unit 331 morphologically analyzes the communication information, for example, acquires one or more independent words, and acquires the number of positive words, which is the number of words existing in a positive word dictionary (not shown) among the one or more independent words. Also, the score acquisition unit 331 morphologically analyzes the communication information, for example, and acquires the ratio of the number of negative words and the ratio of the number of positive words among the number of acquired independent words.

[0251] The score acquisition unit 331, for example, for each member of the organization, obtains the sent e-mails corresponding to the time information of each day during the period to be processed, paired with each member identifier, obtains the e-mail of the source of the said e-mail, and determines whether the identifier of the sender of the said source e-mail (for example, e-mail address) is stored in the organization information storage unit 311 in association with the said member identifier. If it is stored, the counter for the reply to the message from the reply target (management, management position, or supervisor, etc.) is incremented by 1. Also, the score acquisition unit 331, for each member of the organization, obtains the number of e-mails from the reply target among the received e-mails corresponding to the time information of each day during the period to be processed, paired with each member identifier. More specifically, for example, the score acquisition unit 331 obtains, for example, the member identifiers of the supervisor, management, and management positions paired with the said member identifier, obtains the e-mail addresses of the said member identifiers from the set of member information, and obtains the number of e-mails with the said e-mail address as the sender (the number of e-mails from the reply target). Then, the score acquisition unit 331 calculates, for example, for each member of the organization, the reply rate of the member to the message from the reply target from the number of e-mails from the reply target and the value of the reply counter. For example, it is assumed that in the storage unit 31, the identifiers of the reply targets of each member (for example, e-mail addresses) are stored in association with each member identifier.

[0252] The score acquisition unit 331, for example, for each member of the organization, obtains the attendance information of each day during the period to be processed, paired with each member identifier. Then, the score acquisition unit 331, for example, for each member of the organization, obtains the attendance status information (for example, number of annual leave days, number of overtime hours, number of holiday attendances) during the period to be processed. The technology of obtaining the number of annual leave days, number of overtime hours, number of holiday attendances, etc. from the set of attendance information is a well-known technology.

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

[0254] The score acquisition unit 331 reads, for example, for each member of the organization, one or more responses to a questionnaire corresponding to the time information of the period to be processed, which are paired with each member identifier, from the storage unit 31. Next, the score acquisition unit 331 acquires stress information from the one or more responses, for example, for each member of the organization. Note that the content of the questionnaire and the algorithm for acquiring stress information are not limited. The score acquisition unit 331 acquires, for example, responses to questions that inquire about the degree of stress. The score acquisition unit 331 substitutes, for example, the responses to two or more questions regarding the degree of stress into an arithmetic expression for calculating the degree of stress, and calculates the degree of stress. The score acquisition unit 331 constructs a vector from the responses to two or more questions regarding the degree of stress, for example, and acquires the degree of stress that pairs with the vector that most closely approximates the said vector. In such a case, it is assumed that two or more pieces of correspondence information that are pairs of vectors and degrees of stress are stored in the storage unit 31.

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

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

[0257] For example, for each member of the organization, the score acquisition unit 331 obtains, in pairs with each member identifier, one or more images of the period to be processed from the image storage means 3127. Next, the score acquisition unit 331 performs, for example, facial expression analysis processing on each of the one or more images, and obtains a facial expression identifier (for example, any one of joy, anger, sorrow, and happiness, normal, etc.). Further, the score acquisition unit 331 performs, for example, statistical processing on the one or more obtained facial expression identifiers, etc., and obtains the ratio of negative facial expressions (angry, crying, etc.). Note that such a ratio is an example of a feature. Also, the processing of performing facial expression analysis and obtaining a facial expression identifier is a known technique.

[0258] For example, for each member of the organization, the score acquisition unit 331 obtains, in pairs with each member identifier, one or more voices of the period to be processed from the voice storage means 3128. Next, the score acquisition unit 331 obtains, for example, one or more types of feature quantities (for example, spectral information, speech section, silent section, etc.) from each of the one or more voices, and uses the feature quantities to obtain the features of the voice. The score acquisition unit 331 obtains, for example, using the feature quantities, the ratio of the speaking time in the participated meetings, and an emotion identifier (laughing, normal, angry, crying, etc.). Note that the technique for obtaining feature quantities is a known technique such as LPC analysis and cepstrum analysis.

[0259] For example, for each member of the organization, the score acquisition unit 331 reads out the life information of the period to be processed from the life information storage means 3129 in pairs with each member identifier. Then, the score acquisition unit 331 performs statistical processing on the life information for each member, for example, and obtains the features of the life information. Note that the features of the life information are, for example, the average value of the sleep time, the variance of the sleep time, the number of days of drinking alcohol, the ratio of the days of drinking alcohol, and the average number of cigarettes smoked per day.

[0260] The score acquisition unit 331 may acquire correspondence relationship information using two or more pieces of teacher data having a teacher vector with two or more acquired teacher attributes and a teacher score. Note that the teacher vector is information acquired by the score acquisition unit 331 through the above-described processing. Also, the correspondence relationship information may be stored in advance in the correspondence relationship information storage unit 314.

[0261] For example, the score acquisition unit 331 provides a learning model, which is correspondence relationship information, and a prediction vector, which is a vector having two or more respective prediction attributes as elements, to a machine learning module, performs prediction processing by executing the module, and acquires a prediction score. Note that the prediction attributes are prediction daily information or information that can be acquired from the prediction daily information. Also, the prediction vector is information acquired by the score acquisition unit 331 through the above-described processing. Also, the machine learning module may be, for example, Tiny_SVM, a function of TensorFlow, a function of MicrosoftML, etc., without limitation.

[0262] For example, the score acquisition unit 331 acquires a prediction score using a correspondence table, which is correspondence relationship information, and a prediction vector having two or more respective prediction attributes as elements. More specifically, for example, the score acquisition unit 331 searches the correspondence table for a teacher vector that most closely approximates a prediction vector having two or more respective prediction attributes as elements, and acquires the teacher score paired with the teacher vector as the prediction score.

[0263] Also, for example, the score acquisition unit 331 determines, from the correspondence table, two or more teacher vectors whose distance from a prediction vector having two or more respective prediction attributes as elements satisfies a predetermined condition and is as close as possible, and acquires a representative value of the teacher scores corresponding to the two or more teacher vectors. Note that the representative value is, for example, an average value, a median value, or a weighted average. The weighted average is acquired, for example, by calculating a weight inversely proportional to the distance between the vectors and using the weight. Note that the predetermined condition is, for example, being within a threshold value or smaller than the threshold value, or the closeness of the distance being within the top N (N is a natural number of 2 or more).

[0264] Also, the score acquisition unit 331 substitutes, for example, each of two or more prediction features into an arithmetic expression, executes the arithmetic expression, and calculates a prediction score. Note that the arithmetic expression is stored in the correspondence information storage unit 314.

[0265] The score acquisition unit 331 acquires a prediction score, for example, using the correspondence between one or more teacher daily information including teacher work information and a teacher score, and one or more prediction daily information including prediction work information. Note that using the correspondence usually means using the above-mentioned correspondence information.

[0266] The score acquisition unit 331 acquires a prediction score, for example, using the correspondence between one or more teacher daily information including teacher input information and a teacher score, and one or more prediction daily information including prediction input information.

[0267] The score acquisition unit 331 acquires a prediction score, for example, using the correspondence between one or more teacher daily information including teacher work input information and a teacher score, and one or more prediction daily information including prediction work input information.

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

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

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

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

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

[0273] The score acquisition unit 331 acquires a predicted score, for example, using the correspondence relationship between one or more teacher daily information including teacher biometric information and teacher scores, and one or more predicted daily information including predicted biometric information.

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

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

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

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

[0278] The score acquisition unit 331 may, for example, read from the storage unit 31 the past score corresponding to the obtained predicted score, and use the past score and the obtained predicted score to obtain score variation information regarding an increase or decrease in the score. The score variation information is, for example, "predicted score - past score", "past score - predicted score", "information indicating whether the score increases or decreases", or "information indicating the degree of increase or decrease in the score".

[0279] The correspondence acquisition means 3311 constituting the score acquisition unit 331 acquires correspondence relation information for specifying the correspondence between one or more teacher daily information and the teacher score.

[0280] The correspondence acquisition means 3311 may, for example, for each organization condition constituted by using one or more organization attribute values, acquire correspondence relation information for specifying the correspondence between one or more teacher daily information and the teacher score. The organization condition is, for example, one organization attribute value. The organization condition is, for example, a logical formula having two or more respective organization conditions as elements (for example, "organization A or organization B"). The organization condition is, for example, an industry identifier such as "electric manufacturer", "bank", or "trading company". The organization condition is, for example, a scale identifier such as "large enterprise" or "small and medium-sized enterprise".

[0281] Hereinafter, a processing example in which the correspondence acquisition means 3311 acquires each of a learning model, a correspondence table, and an arithmetic expression will be described. (1) When acquiring a learning model

[0282] The correspondence acquisition means 3311 performs learning processing by using, for example, one or two or more pairs of one or more teacher daily information and the teacher score according to a machine learning algorithm, and acquires correspondence relation information as a learning model.

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

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

[0285] Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each set of teacher daily information. Then, the correspondence acquisition means 3311 obtains two or more pieces of teacher data. In the teacher data, it can be said that each teacher characteristic of the teacher vector is an explanatory variable and the teacher score is an objective variable. Also, the teacher characteristics are teacher daily information or information obtained from teacher daily information.

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

[0287] Note that this learning model is a learning model that takes a prediction vector, which is prediction daily information or / and information obtained from the prediction daily information, as an input and outputs a prediction score. Also, as the algorithm for the learning process of machine learning, algorithms such as deep learning, decision trees, random forests, SVR, and SVM can be used. That is, algorithms such as those for the learning process of machine learning are not restricted. Also, the module that performs the learning process of machine learning is not restricted, for example, Tiny_SVM, functions of TensorFlow, functions of MicrosoftML, etc. (2) When obtaining a correspondence table

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

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

[0290] The correspondence acquisition means 3311 reads out, for example, an arithmetic expression source (for example, "score S = ax1 + bx2 + cx3 + ··· + (n - 1)x n + n (a, b, c ··· n are parameters, x1, x2, x3, ··· x n are variables)") from the storage unit 31. Next, the correspondence acquisition means 3311 substitutes, for example, two or more each teacher data into the variables of the arithmetic expression source, and obtains the parameters for obtaining each teacher score by multiple regression analysis, multivariate analysis, etc. Then, the correspondence acquisition means 3311 at least temporarily stores the obtained arithmetic expression in the correspondence relationship information storage unit 314.

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

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

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

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

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

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

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

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

[0299] Also, the score acquisition means 3312 may, for example, determine from the correspondence table vectors that are closer than a predetermined condition is satisfied with respect to the acquired prediction vector, and acquire a representative value of the teacher scores corresponding to the two or more vectors. Note that the representative value is, for example, an average value, a median value, or a weighted average. The weighted average is obtained, for example, by calculating a weight inversely proportional to the distance between the vectors and using the weight. Also, the predetermined condition is, for example, that the distance is within a threshold value or smaller than the threshold value, or that the shortness of the distance is in the top N (N is 2 or more). (3) When using an arithmetic expression

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

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

[0302] Here, output usually means transmission to the terminal device 4. However, the output here may be considered as a concept including display on a display, projection using a projector, printing by a printer, sound output, storage in a recording medium, delivery of a processing result to another processing device or another program, etc.

[0303] The score output unit 341 passes the prediction score acquired by the score acquisition unit 331 to the score acquisition unit 331, and causes the score acquisition unit 331 to acquire score variation information using the prediction score, and may output the score variation information.

[0304] The terminal storage unit 41 constituting the terminal device 4 stores various types of information. The various types of information are, for example, an organization identifier for identifying a user's organization and a constituent member identifier. The various types of information are, for example, information received by the terminal reception unit 45 (for example, a prediction score). Note that the organization identifier may be considered as information for identifying a user.

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

[0306] The input means for various instructions, information, etc. can be anything, such as a touch panel, keyboard, mouse, menu screen, etc. The terminal reception unit 42 can be realized by a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen, etc.

[0307] The various processes of the terminal processing unit 43 are, for example, processes of configuring the information received by the terminal reception unit 45 into data to be displayed. The various processes are, for example, processes of configuring instructions, etc. received by the terminal reception unit 42 into instructions, etc. to be transmitted.

[0308] The terminal transmission unit 44 transmits various instructions, information, etc. to the score prediction device 3. The various instructions, information, etc. are, for example, instructions configured by the terminal processing unit 43, instructions and information, etc. received by the terminal reception unit 42.

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

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

[0311] Here, output is a concept including display on a display, projection using a projector, printing by a printer, sound output, transmission to an external device, storage in a recording medium, delivery of a processing result to another processing device or another program, etc.

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

[0313] The process by which information is stored in the storage unit 31 or the like is not limited. For example, information may be stored in the storage unit 31 or the like via a recording medium, or information transmitted via a communication line or the like may be stored in the storage unit 31 or the like, or information input via an input device may be stored in the storage unit 31 or the like.

[0314] The reception unit 32 and the terminal reception unit 45 are usually realized by wireless or wired communication means, but may also be realized by means of receiving broadcasts.

[0315] The processing unit 33, the score acquisition unit 331, the correspondence acquisition means 3311, the score acquisition means 3312, and the terminal processing unit 43 can usually be realized from a processor, a memory, etc. The processing procedures of the processing unit 33 or the like are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may also be realized by hardware (dedicated circuit). Note that the processor may be a CPU, an MPU, a GPU, etc., and its type is not limited.

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

[0317] The terminal output unit 46 may or may not be considered to include output devices such as a display and a speaker. The terminal output unit 46 can be realized by the driver software of the output device or the driver software of the output device and the output device or the like.

[0318] Next, an operation example of the score prediction device 3 will be described using the flowchart of FIG. 14.

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

[0320] (Step S1402) The processing unit 33 accumulates the daily information received in step S1401 in the daily information storage unit 312. The process returns to step S1401. Note that the accumulated daily information is usually associated with a member identifier or an organization identifier. Also, the accumulated daily information is usually associated with time information. That is, it is preferable that the processing unit 33 obtains date and time information from a clock (not shown) and accumulates the daily information in the daily information storage unit 312 in association with the date and time information.

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

[0322] (Step S1404) The correspondence acquisition means 3311 performs a correspondence acquisition process. An example of the correspondence acquisition process will be described using the flowchart of FIG. 15. Note that the correspondence acquisition process is a process of acquiring correspondence relationship information.

[0323] (Step S1405) The score acquisition means 3312 performs a score acquisition process. An example of the score acquisition process will be described using the flowchart of FIG. 25. Note that the score acquisition process is a process of acquiring a prediction score.

[0324] (Step S1406) The score output unit 341 transmits the predicted score obtained in step S1405 to the terminal device 4 that has sent the prediction instruction. Return to step S1401.

[0325] In the flowchart of FIG. 14, the correspondence acquisition process in 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 necessarily need to be performed continuously.

[0326] Also, in the flowchart of FIG. 14, the process ends due to a power-off or a process termination interrupt.

[0327] Next, an example of the correspondence acquisition process in step S1404 will be described using the flowchart of FIG. 15.

[0328] (Step S1501) The 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, the teacher period information determined in advance from the storage unit 31. Note that the teacher period information is information for specifying the period of teacher daily information used to acquire the correspondence relationship information.

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

[0330] (Step S1503) The correspondence acquisition means 3311 substitutes 1 for the counter i.

[0331] (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 items exists in the daily information storage unit 312. If the i-th teacher daily set exists, the process proceeds to Step S1505; if it does not exist, the process proceeds to Step S1510. Note that the teacher daily set is a set of one or more pieces of teacher daily information.

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

[0333] (Step S1506) The correspondence acquisition means 3311 performs a process of acquiring a feature group 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. 16. Note that the feature group is usually a set of two or more features. Also, the features here are teacher features.

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

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

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

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

[0338] (Step S1511) The correspondence acquisition means 3311 at least temporarily stores the correspondence relation information acquired in step S1510 in the correspondence relation information storage unit 314. Return to the upper-level process.

[0339] Next, an example of the feature group acquisition process in step S1506 will be described using the flowchart of FIG. 16. In the flowchart of FIG. 16, for example, what the daily information to be used is has been determined in advance. Also, the information specifying the daily information to be used is stored, for example, in association with the correspondence relation information. That is, the daily information used when acquiring the correspondence relation information and the daily information used for acquiring the prediction score are usually the same.

[0340] (Step S1601) The score acquisition unit 331 determines whether to use the business input information for acquiring the prediction score. If using the business input information, proceed to step S1602; if not using the business input information, proceed to step S1603.

[0341] (Step S1602) The score acquisition unit 331 performs business input information processing. An example of the business input information processing will be described using the flowchart of FIG. 17. Note that the business input information processing is a process of acquiring one or two or more features regarding the business input information using the business input information.

[0342] (Step S1603) The score acquisition unit 331 determines whether to use the communication information for acquiring the prediction score. If using the communication information, proceed to step S1604; if not using the communication information, proceed to step S1605.

[0343] (Step S1604) The score acquisition unit 331 performs communication information processing. An example of the communication information processing will be described using the flowchart of FIG. 18. The communication information processing is a process of obtaining one or more features related to communication information using the communication information.

[0344] (Step S1605) The score acquisition unit 331 determines whether to use the attendance and absence status information for obtaining the prediction score. If using the attendance and absence status information, it proceeds to step S1606; if not using the attendance and absence status information, it proceeds to step S1607.

[0345] (Step S1606) The score acquisition unit 331 performs attendance and absence status information processing. An example of the attendance and absence status information processing will be described using the flowchart of FIG. 19. The attendance and absence status information processing is a process of obtaining one or more features related to the attendance and absence status information using the attendance and absence status information.

[0346] (Step S1607) The score acquisition unit 331 determines whether to use the business result information for obtaining the prediction score. If using the business result information, it proceeds to step S1608; if not using the business result information, it proceeds to step S1609.

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

[0348] (Step S1609) The score acquisition unit 331 determines whether to use the stress information for obtaining the prediction score. If using the stress information, it proceeds to step S1610; if not using the stress information, it proceeds to step S1611.

[0349] (Step S1610) The score acquisition unit 331 performs stress information processing. An example of stress information processing will be described using the flowchart of FIG. 20. Stress information processing is a process of acquiring one or more features related to stress information.

[0350] (Step S1611) The score acquisition unit 331 determines whether to use measurement information. If measurement information is to be used, it proceeds to step S1611; if measurement information is not to be used, it proceeds to step S1612.

[0351] (Step S1612) The score acquisition unit 331 performs measurement information processing. An example of measurement information processing will be described using the flowchart of FIG. 21. Measurement information processing is a process of acquiring one or more features related to measurement information using the measurement information.

[0352] (Step S1613) The score acquisition unit 331 determines whether to use an image for acquiring a prediction score. If an image is to be used, it proceeds to step S1614; if an image is not to be used, it proceeds to step S1615.

[0353] (Step S1614) The score acquisition unit 331 performs image information processing. An example of image information processing will be described using the flowchart of FIG. 22. Image information processing is a process of acquiring one or more features related to an image using the image.

[0354] (Step S1615) The score acquisition unit 331 determines whether to use voice for acquiring a prediction score. If voice is to be used, it proceeds to step S1616; if voice is not to be used, it proceeds to step S1617.

[0355] (Step S1616) The score acquisition unit 331 performs voice information processing. An example of voice information processing will be described using the flowchart of FIG. 23. Voice information processing is a process of acquiring one or more features related to voice using the voice.

[0356] (Step S1617) The score acquisition unit 331 determines whether to use life information for acquiring the prediction score. If life information is to be used, it proceeds to step S1618; if life information is not to be used, it returns to the upper-level process.

[0357] (Step S1618) The score acquisition unit 331 performs life information processing and returns to the upper-level process. An example of life information processing will be described using the flowchart of FIG. 24. Life information processing is a process of acquiring one or more features related to life information using life information.

[0358] Next, an example of the business input information processing in step S1602 will be described using the flowchart of FIG. 17.

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

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

[0361] (Step S1703) The score acquisition unit 331 acquires the daily report paired with the i-th member identifier and the daily reports within the target period from the business input information storage means 3121.

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

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

[0364] (Step S1706) The score acquisition unit 331 acquires one or more document attribute values from the one or more acquired documents. The document attribute values are, for example, the average value of the data volume of the documents for each created day and the change information of the data volume.

[0365] (Step S1707) The score acquisition unit 331 acquires, from the business input information storage means 3121, the program that pairs with the i-th member identifier and is a program within the target period. Note that the program is a program created by a software developer.

[0366] (Step S1708) The score acquisition unit 331 acquires one or more program attribute values from the one or more acquired programs. The program attribute values are, for example, the average value of the number of steps of the programs for each created day and the change information of the number of steps.

[0367] (Step S1709) The score acquisition unit 331 acquires, from the business input information storage means 3121, the operation log that pairs with the i-th member identifier and is an operation log within the target period. Note that the operation log is an operation log for the device (e.g., a personal computer) used by the member.

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

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

[0370] (Step S1712) The score acquisition unit 331 uses one or more daily report attribute values of all target members acquired in step S1704 to acquire representative values for each type of daily report attribute value. The representative value is, for example, an average value, a weighted average value, a median value, or a total. The score acquisition unit 331 calculates, for example, a representative value of the data volume of the daily reports of all members, a representative value of the change information of the data volume of all members, a representative value of the number of occurrences of negative words of all members, a representative value of the occurrence rate of negative words, a representative value of the number of occurrences of positive words, and a representative value of the occurrence rate of positive words. Note that all members are members identified by, for example, member identifiers paired with specified organization identifiers.

[0371] (Step S1713) The score acquisition unit 331 uses one or more document attribute values of all target members acquired in step S1706 to acquire representative values for each type of document attribute value. The score acquisition unit 331 acquires, for example, a representative value of the data volume of documents per day of all members, a representative value of information indicating the variation in the data volume of each day in the target period of all members, the ratio of the data volume of documents created in one day among all members showing an increasing trend, and the ratio of the data volume of documents created in one day among all members showing a decreasing trend.

[0372] (Step S1714) The score acquisition unit 331 uses one or more program attribute values of all target members acquired in step S1708 to acquire representative values for each type of program attribute value. The score acquisition unit 331 acquires, for example, a representative value of the number of steps of the program per day of all members, a representative value of information indicating the variation in the number of steps of each day in the target period of all members, the ratio of the data volume of the number of steps of the program created in one day among all members showing an increasing trend, and the ratio of the number of steps of the program created in one day among all members showing a decreasing trend.

[0373] (Step S1715) The score acquisition unit 331 uses the one or more operation log attribute values of all target members acquired in step S1710 to acquire a representative value for each type of operation log attribute value. For example, the score acquisition unit 331 acquires a representative value of the number of daily operation logs of all members, a representative value of information indicating the variation in the number of operation logs for each day during the target period of all members, the ratio of the number of operation logs of one day among all members showing an increasing trend, and the ratio of the number of operation logs of one day among all members showing a decreasing trend. Return to the upper-level process.

[0374] Next, an example of the communication information processing in step S1604 will be described using the flowchart of FIG. 18.

[0375] (Step S1801) The score acquisition unit 331 substitutes 1 for the counter i.

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

[0377] (Step S1803) The score acquisition unit 331 acquires the chat information paired with the i-th member identifier and the chat information during the target period from the communication information storage means 3122.

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

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

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

[0381] (Step S1807) The score acquisition unit 331 acquires the email paired with the i-th member identifier from the communication information storage means 3122 for the email during the target period.

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

[0383] (Step S1809) The score acquisition unit 331 acquires the voice text information paired with the i-th member identifier from the communication information storage means 3122 for the voice text information during the target period.

[0384] (Step S1810) The score acquisition unit 331 acquires one or more voice text attribute values from the one or more voice text information acquired in step S1809. The voice text attribute values are, for example, the data volume of the voice text information, the number of occurrences of negative words in the voice text information of the constituent members, the occurrence rate of negative words in the voice text information of the constituent members, the number of occurrences of positive words in the voice text information of the constituent members, and the occurrence rate of positive words in the voice text information of the constituent members.

[0385] (Step S1811) The score acquisition unit 331 sets the counter i to 1 and increments it. Return to step S1802.

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

[0387] (Step S1813) The score acquisition unit 331 uses the one or more SNS attribute values of all the target constituent members acquired in step S1806 to obtain a representative value for each type of SNS attribute value. The score acquisition unit 331 obtains, for example, the representative value of the data volume of the SNS information per day of all the constituent members, the representative value of the information indicating the variation in the data volume of the SNS information for each day in the target period of all the constituent members, the ratio of the data volume of the SNS information created in one day among all the constituent members that shows an increasing trend, the ratio of the data volume of the SNS information created in one day among all the constituent members that shows a decreasing trend, the representative value of the number of occurrences of negative words in the SNS information, the representative value of the occurrence rate of negative words in the SNS information, the representative value of the number of occurrences of positive words in the SNS information, and the representative value of the occurrence rate of positive words in the SNS information.

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

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

[0390] Next, an example of the attendance status information process in Step S1606 will be described using the flowchart of FIG. 19.

[0391] (Step S1901) The score acquisition unit 331 substitutes 1 for the counter i.

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

[0393] (Step S1903) The score acquisition unit 331 acquires attendance information that corresponds to the i-th member identifier and is the attendance information for the target period from the attendance status information storage means 3123.

[0394] (Step S1904) The score acquisition unit 331 acquires one or more attendance attribute values using the acquired attendance information. The attendance attribute values are, for example, the number of annual leave days of a member, the number of overtime hours of a member, and the number of holiday attendances of a member.

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

[0396] (Step S1906) The score acquisition unit 331 acquires representative values for each type of attendance attribute value using the one or more attendance attribute values of all the target members acquired in Step S1704, and returns to the upper-level process. Note that the representative values are, for example, the average value, the weighted average value, the median value, and the total. The score acquisition unit 331 acquires, for example, the representative value of the number of annual leave days of all members, the representative value of the number of overtime hours of members, and the representative value of the number of holiday attendances of members.

[0397] Next, an example of the stress information processing in Step S1610 will be described using the flowchart of FIG. 20.

[0398] (Step S2001) The score acquisition unit 331 substitutes 1 for the counter i.

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

[0400] (Step S2003) The score acquisition unit 331 acquires one or more responses that correspond to the i-th member identifier, and acquires one or more responses during the target period from the stress information storage means 3125. Note that the response is a response to a questionnaire for acquiring stress information (for example, stress level).

[0401] (Step S2004) The score acquisition unit 331 acquires stress information using the one or more responses acquired in Step S2003. The stress information may be a single response itself, or may be a stress level obtained by substituting one or more responses (for example, numerical values) into an arithmetic expression and performing an operation, or may be stress information corresponding to a vector that most closely approximates a vector having two or more responses as elements. It is assumed that such an arithmetic expression, or a table having two or more pairs of such vectors and stress information, is stored in the storage unit 31.

[0402] (Step S2005) The score acquisition unit 331 acquires one or more pieces of biological information (for example, heart rate, blood pressure) that correspond to the i-th member identifier, and acquires one or more pieces of biological information during the target period from the measurement information storage means 3126. Note that the response is a response to a questionnaire for acquiring stress information (for example, stress level).

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

[0404] (Step S2007) The score acquisition unit 331 increments the counter i by 1. Return to Step S2002.

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

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

[0407] Next, an example of the measurement information processing in step S1612 will be described with reference to the flowchart of FIG. 21.

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

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

[0410] (Step S2103) The score acquisition unit 331 acquires, from the measurement information storage means 3126, one or more heart rates that correspond to the i-th member identifier and are within the target period.

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

[0412] (Step S2105) The score acquisition unit 331 acquires one or more blood pressures that correspond to the i-th member identifier and are within the target period from the measurement information storage means 3126.

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

[0414] (Step S2107) The score acquisition unit 331 sets the counter i to 1 and increments it. Then it returns to Step S2102.

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

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

[0417] Next, an example of the image information processing in Step S1614 will be described using the flowchart of FIG. 22.

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

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

[0420] (Step S2203) The score acquisition unit 331 acquires, from the image storage means 3127, one or more images that are paired with the i-th member identifier and are one or more images in the target period.

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

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

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

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

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

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

[0427] (Step S2210) The score acquisition unit 331 uses each of the one or more image attribute values of all the target members acquired in step S2108, and for each type of image attribute value, obtains a representative value of each image attribute value. Return to the upper-level process. Note that the representative value is, for example, an average value, a weighted average value, a median value, or a total.

[0428] Next, an example of the voice information processing in step S1616 will be described using the flowchart of FIG. 23.

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

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

[0431] (Step S2303) The score acquisition unit 331 acquires one or more voices that are paired with the i-th member identifier and are one or more voices during the target period from the voice storage means 3128.

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

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

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

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

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

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

[0438] (Step S2310) For each type of voice attribute value, the score acquisition unit 331 acquires a representative value of the voice attribute value using one or more voice attribute values of all the target members acquired in step S2108. Return to the upper-level process. Note that the representative value is, for example, an average value, a weighted average value, a median value, or a total.

[0439] Next, an example of the life information process in step S1618 will be described using the flowchart of FIG. 24.

[0440] (Step S2401) The score acquisition unit 331 substitutes 1 for the counter i.

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

[0442] (Step S2403) The score acquisition unit 331 acquires the sleep time of each day in the target period from the life information storage means 3129, which is the sleep time paired with the i-th member identifier.

[0443] (Step S2404) The score acquisition unit 331 acquires one or more sleep attribute values using the one or more sleep times acquired in step S2403. Note that the sleep attribute value is, for example, the average value of the sleep time per day, information indicating the variation in the sleep time (e.g., variance), the number of days with a sleep time below a threshold value, and the ratio of days with a sleep time below the threshold value.

[0444] (Step S2405) The score acquisition unit 331 acquires the drinking days corresponding to the i-th member identifier and the drinking days within the target period from the living information storage means 3129.

[0445] (Step S2406) The score acquisition unit 331 acquires one or more drinking attribute values using the one or more drinking days acquired in step S2405. Note that 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.

[0446] (Step S2407) The score acquisition unit 331 acquires the number of cigarettes smoked corresponding to the i-th member identifier and the number of cigarettes per day within the target period from the living information storage means 3129.

[0447] (Step S2408) The score acquisition unit 331 acquires one or more cigarette attribute values using the one or more numbers acquired in step S2407. Note that the cigarette attribute value is, for example, the total number of cigarettes smoked during the period, the average number of cigarettes smoked per day, the number of days with the number of cigarettes smoked above a threshold value, and the ratio of days with the number of cigarettes smoked above the threshold value.

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

[0449] (Step S2410) The score acquisition unit 331 acquires a representative value for each type of sleep attribute value using one or more sleep attribute values of all target members acquired in step S2404. The representative value is, for example, the average value of sleep time, the average of information indicating the variation in sleep time, the average number of days when the sleep time is below a threshold value, or the average ratio of days when the sleep time is below a threshold value. The score acquisition unit 331 may acquire, for example, the number or ratio of members whose sleep time satisfies a predetermined condition and is as small as possible.

[0450] (Step S2411) The score acquisition unit 331 acquires a representative value for each type of drinking attribute value using one or more drinking attribute values of all target members acquired in step S2406. The representative value is, for example, the average value of the number of drinking days, the average value of the ratio of drinking days, or the average value of the maximum number of consecutive drinking days. The score acquisition unit 331 may acquire, for example, the number or ratio of members whose drinking days satisfy a predetermined condition and are as large as possible.

[0451] (Step S2412) The score acquisition unit 331 acquires a representative value for each type of tobacco attribute value using one or more tobacco attribute values of all target members acquired in step S2408, and returns to the upper processing. The representative value is, for example, the average value of the total number of cigarettes smoked, the average value of the number of cigarettes smoked, the average value of the number of days when the number of cigarettes smoked is above a threshold value, or the average value of the ratio of days when the number of cigarettes smoked is above a threshold value. The score acquisition unit 331 may acquire, for example, the number or ratio of members whose number of cigarettes smoked satisfies a predetermined condition and is as large as possible.

[0452] Next, an example of the score acquisition process in step S1405 will be described using the flowchart of FIG. 25.

[0453] (Step S2501) The score acquisition means 3312 acquires prediction interval information corresponding to the prediction instruction.

[0454] (Step S2502) The score acquisition means 3312 acquires a prediction organization identifier, which is an organization identifier corresponding to the prediction instruction.

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

[0456] (Step S2504) The score acquisition means 3312 acquires a feature group using the one or more pieces of prediction daily information acquired in step S2503. Such feature group acquisition processing was described using the flowchart of FIG. 16.

[0457] (Step S2505) The score acquisition means 3312 acquires the correspondence information used for the prediction process from the correspondence information storage unit 314.

[0458] (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 acquires a prediction score using the prediction vector and the correspondence information acquired in step S2505. Return to the upper-level process. Note that an example of the prediction score acquisition algorithm was described above.

[0459] Next, an operation example of the terminal device 4 will be described using the flowchart of FIG. 26.

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

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

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

[0463] (Step S2604) The terminal transmission unit 44 transmits the information configured in step S2603 to the score prediction device 3. Return to step S2601.

[0464] (Step S2605) The terminal reception unit 42 determines whether a prediction instruction has been received. If a prediction instruction has been received, proceed to step S2606; if a prediction instruction has not been received, return to step S2601.

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

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

[0467] (Step S2608) The terminal reception unit 45 determines whether a prediction score has been received. If a prediction score has been received, proceed to step S2609; if a prediction score has not been received, return to step S2608.

[0468] (Step S2609) The terminal processing unit 43 configures the prediction score to be output using the prediction score received in step S2608. The terminal output unit 46 outputs the prediction score. Return to step S2601.

[0469] Also, in the flowchart of FIG. 26, the process ends due to a power-off or a processing end interrupt.

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

[0471] (Specific Example 1) Specific Example 1 is a case where a prediction score is obtained using the reply rate of the constituent members' emails to the emails from the management team and superiors.

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

[0473] Then, the correspondence acquisition means 3311 acquires, from the constituent member information database (database of two or more constituent member information) of the organization identifier "A" not shown in the figure, one or more constituent member identifiers of the reply target (management team, superior, etc.) paired with each constituent member identifier by the above-described processing.

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

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

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

[0477] Then, the correspondence acquisition means 3311 provides the two or more pieces of teacher data to a module that performs the learning process of machine learning, executes the module to obtain a learner, and accumulates the learner in the correspondence relationship information storage unit 314 in pairs with the organization identifier "A". Note that the correspondence acquisition means 3311 may obtain a learner using 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 the explanatory variable for each unit period.

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

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

[0480] Next, the score acquisition means 3312 acquires the learner paired with the organization identifier "A" in the correspondence relationship information storage unit 314. Next, the score acquisition means 3312 provides the acquired response rate in 2020 and the learner to a module that performs the prediction process of machine learning, and executes the module to obtain a predicted score. Note that the score acquisition means 3312 may perform the prediction process using 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.

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

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

[0483] (Specific Example 2) Specific Example 2 is a case where the prediction score is obtained using a daily report. In Specific Example 2, the unit period is 1 year.

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

[0485] Next, the correspondence acquisition means 3311 acquires one or two or more daily report attribute values for each member identifier and each year using each daily report. The daily report attribute values to be acquired are, for example, change information of the data volume, appearance rate of negative words in the daily report for a predetermined period, and appearance rate of positive words in the daily report for a predetermined period.

[0486] Next, the correspondence acquisition means 3311 acquires the representative value (for example, 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 the representative value (for example, average value) of the daily report attribute value of all members for each year and for each type of the above daily report attribute value. That is, the correspondence acquisition means 3311 acquires, for each year, for example, the average of the change information of the data volume of all members of the organization with the organization identifier "A", the average value of the appearance rate of negative words in the daily report for a predetermined period (here, 1 year), and the average value of the appearance rate of positive words in the daily report for a predetermined period.

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

[0488] Next, the correspondence acquisition means 3311 provides the acquired plurality of teacher data to a module that performs machine learning learning processing, and acquires a learner by executing the module, and stores the learner in the correspondence relationship information storage unit 314 in a pair with the organization identifier "A".

[0489] Next, it is assumed that the score prediction device 3 has received a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires prediction daily information, which is a daily report paired with the above organization identifier "A" and is a daily report after January 1, 2020, from the business input information storage means 3121.

[0490] Next, the score acquisition means 3312 acquires, for each constituent member identifier, two or more daily report attribute values using the acquired daily reports for each constituent member identifier. Next, the score acquisition means 3312 acquires a representative value (for example, an average value) of the two or more daily report attribute values of all constituent members, and constructs a prediction vector having the representative values of the two or more daily report attribute values as elements. The prediction vector is, for example, a vector having, as elements, the average value of the change information of the data amount, the appearance rate of negative words, and the appearance rate of positive words.

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

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

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

[0494] (Specific Example 3) Specific Example 3 is a case where the prediction score is obtained using the overtime hours among the work attendance status information. Note that in Specific Example 3, the unit period is one month.

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

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

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

[0498] Next, the corresponding acquisition means 3311 provides the acquired plurality of teacher data to a module that performs the learning process of machine learning, and acquires a learning device by executing the module, and stores the learning device in the correspondence relationship information storage unit 314 in pair with the organization identifier "A".

[0499] Next, assume that the score prediction device 3 has received a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires prediction daily information, which is attendance information paired with the above-mentioned organization identifier "A" and is attendance information after January 1, 2020, from the attendance status information storage means 3123.

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

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

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

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

[0504] (Specific Example 4) This is the case where a prediction score is acquired using stress information and measurement information. In Specific Example 4, the unit period is one year. Also, in this specific example, assume that the stress information (e.g., stress level) is acquired by the score acquisition unit 331 by the method described above, associated with the member identifier and the time identifier, and stored in the stress information storage means 3125. Also, assume that the score acquisition unit 331 acquires the attribute values (e.g., average value, variance, etc.) of the stress information of all members in each year, and stores them in the stress information storage means 3125 in association with the organization identifier "A" and the identifier of each year.

[0505] In addition, the measurement information (heart rate, blood pressure) is received by the score prediction device 3 from a wearable terminal (not shown) worn by a member, and is stored in the measurement information storage means 3126 in association with the member identifier and the time identifier. 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 also acquires the representative value of the heart rate and the representative value of the blood pressure of all members for each year, and assumes that they are stored in the measurement information storage means 3126 in pairs with the organization identifier "A" and the identifier of each year.

[0506] In such a situation, the correspondence acquisition means 3311 of the score prediction device 3 acquires, for each year, a teacher vector having as elements the attribute value of the stress information, the representative value of the heart rate, and the representative value of the blood pressure that are paired with the organization identifier "A" of the target organization. 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.

[0507] Next, the correspondence acquisition means 3311 provides the acquired plurality of teacher data to a module that performs the learning process of machine learning, and acquires a learning device by executing the module, and stores the learning device in the correspondence relationship information storage unit 314 in pair with the organization identifier "A".

[0508] Next, it is assumed that the score prediction device 3 has received a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires the stress information that is paired with the above organization identifier "A" and the stress information after January 1, 2020 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.

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

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

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

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

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

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

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

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

[0517] Next, the correspondence acquisition means 3311 acquires, for each month, a teacher vector having the acquired communication attribute values as elements. Next, the correspondence acquisition means 3311 reads out the teacher score corresponding to each month from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires, for each month, teacher data having a teacher vector and a teacher score. 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. However, for example, only one teacher score per year may be stored in the teacher score storage unit 313. When only one teacher score per year is stored in the teacher score storage unit 313, the teacher scores for each month of a certain year are the same teacher score.

[0518] Next, the correspondence acquisition means 3311 provides the acquired large number of teacher data to a module that performs learning processing of machine learning, and acquires a learning device by executing the module. The learning device is stored in the correspondence relationship information storage unit 314 in a pair with the organization identifier "A".

[0519] Next, it is assumed that the score prediction device 3 has received a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires the communication information corresponding to the above organization identifier "A", that is, the communication information for May 2020, from the communication information storage means 3122.

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

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

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

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

[0524] (Specific Example 6) Specific Example 6 is a case where a prediction score is acquired using voice information of a videophone. Assume that a large number of voice information (voice files) are stored in the voice storage means 3128 in association with the organization identifier "A" and time information. In Specific Example 6, the unit period is one year.

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

[0526] Next, the correspondence acquisition means 3311 performs spectral analysis on each acquired voice information every year to acquire two or more feature amounts of the voice. Further, the correspondence acquisition means 3311 acquires a representative value of the feature amounts for each type of feature amount for each year from the two or more feature amounts of the voice. Next, the correspondence acquisition means 3311 acquires a teacher vector having representative values of various types of feature amounts as elements for each year. Next, the correspondence acquisition means 3311 reads out the teacher score corresponding to each year from the teacher score storage unit 313. Next, the correspondence acquisition means 3311 acquires teacher data having a teacher vector and a teacher score for each year.

[0527] Next, the correspondence acquisition means 3311 provides the acquired plurality of teacher data to a module that performs learning processing of machine learning, and acquires a learning device by executing the module, and stores the learning device in the correspondence relationship information storage unit 314 in a pair with the organization identifier "A".

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

[0529] Next, the correspondence acquisition means 3311 performs spectral analysis on each acquired voice information after January 1, 2020 to acquire two or more feature amounts of the voice. Further, the correspondence acquisition means 3311 acquires a representative value of the feature amounts for each type of feature amount from the two or more feature amounts of the voice. Next, the correspondence acquisition means 3311 acquires a prediction vector having representative values of various types of feature amounts as elements.

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

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

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

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

[0534] In such a situation, the correspondence acquisition means 3311 of the score prediction device 3 acquires, for each year, the organization identifier "A" of the target organization and the paired image. Next, the correspondence acquisition means 3311 performs facial expression analysis processing on each of the images to obtain a facial expression identifier (for example, any one of joy, anger, sorrow, and happiness, normal, etc.). Next, the correspondence acquisition means 3311 acquires, for each year, the ratio of negative facial expressions ("anger", "sorrow").

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

[0536] Next, the correspondence acquisition means 3311 provides the acquired plurality of teacher data to a module that performs machine learning learning processing, and obtains a learner by executing the module, and accumulates the learner in the correspondence relationship information storage unit 314 in a pair with the organization identifier "A".

[0537] Next, assume that the score prediction device 3 receives a prediction instruction from the terminal device 4. Next, the score acquisition means 3312 of the score prediction device 3 acquires, from the voice storage means 3128, an image paired with the above organization identifier "A" and an image after January 1, 2020.

[0538] Next, the correspondence acquisition means 3311 performs an expression analysis process on each of the acquired images after January 1, 2020, and acquires an expression identifier. Next, the correspondence acquisition means 3311 acquires the ratio of negative expressions ("anger" and "sorrow") after January 1, 2020.

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

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

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

[0542] (Specific Example 8) Specific Example 8 is a case where a prediction score is acquired using the daily information of other organizations that meet the conditions of the organization identifier of the corresponding organization. In Specific Example 8, the unit period is 1 year.

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

[0544] 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 enterprise" from the daily information storage unit 312 for each organization identifier and each year.

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

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

[0547] Next, the correspondence acquisition means 3311 provides the acquired plurality of teacher data to a module that performs a learning process of machine learning, and acquires a learning device by executing the module. The learning device is stored in the correspondence relationship information storage unit 314 in pairs with organizational conditions (here, the industry identifier "trading company" and the scale identifier "large enterprise").

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

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

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

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

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

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

[0554] Further, according to this embodiment, the score of an organization can be predicted using one or two or more types of information among business information, input information, communication information, work attendance status information, business result information, health information, biological information, stress information, measurement information, voice, image, life information, and the like.

[0555] Also, according to this embodiment, the score of an organization can be predicted without conducting a questionnaire by using the score based on past questionnaires and daily information.

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

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

[0558] Furthermore, the processing in this embodiment may be realized by software. And this software may be distributed by software download or the like. Also, this software may be recorded on a recording medium such as a CD-ROM and distributed. Note that this also applies to other embodiments in this specification. The software that realizes the score prediction device 3 in this embodiment is the following program. That is, this program causes a computer to use a teacher score that is the score of an organization obtained using a plurality of organization response information indicating answers to questions for two or more members of the organization, and daily information during a period corresponding to the questions or the answers, which is one or more teacher daily information that can be obtained in the daily life of the two or more members, and one or more prediction daily information that is the daily information of the two or more members of the organization during the period for which the score of the organization is to be predicted, and to obtain a prediction score that is the score corresponding to the one or more prediction daily information, and is a program for functioning as a score acquisition unit and a score output unit that outputs the prediction score.

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

[0560] 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.

[0561] 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 programs such as a boot-up program, and an RAM 3016 connected to the MPU 3013 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. Here, although not shown, the computer 301 may further include a network card that provides a connection to a LAN.

[0562] A program for causing the computer system 300 to execute functions such as the score prediction device 3 of the above-described embodiments may be stored in a 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 the RAM 3016 during execution. The program may be loaded directly from the CD-ROM 3101 or the network.

[0563] The program does not necessarily include an operating system (OS) that causes the computer 301 to execute functions such as the score prediction device 3 in the above-described embodiment, or a third-party program, etc. The program only needs to include only the part of the instructions that calls appropriate functions (modules) in a controlled manner so as to obtain a desired result. How the computer system 300 operates is well-known, and a detailed description thereof will be omitted.

[0564] In the above program, in steps such as transmitting information and receiving information, processes performed by hardware, for example, processes performed by a modem or an interface card in the transmission step (processes that can only be performed by hardware) are not included.

[0565] Also, the computer that executes the above program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

[0566] Also, in each of the above embodiments, it goes without saying that two or more communication means existing in one device may be physically realized by one medium.

[0567] Also, in each of the above embodiments, each process may be realized by being centrally processed by a single device, or may be realized by being distributedly processed by a plurality of devices.

[0568] Needless to say, the present invention is not limited to the above embodiments, and various modifications are possible, and those are also included within the scope of the present invention.

Industrial Applicability

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

Explanation of Signs

[0570] 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 relationship information storage unit 331 Score acquisition unit 341 Score output unit 3121 Business input information storage means 3122 Communication information storage means 3123 Attendance status information storage means 3124 Business result information storage means 3125 Stress information storage means 3126 Measurement information storage means 3127 Image storage means 3128 Audio storage means 3129 Life information storage means 3311 Correspondence acquisition means 3312 Score acquisition means

Claims

1. a score acquisition unit that acquires a predicted score that corresponds to the one or more predicted daily information by using a teacher score, which is a score of an organization acquired using a plurality of organization answer information indicating answers to questions for each of two or more members of the organization, and a correspondence relationship between the teacher daily information, which is daily information for a period corresponding to the questions or the answers and can be acquired in the daily lives of each of the two or more members, and one or more predicted daily information, which is daily information for each of the two or more members of the organization, which is a period ahead of the period and is a period for which the score of the organization is to be predicted; A score prediction device comprising: a score output unit that outputs the predicted score.

2. Each of the one or more pieces of teacher daily information includes teacher work information related to the work performed by the member; Each of the one or more pieces of predicted daily information includes predicted task information regarding the tasks performed by the members; The score acquisition unit, The score prediction device according to claim 1 , wherein the predicted score is obtained using the correspondence between one or more pieces of teacher daily routine information including the teacher work information and the teacher score, and one or more pieces of predicted daily routine information including the predicted work information.

3. The teacher work information includes teacher input information input by the member, The predicted task information includes predicted input information to be input by the member, The score acquisition unit, The score prediction device according to claim 2 , wherein the predicted score is obtained using the correspondence between one or more pieces of teacher routine information including the teacher input information and the teacher score, and one or more pieces of predicted routine information including the predicted input information.

4. The teacher input information includes teacher work input information related to input for the member to perform his / her own work, The predicted input information includes predicted task input information regarding input for the execution of the member's own task, The score acquisition unit, The score prediction device of claim 3 , wherein the predicted score is obtained using the correspondence between one or more pieces of teacher routine information including the teacher work input information and the teacher score, and one or more pieces of predicted routine information including the predicted work input information.

5. 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, The score prediction device according to claim 3 , wherein the predicted score is obtained using the correspondence between one or more pieces of teacher daily routine information including the teacher communication information and the teacher score, and one or more pieces of predicted daily routine information including the predicted communication information.

6. The score acquisition unit, 4. The score prediction device of claim 3, further comprising: one or more types of information among data amount information that specifies the data amount of the teacher input information, input frequency information that specifies the input frequency of the teacher input information, analysis information that indicates an analysis result of the teacher input information, and timing information that specifies the timing of the input of the teacher input information; and one or more types of information among data amount information of the predicted input information, input frequency information of the predicted input information, analysis information of the predicted input information, and timing information of the predicted input information; one or more types of information obtained from the teacher input information; correspondence relationship information that specifies a correspondence relationship between the teacher score and one or more teacher features including one or more types of information obtained from the teacher input information; and one or more prediction features including one or more types of information obtained from the predicted input information, to obtain the predicted score.

7. The teacher work information includes teacher attendance status information regarding the attendance of the members, The predicted work information includes predicted attendance status information regarding the attendance of the members, The score acquisition unit, The score prediction device of claim 2, wherein the predicted score is obtained using 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.

8. The teacher work information includes teacher work result information regarding the results of the work performed by the member, The predicted work information includes predicted work result information regarding the results of the work performed by the members; The score acquisition unit, The score prediction device of claim 2 , wherein the predicted score is obtained using the correspondence 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.

9. Each of the one or more teacher daily information includes teacher health information related to the health of the member; each of the one or more predictive daily information includes predictive health information regarding the health of the member; The score acquisition unit, The score prediction device of claim 1 , wherein the predicted score is obtained using the correspondence 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.

10. The teacher health information includes teacher biometric information regarding the member's biometric information; the predictive health information includes predictive vital signs regarding the member's physiology; The score acquisition unit, The score prediction device according to claim 9 , wherein the predicted score is obtained using the correspondence between one or more pieces of teacher routine information including the teacher biometric information and the teacher score, and one or more pieces of predicted routine information including the predicted biometric information.

11. The teacher biological information includes teacher stress information related to the stress of the member, The predicted biological information includes predicted stress information regarding the stress of the member, The score acquisition unit, The score prediction device according to claim 10 , wherein the predicted score is obtained using the correspondence between one or more pieces of teacher daily routine information including the teacher stress information and the teacher score, and one or more pieces of predicted daily routine information including the predicted stress information.

12. The teacher biometric information includes teacher measurement information which is a measurement result of the member's biometric information, The predicted biological information includes predicted measurement information obtained from the biological body of the member, The score acquisition unit, The score prediction device according to claim 10 or 11, wherein the predicted score is obtained using the correspondence between one or more pieces of teacher routine information including the teacher measurement information and the teacher score, and one or more pieces of predicted routine information including the predicted measurement information.

13. The teacher biometric information includes teacher biometric feature information which is an image of the member, or a feature of the image of the member, or a voice uttered by the member, or a feature of the voice uttered by the member, The predicted biometric information includes predicted biometric feature information that is an image of the member, or a feature of the image of the member, or a voice uttered by the member, or a feature of the voice uttered by the member, The score acquisition unit, The score prediction device according to any one of claims 10 to 12, wherein the predicted score is obtained using the correspondence between one or more pieces of teacher routine information including the teacher biometric characteristic information and the teacher score, and one or more pieces of predicted routine information including the predicted biometric characteristic information.

14. The teacher health information includes teacher life information relating to the life of the member; the predictive health information includes predictive lifestyle information regarding the member's lifestyle; The score acquisition unit, The score prediction device according to any one of claims 9 to 13, wherein the predicted score is obtained using the correspondence between one or more pieces of teacher daily life information including the teacher daily life information and the teacher score, and one or more pieces of predicted daily life information including the predicted daily life information.

15. The score acquisition unit, A correspondence acquisition means for acquiring correspondence information that specifies a correspondence between the one or more pieces of teacher daily information and the teacher score; The score prediction device according to claim 1 , further comprising: a score acquisition means for acquiring the predicted score by using the correspondence information and the one or more pieces of predicted daily life information.

16. The one or more pieces of teacher daily information and the teacher score correspond to one or more organizational attribute values; The correspondence acquisition means includes: Obtaining correspondence information that specifies a correspondence between the one or more pieces of teacher daily information and the teacher score for each organizational condition configured using the one or more organizational attribute values; The score acquisition means includes: The score prediction device according to claim 15 , wherein the predicted score is obtained using the correspondence information corresponding to an organizational condition that the organization to be predicted meets and the one or more pieces of predicted daily information.

17. The correspondence acquisition means includes: Using the one or more pieces of teacher routine information and the teacher score, a learning process is performed by a machine learning algorithm, and correspondence information is obtained which is a learning model having the one or more pieces of teacher routine information as explanatory variables and the teacher score as a target variable; The score acquisition means includes: The score prediction device according to claim 15 or 16, further comprising: a machine learning algorithm that uses the learning model and the one or more pieces of predicted everyday information to perform a prediction process and obtain the predicted score.

18. The correspondence acquisition means includes: Using the one or more pieces of teacher routine information, a teacher vector is obtained, which is a vector having two or more elements, and correspondence relationship information is obtained, which is a correspondence table having one or more pieces of correspondence information that are pairs of the teacher vector and the teacher score; The score acquisition means includes: The score prediction device according to claim 15 or 16, further comprising: a prediction vector, which is a vector having two or more elements, obtained by using the one or more pieces of predicted everyday information; applying the prediction vector to the correspondence table; and obtaining the predicted score.

19. The correspondence acquisition means includes: Using the one or more pieces of teacher routine information, a teacher vector is obtained, which is a vector having two or more elements, and correspondence information is obtained, which is an arithmetic formula in which each element of the teacher vector is input and the teacher score is output; The score acquisition means includes: A score prediction device as described in claim 15 or 16, which uses the one or more pieces of predicted everyday information to obtain a prediction vector, which is a vector having two or more elements, provides each element of the prediction vector to the arithmetic expression, executes the arithmetic expression, and obtains the predicted score.

20. A score prediction method comprising all the processes performed by the score prediction device according to any one of claims 1 to 19.

21. Computer, A program for causing the score prediction device according to any one of claims 1 to 19 to function as such a device.

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